Teams can now manage Terraform, OpenTofu, and Terragrunt in a single platform without fragmented tooling.
Built-in governance, policy enforcement, and approvals streamline secure infrastructure operations.
End-to-end visibility and drift detection improve reliability across complex, multi-environment deployments.
The launch marks a major step toward a unified, multi-IaC control plane for modern infrastructure teams.
Bringing First-Class Terragrunt Support to IaCM
“We’ve been operating in a hybrid environment with both OpenTofu and Terragrunt, and Harness has made it much easier to bring those workflows together into a single, consistent platform with IaCM. The addition of Terragrunt support is a valuable step toward simplifying how we manage infrastructure at scale.”
— Lead Platform Engineer, Enterprise Customer
Infrastructure as Code is now a standard for modern cloud operations, with most enterprises using IaC to provision and manage environments. However, as adoption grows, so does complexity. Teams are no longer managing a handful of environments. They are operating across multiple regions, accounts, and services, often at massive scale.
This is where traditional approaches begin to fall short.
As organizations scale their infrastructure, Terraform alone is often not enough. Teams adopt Terragrunt to manage complex, multi-environment deployments, but they are often forced to stitch together fragmented tooling that lacks visibility, governance, and consistency.
At Harness, we are changing that.
Today, we are excited to announce native Terragrunt support in Harness IaCM, bringing it to full parity with Terraform and OpenTofu while delivering capabilities that go beyond what is available in standalone tooling. This is more than support. It is about making Terragrunt a first-class platform for enterprise infrastructure management.
Orchestrate complex Terragrunt environments with full visibility across all units
Apply cost estimation, approvals, and policy enforcement natively
Detect and manage drift across environments with granular insights
View infrastructure changes at the resource level across orchestrated deployments
Terragrunt has become a critical layer for managing infrastructure at scale because it simplifies how teams structure and reuse configurations across environments. Harness builds on that foundation with deep, native integration, enabling platform teams to operate with both flexibility and control.
This is especially important for enterprises where a single deployment spans multiple environments and services. Harness abstracts that complexity while maintaining governance, auditability, and consistency.
Extending IaCM to a Multi-IaC Future
Terragrunt is part of a broader shift toward multi-tool infrastructure strategies.
Modern teams are no longer standardized on a single IaC tool. Instead, they operate across:
Terraform and OpenTofu for provisioning
Terragrunt for orchestration
CDK for developer-driven infrastructure
Ansible for configuration and automation
This creates challenges around consistency, visibility, and governance. Harness IaCM is built for this reality. We are evolving IaCM into a unified control plane for multi-IaC workflows, where teams can manage different frameworks with a consistent experience, shared policies, and centralized visibility.
This means:
Eliminating fragmented pipelines across tools
Standardizing governance across environments
Gaining full visibility into infrastructure state and changes
Instead of managing infrastructure in silos, teams can now operate from a single platform across the entire lifecycle.
We are continuing to support modern frameworks like AWS CDK, enabling developer-centric infrastructure workflows alongside provisioning, configuration, and orchestration tools.
AI-Driven Automation
We are introducing intelligence into IaC workflows to simplify tasks such as drift management and optimization. This helps teams reduce manual effort and operate more efficiently at scale.
Together, these investments move IaCM toward a unified, multi-IaC platform that combines flexibility, governance, and automation. Terragrunt has become essential for managing infrastructure at scale but until now, it hasn’t had a platform that truly supports it. As infrastructure continues to grow in complexity, our focus remains the same. Helping teams move faster, reduce risk, and scale with confidence no matter which IaC tools they use.
We’ve come a long way in how we build and deliver software. Continuous Integration (CI) is automated, Continuous Delivery (CD) is fast, and teams can ship code quickly and often. But environments are still messy.
Shared staging systems break when too many teams deploy at once, while developers wait on infrastructure changes. Test environments get created and forgotten, but over time, what is running in the cloud stops matching what was written in code.
We have made deployments smooth and reliable, but managing environments still feels manual and unpredictable. That gap has quietly become one of the biggest slowdowns in modern software delivery.
This is the hidden bottleneck in platform engineering, and it's a challenge enterprise teams are actively working to solve.
As Steve Day, Enterprise Technology Executive at National Australia Bank, shared:
“As we’ve scaled our engineering focus, removing friction has been critical to delivering better outcomes for our customers and colleagues. Partnering with Harness has helped us give teams self-service access to environments directly within their workflow, so they can move faster and innovate safely, while still meeting the security and governance expectations of a regulated bank.”
Harness IDP Environment Management List of Available Environments
This is not another self-service workflow. It is environment lifecycle management built directly into the delivery platform.
The result is faster delivery, stronger governance, and lower operational overhead without forcing teams to choose between speed and control.
Closing the Gap Between CD and IaC
Continuous Delivery answers how code gets deployed. Infrastructure as Code defines what infrastructure should look like. But the lifecycle of environments has often lived between the two.
A look at the Harness IDP Environment Management User Journey
Teams stitch together Terraform projects, custom scripts, ticket queues, and informal processes just to create and update environments. Day two operations such as resizing infrastructure, adding services, or modifying dependencies require manual coordination. Ephemeral environments multiply without cleanup. Drift accumulates unnoticed.
The outcome is familiar: slower innovation, rising cloud spend, and increased operational risk.
Environment Management closes this gap by making environments real entities within the Harness platform. Provisioning, deployment, governance, and visibility now operate within a single control plane.
Harness is the only platform that unifies environment lifecycle management, infrastructure provisioning, and application delivery under one governed system.
Platform teams define reusable, standardized templates that describe exactly what an environment contains. A blueprint includes infrastructure resources, application services, dependencies, and configurable inputs such as versions or replica counts. Role-based access control and versioning are embedded directly into the definition.
Harness IDP Environment Management Blueprint
Developers consume these blueprints from the Internal Developer Portal and create production-like environments in minutes. No tickets. No manual stitching between infrastructure and pipelines. No bypassing governance to move faster.
Consistency becomes the default. Governance is built in from the start.
Full Lifecycle Control
Environment Management handles more than initial provisioning.
Infrastructure is provisioned through Harness IaCM. Services are deployed through Harness CD. Updates, modifications, and teardown actions are versioned, auditable, and governed within the same system.
Teams can define time-to-live policies for ephemeral environments so they are automatically destroyed when no longer needed. This reduces environment sprawl and controls cloud costs without slowing experimentation.
Harness EM also introduces drift detection. As environments evolve, unintended changes can occur outside declared infrastructure definitions. Drift detection provides visibility into differences between the blueprint and the running environment, allowing teams to detect issues early and respond appropriately. In regulated industries, this visibility is essential for auditability and compliance.
For enterprises operating at scale, self-service without control is not viable.
Environment Management leverages Harness’s existing project and organization hierarchy, role-based access control, and policy framework. Platform teams can control who creates environments, which blueprints are available to which teams, and what approvals are required for changes. Every lifecycle action is captured in an audit trail.
This balance between autonomy and oversight is critical. Environment Management delivers that balance. Developers gain speed and independence, while enterprises maintain the governance they require.
"Our goal is to make environment creation a simple, single action for developers so they don't have to worry about underlying parameters or pipelines. By moving away from spinning up individual services and using standardized blueprints to orchestrate complete, production-like environments, we remove significant manual effort while ensuring teams only have control over the environments they own."
— Dinesh Lakkaraju, Senior Principal Software Engineer, Boomi
From Portal to Platform
Environment Management represents a shift in how internal developer platforms are built.
Instead of focusing solely on discoverability or one-off self-service actions, it brings lifecycle control, cost governance, and compliance directly into the developer workflow.
Developers can create environments confidently. Platform engineers can encode standards once and reuse them everywhere. Engineering leaders gain visibility into cost, drift, and deployment velocity across the organization.
Environment sprawl and ticket-driven provisioning do not have to be the norm. With Environment Management, environments become governed systems, not manual processes. And with CD, IaCM, and IDP working together, Harness is turning environment control into a core platform capability instead of an afterthought.
This is what real environment management should look like.
Over the last few years, something fundamental has changed in software development.
If the early 2020s were about adopting AI coding assistants, the next phase is about what happens after those tools accelerate development. Teams are producing code faster than ever. But what I’m hearing from engineering leaders is a different question:
What’s going to break next?
That question is exactly what led us to commission our latest research, State of DevOps Modernization 2026. The results reveal a pattern that many practitioners already sense intuitively: faster code generation is exposing weaknesses across the rest of the software delivery lifecycle.
In other words, AI is multiplying development velocity, but it’s also revealing the limits of the systems we built to ship that code safely.
The Emerging “Velocity Paradox”
One of the most striking findings in the research is something we’ve started calling the AI Velocity Paradox - a term we coined in our2025 State of Software Engineering Report.
Teams using AI coding tools most heavily are shipping code significantly faster. In fact, 45% of developers who use AI coding tools multiple times per day deploy to production daily or faster, compared to 32% of daily users and just 15% of weekly users.
At first glance, that sounds like a huge success story. Faster iteration cycles are exactly what modern software teams want.
But the data tells a more complicated story.
Among those same heavy AI users:
69% report frequent deployment problems when AI-generated code is involved
Incident recovery times average 7.6 hours, longer than for teams using AI less frequently
47% say manual downstream work, QA, validation, remediation has become more problematic
What this tells me is simple: AI is speeding up the front of the delivery pipeline, but the rest of the system isn’t scaling with it. It’s like we are running trains faster than the tracks they are built for. Friction builds, the ride is bumpy, and it seems we could be on the edge of disaster.
The result is friction downstream, more incidents, more manual work, and more operational stress on engineering teams.
Why the Delivery System Is Straining
To understand why this is happening, you have to step back and look at how most DevOps systems actually evolved.
Over the past 15 years, delivery pipelines have grown incrementally. Teams added tools to solve specific problems: CI servers, artifact repositories, security scanners, deployment automation, and feature management. Each step made sense at the time.
But the overall system was rarely designed as a coherent whole.
In many organizations today, quality gates, verification steps, and incident recovery still rely heavily on human coordination and manual work. In fact, 77% say teams often have to wait on other teams for routine delivery tasks.
That model worked when release cycles were slower.
It doesn’t work as well when AI dramatically increases the number of code changes moving through the system.
Think of it this way: If AI doubles the number of changes engineers can produce, your pipelines must either:
cut the risk of each change in half, or
detect and resolve failures much faster.
Otherwise, the system begins to crack under pressure. The burden often falls directly on developers to help deploy services safely, certify compliance checks, and keep rollouts continuously progressing. When failures happen, they have to jump in and remediate at whatever hour.
These manual tasks, naturally, inhibit innovation and cause developer burnout. That’s exactly what the research shows.
Across respondents, developers report spending roughly 36% of their time on repetitive manual tasks like chasing approvals, rerunning failed jobs, or copy-pasting configuration.
As delivery speed increases, the operational load increases. That burden often falls directly on developers.
What Organizations Should Do Next
The good news is that this problem isn’t mysterious. It’s a systems problem. And systems problems can be solved.
From our experience working with engineering organizations, we've identified a few principles that consistently help teams scale AI-driven development safely.
1. Standardize delivery foundations
When every team builds pipelines differently, scaling delivery becomes difficult.
Standardized templates (or “golden paths”) make it easier to deploy services safely and consistently. They also dramatically reduce the cognitive load for developers.
2. Automate quality and security checks earlier
Speed only works when feedback is fast.
Automating security, compliance, and quality checks earlier in the lifecycle ensures problems are caught before they reach production. That keeps pipelines moving without sacrificing safety.
3. Build guardrails into the release process
Feature flags, automated rollbacks, and progressive rollouts allow teams to decouple deployment from release. That flexibility reduces the blast radius of new changes and makes experimentation safer.
It also allows teams to move faster without increasing production risk.
4. Remember measurement, not just automation
Automation alone doesn’t solve the problem. What matters is creating a feedback loop: deploy → observe → measure → iterate.
When teams can measure the real-world impact of changes, they can learn faster and improve continuously.
The Next Phase of AI in Software Delivery
AI is already changing how software gets written. The next challenge is changing how software gets delivered.
Coding assistants have increased development teams' capacity to innovate. But to capture the full benefit, the delivery systems behind them must evolve as well.
The organizations that succeed in this new environment will be the ones that treat software delivery as a coherent system, not just a collection of tools.
Because the real goal isn’t just writing code faster. It’s learning faster, delivering safer, and turning engineering velocity into better outcomes for the business.
And that requires modernizing the entire pipeline, not just the part where code is written.
Automate BigQuery schema deployments with Harness using secure OIDC authentication and CI/CD pipelines.
Animesh Pathak
Stephen Atwell
May 29, 2026
Time to read
Modern data platforms are evolving rapidly, and Google Cloud BigQuery has become a core part of analytics, AI, and large-scale reporting architectures. Teams (including Harness) rely on BigQuery to process and analyze massive datasets, but managing schema changes in a secure, repeatable way can still be challenging.
Today, we’re excited to announce BigQuery support for Harness Database DevOps, enabling teams to bring the same automation, governance, and reliability they expect from application DevOps to their BigQuery deployments.
With this release, organizations can now manage BigQuery schema changes using pipeline-driven Database DevOps workflows directly within Harness, while also leveraging secure OIDC-based authentication for keyless access.
The Challenge: Managing BigQuery Changes at Scale
BigQuery helps organizations move fast with data, but database change management often remains manual and fragmented.
Common challenges include:
Manual schema deployments that slow down releases
Limited visibility into schema changes across environments
Inconsistent promotion workflows between development, staging, and production
Managing long-lived service account keys
Difficulty enforcing governance and approvals
Without a standardized deployment process, teams struggle to balance speed, reliability, and security.
Bringing Database DevOps to BigQuery
Harness Database DevOps now supports BigQuery as a first-class database platform, allowing teams to manage schema changes through automated, pipeline-driven workflows.
This means BigQuery schema changes can now be treated just like application code versioned, tested, approved, and promoted through environments using Harness pipelines.
Harness securely authenticates to BigQuery at runtime
This improves:
Security posture
Compliance readiness
Credential management
Operational reliability
No static JSON keys are stored in Harness or delegate environments.
Automated Database Change Pipelines
Use Harness pipelines to automate BigQuery schema deployments with repeatable workflows across environments.
Teams can:
Trigger deployments from Git changes
Standardize promotion workflows
Validate changes before production releases
Automate schema delivery using CI/CD
Governance and Control
Leverage Harness approval gates, RBAC, and policy enforcement to ensure safe production changes. This helps organizations introduce governance into analytics database deployments without slowing down delivery velocity.
Deployment Visibility and Auditability
Track every BigQuery deployment with:
Pipeline execution history
Deployment logs
Approval records
Change visibility across environments
This creates a more transparent and auditable deployment process for data teams.
Why This Matters
As organizations increasingly rely on BigQuery to power analytics and AI workloads, database changes require the same level of automation and governance as application deployments.
From Conversations to Community: Our First MongoDB DBDevOps Meetup in India
Harness and Namma MUG hosted India’s first MongoDB Database DevOps meetup, exploring CI/CD, automation, migrations, and MongoDB-native workflows.
Animesh Pathak
May 22, 2026
Time to read
On May 16th, 2026, Inspired by the growing MongoDB and DevOps community in Bengaluru, we partnered with the Namma MUG community to bring together engineers exploring automation, CI/CD, Infrastructure as Code, and database migration strategies for modern applications.We had been looking forward to for a long time at Harness, our first Database DevOps community event in India focused on MongoDB and modern database automation practices.
The event was a deep dive for experts into how database automation can work with MongoDB easily, without needing manual steps.
My session on OSS Native Mongo Executor initiative was attended by several engineers already using tools like Liquibase, Flyway, and ORM driven migration workflows. That led to incredibly valuable conversations around what Database DevOps should look like for MongoDB-native environments.
Interestingly, many attendees wanted to understand:
How Harness DBDevOps works internally
How pipelines orchestrate MongoDB deployments
How changelog-driven workflows compare against traditional scripting
Whether Liquibase-style workflows can fit naturally into MongoDB ecosystems
How rollback and migration tracking works in NoSQL environments
We also had several deep discussions around CI/CD production rollout strategies and the differences between native Mongo execution and traditional relational migration engines.
These discussions were incredibly insightful because they showed that teams are no longer thinking only about “Database Scripts” - they are thinking about full database delivery workflows integrated into DevOps platforms.
What the Community Told Us
One clear thing we heard throughout all our discussions was how much people want easier ways to get started and more hands-on examples for working with MongoDB DevOps. People kept asking us for simple guides for beginners, real examples of how to set up Continuous Integration and Continuous Delivery (CI/CD), starting templates, and clear steps for moving and rolling back databases from start to finish. We also got into some deep technical talks about handling complex queries, moving databases while they are live, and making sure our deployments are reliable, especially when we talk about advanced ways to undo changes.
A lot of the attendees were really curious about how our MongoDB-native ways of doing migrations are different from the older, traditional database methods. That led us into bigger discussions about why using native MongoDB tools is important, how we manage schema changes in NoSQL, and the unique problems we face with document databases as we move from simple open-source tools to big enterprise-level Database DevOps systems. Overall, the reaction to our new OSS Native Mongo Executor was fantastic! It was clear that people really liked our approach of building Database DevOps features that fit naturally with MongoDB, instead of trying to force old relational rules onto a NoSQL system.
The future of Database DevOps is expanding beyond relational systems, and it’s exciting to see the MongoDB community helping shape that journey with us. A huge thank you to everyone who joined us, especially the speakers and community members who made the event successful: Naveen Kumar, Narendra Gottipati.Pritesh Kiri, Aripriya Basu
For us at Harness, this meetup made us realise something important: The community is actively looking for better ways to automate MongoDB operations while maintaining reliability, governance, and developer velocity. We have a lot more events coming up which you can join - Harness · Events Calendar
The NoSQL Storm is the second edition of the Database DevOps comic series, inspired by the fast-paced world of MongoDB and modern distributed applications. Follow the journey through scaling challenges, schema evolution, operational chaos, and the ne
Animesh Pathak
May 21, 2026
Time to read
In the second edition of our Database DevOps comic series, The NoSQL Storm, we dive into the fast-moving universe of MongoDB, distributed data, schema flexibility, and the operational challenges teams face when speed outpaces process. From unexpected production surprises to scaling modern applications without losing control, this issue explores how Database DevOps practices bring stability, automation, and confidence to NoSQL workflows. Whether you're a developer, DBA, platform engineer, or MongoDB enthusiast, this comic brings real-world challenges to life in a fun and visual way.
Learn how to reduce CI costs with test optimization, caching, and right-sized infrastructure. Cut build time and cloud spend by up to 75%.
Chinmay Gaikwad
May 20, 2026
Time to read
Continuous integration (CI) costs can escalate quickly as engineering teams scale. While most organizations focus on cloud bills, the true cost of CI includes slow build times, developer wait time, inefficient test execution, and overprovisioned infrastructure.
CI cost optimization is the practice of reducing the total cost of CI pipelines by improving build efficiency, minimizing compute usage, and eliminating unnecessary work without slowing down development.
In this guide, you will learn how to reduce CI costs using four proven strategies: test optimization, intelligent caching, infrastructure right-sizing, and governance controls. Teams that implement these approaches often reduce build times and costs by 50 to 75 percent, while improving developer productivity and feedback cycles.
What Are CI Costs?
CI costs extend far beyond your cloud invoice. They include both direct infrastructure expenses and indirect productivity losses.
Direct costs:
Compute resources such as build runners, containers, and virtual machines
Storage for artifacts, caches, and logs
Networking and data transfer
Indirect costs:
Developer wait time during slow builds
Context switching due to pipeline failures
Time spent debugging flaky tests
Engineering effort maintaining CI infrastructure
Why this matters
Research on developer productivity shows that interruptions can take 15 to 25 minutes to recover focus. When builds are slow or unreliable, this hidden cost compounds across teams and often exceeds infrastructure spend.
What Drives CI Costs?
CI costs are primarily driven by four factors:
Build duration: which increases compute usage
Test execution volume: which expands the runtime
Infrastructure inefficiency: which resources waste the budget
Pipeline design: which can create redundant work
Understanding these drivers is the first step toward meaningful cost reduction.
Strategy 1: Optimize Your Testing
Testing is typically the largest contributor to CI runtime and cost. Optimizing test execution delivers the highest return on investment.
Selective Test Execution
Most teams run their full test suite on every commit. This is inefficient, especially in large repositories.
Selective test execution runs only the tests affected by a code change.
Benefits:
Reduces test volume by 50 to 80 percent
Shortens feedback loops
Lowers compute usage
For example, large engineering teams using test selection techniques have reduced build times from more than 20 minutes to under five minutes, saving significant developer time.
Flaky Test Management
Flaky tests are tests that fail intermittently without code changes. They introduce hidden costs:
Trigger unnecessary reruns
Reduce trust in CI results
Waste developer time
Industry studies suggest flaky tests consume a measurable portion of engineering productivity.
Best practices:
Automatically detect flaky tests
Quarantine them so they do not block pipelines
Track flaky test rate and aim for less than 2 percent
Prioritize fixes based on impact
Test Parallelization
Running tests sequentially is inefficient.
Parallelization distributes tests across multiple runners, reducing execution time.
Why Artifact Repository Sprawl Slows Down Software Delivery
Artifact repository sprawl across multiple registries creates CI/CD bottlenecks, security blind spots, and compliance gaps. Learn how registry consolidation with unified governance fixes it.
Shibam Dhar
May 20, 2026
Time to read
Three weeks into a platform modernization project, this question landed in my inbox: "Why does our deployment pipeline take 40 minutes instead of four?"
This is artifact repository sprawl in practice, and it does more than slow pipelines. It fragments your security posture, your compliance evidence, and your ability to answer basic questions like "what's actually running in production right now?"
How Artifact Repository Sprawl Creates CI/CD Bottlenecks
Modern software delivery pipelines consume and produce artifacts at every stage. A typical microservices application might pull base container images, install language-specific packages, bundle compiled binaries, and push versioned containers, all before a single integration test runs. When each artifact type lives in a separate registry, every pipeline stage authenticates separately, fetches metadata independently, and logs access in disconnected audit systems.
The operational cost compounds quickly. Build jobs that should complete in minutes stall while waiting for credential rotation across four registry providers. Terraform modules reference hardcoded repository URLs that break when teams migrate between vendors. Developers waste hours debugging "works on my machine" issues that trace back to different registries serving different cached versions in CI versus local environments.
Container registry management alone doesn't solve this. You can centralise Docker images perfectly and still have sprawl across Maven Central proxies, PyPI mirrors, and npm registries that each handle authentication, scanning, and access policies differently. The sprawl persists even when every tool works correctly in isolation.
What this actually looks like in a pipeline:
# A typical fragmented pipeline - four different auth mechanisms, four different APIs
stages:
- name: Pull Base Image
spec:
connectorRef: docker_hub_connector # Registry 1: Docker Hub
image: node:20-alpine
- name: Install Dependencies
spec:
command: npm install # Registry 2: npm registry (or private Verdaccio)
- name: Build Java Service
spec:
command: mvn package # Registry 3: Maven Central / Artifactory
- name: Push Container
spec:
connectorRef: ecr_connector # Registry 4: Amazon ECR
repo: my-app
tags: <+pipeline.sequenceId>
Four registries, four sets of credentials to rotate, four places to check when something breaks. Now multiply that by every microservice in your org.
How Registry Consolidation Reduces Security Blind Spots
Software supply chain governance requires knowing what entered your build process, who approved it, and whether it matches what shipped to production. Artifact repository sprawl makes that visibility nearly impossible without building custom integration layers that inevitably lag behind the registries they monitor.
Consider a realistic scenario: your security team needs to answer whether a new CVE affects any production workload. With fragmented registries, you're querying Docker Hub for container manifests, Artifactory for Java dependencies, a separate S3 bucket for ML models, and hoping the correlation logic catches every transitive dependency. Miss one registry in the sweep and you've got an incomplete answer. Get the timing wrong and you're correlating artifacts from different build windows.
Unified artifact management changes the equation. When containers, packages, and models flow through a single governance boundary, you can enforce consistent policies at ingestion time rather than auditing violations after deployment. Access control becomes auditable in one place instead of five.
This matters for supply chain attacks targeting package managers, which increasingly exploit the trust developers place in upstream dependencies. When every language ecosystem has its own registry with different security scanning capabilities and policy enforcement mechanisms, attackers optimize for the weakest link. A malicious npm package that wouldn't pass container scanning slips through because the npm registry didn't apply the same controls.
How a unified registry changes incident response:
# Fragmented approach: check each registry separately
1. Query Docker Hub for affected container manifests (minutes)
2. Query Artifactory for affected Java dependencies (minutes)
3. Query npm registry for affected Node packages (minutes)
4. Cross-reference results manually (hours)
5. Hope you didn't miss a registry (uncertainty)
# Consolidated approach: one query, full picture
1. Search artifact registry for component with CVE ID (seconds)
2. View which artifacts contain the dependency (SBOM) (seconds)
3. Check Deployments tab for production exposure (seconds)
4. Full answer with audit trail (confidence)
The Hidden Cost of Sprawl on Platform Teams
Platform engineering teams building internal developer portals face a choice: abstract away registry complexity or force application teams to manage it themselves. Neither option works well with artifact sprawl. Abstraction requires maintaining integration code for every registry type, each with different APIs for search, versioning, and access control. Forcing teams to manage it themselves guarantees inconsistent practices and duplicate effort across squads.
The operational burden shows up in unexpected places. Onboarding a new service means provisioning credentials across multiple registries. Rotating secrets means updating pipelines in every repository that publishes or consumes artifacts. And when you need to answer "who pulled what and when" for a compliance audit, you're stitching together logs from disconnected systems with different formats and retention windows.
DevOps toolchain efficiency suffers because fragmented registries create artificial boundaries in automation workflows. Teams end up building brittle orchestration logic that breaks whenever registry APIs change or network partitions separate previously co-located systems.
Why Sprawl Compounds in Hybrid and Multicloud Environments
Running workloads across on-premises data centres and multiple cloud providers amplifies every artifact sprawl problem. Each environment tends to accumulate its own preferred registries: Amazon ECR for AWS workloads, Google Artifact Registry for GCP services, a self-hosted Harbor instance in the data centre. What started as practical deployment choices hardens into infrastructure that's expensive to consolidate and risky to migrate.
Software delivery pipeline consistency becomes nearly impossible. A feature branch tested against artifacts from the on-prem registry might behave differently in production pulling from ECR because different proxy cache timing introduced a version skew. Compliance auditors asking for artifact lineage get stitched-together spreadsheets instead of queryable attestations because no single system has the full picture.
Registry consolidation doesn't mean forcing everything into one physical location. It means establishing a logical control plane that can proxy, cache, and govern artifacts regardless of where they're ultimately stored. The governance layer stays consistent even when artifacts need to live close to compute for latency or compliance reasons.
How Harness Artifact Registry Addresses Sprawl
Harness Artifact Registry was designed to centralise artifact storage and enforce governance across engineering teams dealing with exactly these sprawl problems. It supports 16+ package types natively, including Docker, Helm, Maven, npm, PyPI, NuGet, Go, Cargo, Dart, Swift, RPM, Conda, Hugging Face (for ML models), and generic files, so teams don't need a separate registry for each language ecosystem.
Upstream proxy and caching is where consolidation starts in practice. Instead of every developer and CI job pulling directly from Docker Hub, Maven Central, PyPI, or npm, they pull through Harness AR's proxy layer. The proxy caches artifacts locally, so external registry downtime doesn't break your builds, and every fetch is subject to the same governance policies.
# Before: Direct pulls from multiple external registries
developer laptop --> Docker Hub
CI runner --> Maven Central
CI runner --> npm registry
CI runner --> PyPI
# After: Everything routes through Harness AR upstream proxies
developer laptop --> Harness AR (Docker proxy) --> Docker Hub
CI runner --> Harness AR (Maven proxy) --> Maven Central
CI runner --> Harness AR (npm proxy) --> npm registry
CI runner --> Harness AR (Python proxy) --> PyPI
Upstream proxies are available for all 16+ supported package types, so the governance boundary is genuinely universal rather than limited to containers.
The Dependency Firewall gates what enters your registry from upstream sources. Currently, OPA policies apply only to artifacts fetched through upstream proxies. Direct pushes to hosted registries are not yet subject to Dependency Firewall policies; that capability is coming soon.
For now, governance for direct pushes relies on Security Tests policy sets (Docker/Helm only) or post-ingestion scanning via STO/SCS. There are some built-in policy templates that cover the most common scenarios:
CVSS Threshold - Block packages with vulnerability scores above a threshold
License Policy - Block packages with non-compliant licenses (e.g., GPL in a proprietary codebase)
Package Age - Block packages published too recently (a common indicator of typosquatting attacks)
Each evaluation results in one of three statuses: Passed, Warning, or Blocked. Blocked artifacts are never cached in your registry. You can write custom Rego policies beyond the built-in templates.
# Example: Block any npm package published less than 7 days ago
package artifact
deny[msg] {
input.metadata.published_days_ago < 7
msg := sprintf("Package %s was published %d days ago (minimum: 7)",
[input.metadata.name, input.metadata.published_days_ago])
}
Currently, the Dependency Firewall's OPA policies apply to upstream proxy fetches. Support for applying these policies across all registry types, including direct pushes to hosted registries, is coming soon.
Role-based access control provides three pre-built roles (Viewer, Contributor, Admin) that can be assigned to users, user groups, or service accounts at the registry level.
Role
Pull
Push
Delete
Manage Settings
Viewer
Yes
No
No
No
Contributor
Yes
Yes
No
No
Admin
Yes
Yes
Yes
Yes
Security scanning and quarantine work through two layers. First, the Dependency Firewall evaluates upstream artifacts against OPA policies at fetch time, blocking anything that fails before it ever enters your registry. Second, for artifacts already in the registry, Harness integrates with Security Testing Orchestration (STO) and Supply Chain Security (SCS) to scan for vulnerabilities and generate SBOMs. Registries can be configured with Security Tests policy sets that evaluate artifacts during ingestion via a scan pipeline (currently supported for Docker and Helm registries). Artifacts that violate policies are automatically quarantined, preventing them from being pulled or used in any downstream pipeline. This requires enabling the relevant policy configuration on your registry.
Quarantine can also be applied manually through the UI on any artifact (three-dot menu > Quarantine), with a required reason for audit purposes. Quarantined artifacts can be released via "Remove from Quarantine" once the issue is resolved.
The artifact details page surfaces security and deployment data directly:
Vulnerabilities tab - Scan results from STO (requires STO module)
Deployments tab - Which environments this artifact is deployed to and instance counts (requires CD module)
Audit trails are built into the Harness platform. Every artifact action is tracked with the actor, timestamp, and context. You can query these via the UI (Account Settings > Audit Trail, filter by Artifact Registry) or the API.
Teams serious about software supply chain governance end up implementing these controls eventually. Harness AR packages upstream proxy caching, Dependency Firewall, RBAC, security scanning via STO/SCS, and platform-wide audit trails into a single registry that covers the breadth of package types modern engineering teams actually use. The alternative is maintaining a constellation of registry-specific integrations that break whenever vendors deprecate APIs or security requirements tighten.
Fixing artifact repository sprawl doesn't require ripping out every existing registry overnight. It requires establishing a control plane that can answer basic questions reliably: what artifacts exist, where they came from, who has access, and what depends on them. Once you have that visibility, you can start enforcing policies consistently and eliminating redundant tooling incrementally.
The teams that move fastest at scale treat artifact management as infrastructure that enables speed rather than a storage problem that needs solving registry by registry. They consolidate governance boundaries, route external dependencies through proxy layers with policy enforcement, and build confidence that what passed security checks is actually what reached production.
If your deployment pipelines feel slower than they should, or your security team struggles to answer supply chain questions confidently, artifact sprawl is worth examining. The operational debt compounds quietly until it doesn't, usually during an incident when you need answers fast and discover your artifact lineage spans five disconnected systems with inconsistent audit logs.
FAQ
Do I have to migrate all my artifacts to Harness AR at once?
No. Start with upstream proxies (no migration needed), then migrate hosted artifacts incrementally per team/package type.
What if I'm already using JFrog Artifactory?
Harness AR can proxy Artifactory as an upstream source while you migrate, or coexist indefinitely if you need Artifactory-specific features.
Does this lock me into Harness for CI/CD?
No. Harness AR works with any CI/CD tool that can authenticate to a registry. The integrations with Harness CD/STO/SCS are optional add-ons.
Core Java vs Enterprise Java: Jakarta EE, Spring Boot & Modern Trade-offs [2026 Guide]
Java SE, Jakarta EE, and Spring Boot have converged more than most teams realize. A 2026 guide to choosing — and standardizing — your enterprise Java stack.
Dewan Ahmed
May 18, 2026
Time to read
The "Java EE vs Java SE" framing is dated. In 2026, every modern enterprise Java app runs on Java SE 21 or 25 LTS. The real decision is which framework or runtime sits on top — Spring Boot, Quarkus, Helidon, Micronaut, or vanilla Jakarta EE on Open Liberty, Payara, or WildFly.
The javax.* → jakarta.* namespace migration is the upgrade gate most teams are still working through. Jakarta EE 9 (2020) renamed every package. Spring Boot 3 and 4 require the new namespace. Any framework or library jump in 2026 has to reckon with it.
The "heavyweight app server" critique no longer applies to the runtimes anyone is choosing. Quarkus, Helidon, and Open Liberty's lightweight profiles compile to native images, start in tens of milliseconds, and run in under 100 MB — competitive with Go and Node on cold-start and footprint.
Standardizing delivery velocity matters more than framework preference. Mixed Java fleets (Spring Boot + Quarkus + legacy Jakarta EE) are the norm. AI-powered CD, GitOps, and policy-as-code give platform teams a single operational model across all of them, without forcing framework consolidation.
When you're architecting an enterprise Java application, one decision quietly shapes everything downstream: runtime footprint, deployment pipelines, and how your platform team handles incidents at 3 a.m. For two decades, that decision was framed as Java SE vs Java EE. In 2026, that framing has quietly inverted.
Nearly every modern enterprise Java app runs on Java SE 21 or 25 LTS. The real choice now sits one layer up: which framework or runtime sits on top of the JVM. Spring Boot. Quarkus. Helidon. Micronaut. Vanilla Jakarta EE on Open Liberty, Payara, or WildFly. These options have converged on the same underlying APIs. Spring Boot 3 and 4 sit on jakarta.* packages, the same namespace Jakarta EE itself uses. But they differ sharply in startup time, memory footprint, deployment topology, and what your CI/CD pipeline has to do to ship them safely.
This guide is for the platform engineer, architect, or staff engineer who needs to make that call once and live with it across dozens of services. We'll cover what changed, where the stacks still diverge, and how to standardize delivery across a mixed Java fleet without forcing consolidation no team wants.
What is Java SE?
Java SE (Standard Edition) is the foundation of every Java application, from a five-line script to a globally distributed system. It's the language, the runtime, and the core libraries every Java program assumes is there.
But describing Java SE as just "the foundation" undersells what's happened to it in the last three years. Java SE in 2026 is not the Java SE of 2018.
What Java SE provides
At its core, Java SE includes:
The Java language itself, including modern features like records, sealed classes, pattern matching, and switch expressions
The JVM, which gives you platform independence and decades of mature garbage collection, JIT compilation, and observability tooling
Core libraries for collections, concurrency, file I/O, networking, and HTTP
Build and dev tools: javac, jshell, jpackage, and the AOT cache introduced in recent LTS releases
These pieces form the runtime baseline that every Java framework, including Spring Boot, Quarkus, and Jakarta EE implementations, sits on top of.
What's new in Java SE that actually matters
If you've been away from the platform for a few years, four changes are worth knowing about before you make any architectural decisions:
Virtual threads (stable in Java 21). Project Loom collapsed the cost of a thread from megabytes of stack to a few hundred bytes. A single JVM can now run millions of concurrent virtual threads. This is the biggest concurrency change in Java's history and it removes the main argument for reactive frameworks like WebFlux on most workloads. Blocking code is fast again.
AOT compilation and native images. GraalVM native image and the JDK's own ahead-of-time caching turn Java apps into binaries that start in tens of milliseconds and use a fraction of the memory of a warm JVM. This used to be a Quarkus or Micronaut differentiator. It's now table stakes across the ecosystem, including Spring Boot 3+.
Records, sealed classes, and pattern matching. The boilerplate that used to push teams toward Lombok or Kotlin is mostly gone. Data-oriented programming in modern Java looks closer to Scala or Kotlin than to Java 8.
Java 25 LTS performance work. Compact object headers shrink object overhead by roughly 22% on heap-heavy workloads. The G1 garbage collector got a redesigned card table in Java 26 that delivers measurable throughput gains on reference-heavy code.
What Java SE doesn't give you
Plain Java SE is honest about its scope. It does not give you:
A web server or HTTP routing layer
Dependency injection
Database access beyond raw JDBC
Transaction management
Security, authentication, or authorization frameworks
A configuration system
You can build all of these by hand. Almost no one does. In practice, "I'm using Java SE" in 2026 means "I'm using Java SE plus a framework that supplies the missing pieces." That framework is the actual decision, which is where the rest of this guide focuses.
What is Jakarta EE? (Formerly Java EE)
Jakarta EE is the modern successor to Java EE, the standardized set of APIs and specifications for building enterprise-scale Java applications. If you wrote enterprise Java between 2000 and 2017, you wrote Java EE. Everything since 2018 is Jakarta EE.
The name change wasn't cosmetic. It came with a migration that every Java team upgrading in 2026 still has to plan for.
What changed: Java EE became Jakarta EE
Oracle transferred Java EE to the Eclipse Foundation in 2017. The platform was renamed Jakarta EE because Oracle retained the "Java" trademark. Java EE 8 (2017) was the last release under the old name. Jakarta EE 8 (2019) was the same platform under new governance.
Then came the breaking change. Starting with Jakarta EE 9 (2020), every package was renamed from javax.* to jakarta.*. An import that used to read import javax.persistence.Entity now reads import jakarta.persistence.Entity. The change was mechanical, but it touched every file in every Jakarta EE codebase on the planet, and it forced every framework that depended on those APIs to publish a major-version break.
This is why Spring Boot 3 (late 2022) was a hard upgrade. Spring Boot 3 dropped javax.* and adopted jakarta.*. Any Spring Boot 2.x application moving to 3.x or 4.x has to migrate the namespace. Tools like Eclipse Transformer and OpenRewrite automate most of it, but the migration is still the gating event for many platform upgrades happening in 2026.
What Jakarta EE provides today
Jakarta EE 11, released in 2025, is the current stable platform. Jakarta EE 12 is in development. The headline specifications most teams interact with are:
CDI (Contexts and Dependency Injection), the dependency injection container at the center of every modern Jakarta EE app. CDI replaced EJB as the default DI mechanism years ago. EJB still exists but is largely a legacy concern.
Jakarta Persistence (JPA) for ORM and database access
Jakarta REST (JAX-RS) for REST endpoints
Jakarta Servlet and WebSocket for HTTP and bidirectional communication
Jakarta Data, new in Jakarta EE 11. A standardized repository pattern, similar in feel to Spring Data, that simplifies persistence access
Jakarta Concurrency, updated in Jakarta EE 11 with first-class virtual thread support
Jakarta Messaging (JMS), Jakarta Transactions (JTA), Jakarta Security, Jakarta Validation, and Jakarta Batch for the rest of the platform
If you're a Spring developer, several of these will look familiar. That's not coincidence. Spring's annotations and patterns shaped Jakarta EE's modernization, and Jakarta EE's specifications now define the underlying APIs Spring builds on. The two ecosystems converged.
Jakarta EE Core Profile: the cloud-native subset
A common objection to Jakarta EE is that it's too heavy for microservices. Jakarta EE 10 answered this directly with the Core Profile: a minimal subset of specifications (CDI Lite, JAX-RS, JSON-P, JSON-B, Annotations, Interceptors, Dependency Injection) explicitly designed for lightweight cloud-native runtimes and AOT compilation.
The Core Profile is what runtimes like Quarkus implement when they want Jakarta EE compatibility without the full platform's footprint. It's the answer to "Jakarta EE doesn't fit in a container." It does. The original critique was about WebSphere and WebLogic, not about Jakarta EE the specification.
Modern Jakarta EE runtimes
In 2026, picking Jakarta EE doesn't mean picking a multi-gigabyte application server. The runtimes teams actually choose are:
Quarkus (Red Hat). Compiles to GraalVM native images. Cold start under 50 ms, memory footprint under 50 MB. Built for containers, serverless, and Kubernetes from day one.
Helidon (Oracle). Available in two flavors: Helidon SE (reactive, lightweight) and Helidon MP (full MicroProfile and Jakarta EE). Native image support.
Open Liberty (IBM). Modular runtime where you load only the features you need. The lightweight profile is competitive with Spring Boot on memory.
Payara Micro and Payara Server. The successor to GlassFish, with strong support for incremental modernization of legacy Java EE workloads.
WildFly (Red Hat). The community upstream of JBoss EAP. Suitable for both traditional app server deployments and bootable JAR packaging.
The legacy "heavyweight Java EE" stereotype belongs to WebSphere full profile and WebLogic. Those are real products with real footprints, but in 2026 they're an active migration target, not a forward choice for new development.
Figure: Modern enterprise Java is a layered stack. Frameworks and runtimes pick their packaging and opinions, but they all sit on the same jakarta.* API surface and the same JVM.
Where the modern Java stacks actually differ
The honest answer to "Spring Boot vs Jakarta EE" in 2026 is that they differ less than they used to and more than the convergence story implies. The two questions worth separating are: what's actually shared now, and where does the choice still change your life as a platform engineer.
What's converged (no longer a real differentiator)
Three things used to be on every Java EE vs Spring comparison and aren't anymore:
The API surface. Spring Boot 3 and 4 use the same jakarta.* packages Jakarta EE itself defines. A Servlet is a Servlet. A @PersistenceContext is a @PersistenceContext. The annotations and types your business logic touches are the same on both stacks.
Concurrency model. Virtual threads (Java 21) work identically under any framework. Both Spring Boot and Jakarta EE Concurrency expose virtual-thread executors. The reactive-or-blocking debate that defined the last five years has largely collapsed for typical CRUD services.
Native compilation. GraalVM native image works for Spring Boot (via Spring AOT), Quarkus, Helidon, Micronaut, and most Jakarta EE runtimes. Cold-start under 100 ms and memory under 100 MB are no longer Quarkus differentiators. They're available on every modern stack with varying degrees of polish.
If a comparison article tells you the choice between Spring Boot and Jakarta EE comes down to APIs, threading, or native compilation, it's working from a 2020 mental model.
Where the stacks still diverge
Four areas actually shape your platform team's day-to-day:
Packaging and deployment. Spring Boot's fat-JAR plus embedded Tomcat or Netty is the assumed baseline across most of the industry. Quarkus and Helidon SE produce equally simple bootable JARs but lean harder on native images for cold-start-sensitive workloads. Open Liberty, Payara, and WildFly support deployable WAR or EAR archives onto a runtime, which still matters in regulated environments where the runtime is provisioned and audited separately from the application.
Startup and memory profile. This is where the real numbers diverge. A typical Spring Boot service on the JVM starts in 2 to 5 seconds and runs in 200 to 400 MB. Quarkus on the JVM lands closer to 1 second and 150 MB. Quarkus or Helidon as a native binary starts in 30 to 80 ms and runs in 30 to 80 MB. If you're scaling to zero, running on edge nodes, or paying per-millisecond on a serverless platform, that gap is the entire reason to look beyond Spring Boot.
Configuration philosophy. Spring Boot leans hard on auto-configuration: pull in a starter, get sane defaults, override what you need. Jakarta EE leans harder on explicit declaration through CDI and standard configuration sources. Neither is objectively better, but they shape how readable a 50-service codebase is to a new hire. Spring Boot wins on initial productivity. Jakarta EE wins on "what is this service actually doing" once the codebase has aged for three years.
Ecosystem and hiring. Spring Boot has the larger community, the larger Stack Overflow corpus, and the deeper integration library ecosystem. For most enterprise teams, that gravity is the dominant factor. Jakarta EE runtimes and Quarkus, Helidon, and Micronaut all have first-class documentation, but the available talent pool is meaningfully smaller. This is a delivery risk, not a technology risk, and it's worth treating it as one.
The honest framing for a platform team in 2026: pick the stack whose packaging, runtime profile, and ecosystem maturity match your actual workload. Don't pick based on philosophical preferences for "standards" or "convention over configuration." Those debates were settled in the convergence.
From convergence to choice: what actually drives the decision in 2026
By this point in the article, the framing should be obvious: Spring Boot, Quarkus, Helidon, Micronaut, and vanilla Jakarta EE on Open Liberty or Payara are not five different platforms. They're five different opinions sitting on the same jakarta.* APIs and the same JVM. So how do teams actually decide?
In practice, four signals do most of the work.
Signal 1: What does the rest of your fleet run?
The single biggest predictor of which stack a new service uses is which stack the team's other services already use. This is not laziness. It's a sound platform decision. Two services on the same framework share build tooling, base container images, observability libraries, configuration patterns, deployment templates, and on-call runbooks. A team running 40 Spring Boot services will pay a real operational tax to introduce a Quarkus service, even if Quarkus is technically the better fit for that one workload.
The exception is when the new workload has a specific profile that the existing stack genuinely can't serve well. A Spring Boot shop building one event-driven function that needs to scale to zero on AWS Lambda has a legitimate reason to reach for Quarkus or a native Spring Boot image. A Jakarta EE shop building one async data-processing service has a legitimate reason to reach for Spring Boot's mature integration ecosystem. The decision rule is not "best tool for the job in isolation," it's "best tool given what we already operate."
Signal 2: What's the deployment target?
The deployment target matters more than most architecture discussions admit. Three patterns dominate:
Long-running services on Kubernetes or VMs. Any framework works. Spring Boot is the path of least resistance because the ecosystem assumes it. Quarkus, Helidon, and Open Liberty's lightweight profiles are competitive on the JVM and pull ahead on memory.
Serverless and scale-to-zero. Cold start is the dominant cost. Native compilation moves from a nice-to-have to a requirement. Quarkus native and Spring Boot native are the realistic options. Helidon SE native is competitive.
Traditional application servers. If the deployment target is an existing WebLogic or WebSphere environment, the question isn't which framework to adopt. The question is whether to keep deploying onto that runtime (Open Liberty and Payara are the modernization paths) or to refactor toward a JAR-based deployment model.
Signal 3: What's the team's reactive vs imperative bias?
Five years ago, this was a religious debate. Virtual threads have mostly settled it for new code. But existing services that are already reactive don't get a free migration, and teams that have built fluency with Project Reactor, RxJava, or Mutiny will keep getting value from those investments.
The practical guidance:
New service, no existing reactive code, typical CRUD or RPC workload: write imperative code on virtual threads. Spring Boot or Jakarta EE either way.
New service, high-fan-out integration or backpressure-sensitive streaming: reactive still wins. Spring WebFlux or Quarkus with Mutiny.
Existing reactive codebase: do not migrate to imperative just because virtual threads exist. The migration cost is real. The benefit is marginal for code that already works.
Signal 4: How much governance do you need?
This is the question that quietly distinguishes Jakarta EE from Spring Boot in regulated environments. Jakarta EE is a specification with multiple compatible implementations. A regulated bank or insurer can require "any Jakarta EE 11 compatible runtime" in a procurement document and have meaningful vendor portability. Spring Boot is a single implementation, governed by VMware. That's fine for most teams. It's a real consideration for organizations with compliance requirements around vendor lock-in.
Quarkus, Helidon, and Open Liberty all sit on the Jakarta EE side of this line because they implement Jakarta EE specifications. Spring Boot does not, despite using jakarta.* packages. The distinction matters less than it used to, but it has not gone away.
The takeaway
The convergence at the API layer means most teams can pick any of these stacks and ship perfectly good software. The choice is no longer a technology bet. It's a fit-to-fleet, fit-to-deployment-target, and fit-to-governance-model decision. The teams that get this wrong are the ones still litigating it as a technology choice.
Why your stack choice shapes reliability and AI SRE
Stack choice does not end at deployment. It shapes how your services emit telemetry, how incidents propagate, and how quickly your platform team can pin down the root cause when something breaks at 2 a.m. The convergence story makes parts of this easier (shared APIs mean shared observability standards) and parts of it harder (mixed fleets mean more surface area for incidents to hide in).
Three operational realities worth thinking through.
1. Mixed fleets are the norm, not the exception
The 2026 platform team rarely operates a single-framework fleet. Most enterprise Java estates look like this: a long tail of Spring Boot services, a growing edge of Quarkus or native-compiled services for cold-start-sensitive workloads, and a stable core of older Jakarta EE applications running on Open Liberty, Payara, or WildFly. Sometimes a few WebLogic or WebSphere systems are still in active modernization.
This mix is fine. It reflects real organizational decisions made over time. But it means your reliability strategy cannot assume framework homogeneity. Health endpoint conventions, log formats, metric names, and tracing instrumentation differ across these stacks unless you actively unify them. The teams that struggle most with incident response are the ones who let each service team pick its own conventions.
2. Observability standards have converged. Implementations have not.
OpenTelemetry has become the cross-stack standard for traces, metrics, and logs in enterprise Java. Spring Boot, Quarkus, Helidon, Micronaut, and most Jakarta EE runtimes all ship with OpenTelemetry instrumentation either built-in or one dependency away. This is genuinely good news for platform teams.
The catch: standardization at the protocol layer does not give you standardization at the convention layer. Two services emitting OpenTelemetry traces can still tag spans with completely different attribute names. Two services emitting metrics can still use different naming conventions for the same operation. AI SRE platforms perform best when the signals they ingest are semantically consistent. That consistency is a platform-engineering decision, not a framework decision.
The practical guidance: pick a single OpenTelemetry semantic convention (the OTel HTTP and database conventions are reasonable defaults) and enforce it across stacks through your shared observability libraries. The framework choice does not matter as much as whether you've made the convention choice at all.
3. Cold-start patterns differ enough to change incident behavior
A typical Spring Boot service on the JVM takes 2 to 5 seconds to start, hits steady-state CPU and memory after another 30 to 60 seconds of JIT warmup, and produces meaningful traces and metrics throughout. A Quarkus native binary starts in under 100 milliseconds and reaches steady state immediately. These are different operational profiles. They produce different incident patterns.
Spring Boot deployments tend to fail visibly during startup or warmup. Native deployments tend to fail at build time or never. Spring Boot scaling events are slower and more forgiving. Native scaling events are faster but more brittle when something is wrong with the binary itself. AI SRE platforms detect anomalies based on baselines, and your baselines should reflect the runtime profile of the service being monitored. A 3-second startup that is normal for a JVM service is a critical anomaly for a native service.
Where AI-driven reliability earns its keep
This is where AI SRE platforms like Harness AI SRE become operationally meaningful. In a single-framework fleet, a senior SRE can mostly hold the operational model in their head. In a mixed fleet of 50 to 500 services across Spring Boot, Quarkus, and legacy Jakarta EE, no human can. The questions AI SRE answers well are exactly the questions mixed-fleet teams ask:
Which of these 12 simultaneous alerts are symptoms of the same root cause?
Is this latency spike on Service A correlated with the deployment of Service B 40 minutes ago?
Has this service's startup pattern drifted from its historical baseline in a way that predicts a future outage?
Across this fleet, which services share the dependency that just got a critical CVE?
These questions are tractable for AI when the underlying telemetry is consistent. They are intractable for humans regardless of telemetry quality. That's the operational case for treating AI SRE as platform infrastructure rather than as a tool individual teams adopt.
The framework choice shapes the data. The platform decision is what you do with it.
Framework decision matrix: which stack fits which workload
The honest answer to "which Java stack should we use" depends on what you're building, what you already operate, and what your deployment target looks like. The matrix below is opinionated and concrete. Use it as a starting point, not a final answer.
Spring Boot
Choose when:
Your team already runs Spring Boot. The hiring pool, ecosystem, and shared platform tooling pay for themselves several times over.
You need the broadest integration library coverage. Spring Data, Spring Security, Spring Cloud, and the surrounding ecosystem are deeper than any Jakarta EE alternative.
You want a single mainstream choice that any senior Java engineer will recognize on day one.
You need a mature reactive option for backpressure-sensitive or very high fan-out workloads (Spring WebFlux).
Avoid when:
Cold-start time is your dominant cost and you don't want to take on Spring Boot AOT and native image build complexity.
Procurement requires multi-vendor specification compatibility. Spring Boot is a single VMware-governed implementation.
Current version baseline: Spring Boot 4.0 (released late 2025), running on Java 21 or 25 LTS. Spring Boot 3.x remains a reasonable choice for teams not ready to upgrade Spring Framework to 7.
Quarkus
Choose when:
You're deploying to serverless, edge, or scale-to-zero environments where cold start and memory footprint are the dominant operational costs.
Native image is a first-class concern, not an afterthought. Quarkus was designed around it.
You want Jakarta EE compatibility with cloud-native packaging and a developer experience optimized for fast feedback loops (live reload, dev services).
You're greenfield or building a clearly-bounded subset of services where the team can absorb the smaller talent pool.
Avoid when:
The team has no existing Quarkus expertise and the workload doesn't actually need native compilation. The startup-time gains on the JVM are real but marginal compared to the hiring cost.
You're deeply invested in Spring-specific libraries (Spring Cloud, Spring Data's full feature set). Quarkus has equivalents for most things, but the migration cost is real.
Helidon
Choose when:
You're an Oracle-aligned shop or run on Oracle Cloud Infrastructure. Helidon is well-supported there.
You want a clear choice between a reactive flavor (Helidon SE) and a full Jakarta EE / MicroProfile flavor (Helidon MP) inside the same product family.
You need native image support backed by an enterprise vendor.
Avoid when:
You're not in an Oracle-leaning environment. The community and ecosystem around Helidon are smaller than Quarkus or Spring Boot, and the network effects matter.
Micronaut
Choose when:
You want compile-time dependency injection to avoid reflection overhead and improve startup time on the JVM, without committing to a native image build.
You're building polyglot teams across Java, Kotlin, and Groovy and want consistent ergonomics.
You like the architectural opinions: ahead-of-time everything, no runtime classpath scanning, fast cold start without GraalVM as a hard requirement.
Avoid when:
You need a deep ecosystem of integration libraries on day one. Micronaut's ecosystem is solid but smaller than Spring's.
Vanilla Jakarta EE on Open Liberty, Payara, or WildFly
Choose when:
You have existing Java EE applications you're modernizing incrementally. These runtimes support the deployable WAR or EAR model and let you upgrade Java versions and Jakarta EE versions without rewriting deployment topology.
Procurement or compliance requires multi-vendor specification compatibility. "Any Jakarta EE 11 compatible runtime" is a meaningful procurement clause. "Spring Boot 4" is not.
You want long-term stability over framework innovation. Jakarta EE specifications evolve more slowly and with stronger backwards compatibility guarantees than Spring Boot.
Your operations team is already proficient with one of these runtimes and the cost of switching outweighs the benefit.
Avoid when:
You're greenfield with no Jakarta EE legacy. The other options on this list will move faster.
A note on legacy WebSphere and WebLogic
Neither of these is a forward choice in 2026. Both are real products with real production footprints, but new development on them is rare outside very specific enterprise circumstances. If you're running WebSphere full profile or WebLogic, the relevant question is the modernization path: typically Open Liberty (the IBM-supported migration target from WebSphere) or Helidon and WildFly (common WebLogic migration targets).
How to actually decide
If you've read this far and the matrix still feels like five reasonable options, default to one of two answers:
Greenfield, no strong existing fleet bias: Spring Boot. The ecosystem advantage compounds over time, and any future "we should have picked X for cold start" pain is fixable with Spring Boot AOT or by introducing a second framework for that specific workload.
Greenfield, cold start matters from day one: Quarkus. The investment in native image tooling and the Quarkus dev experience pays off when scale-to-zero and per-millisecond billing are real costs.
For everything else, the matrix above is a tiebreaker. The decision rule that beats every other rule is: pick the framework your platform team can operate well at 2 a.m.
What this looks like in practice
The article has been pushing toward one conclusion: in 2026, most enterprise Java estates are mixed-framework by design, and the platform team's job is to make that mix operable rather than to force consolidation.
What that looks like concretely:
A Spring Boot core handles the long tail of CRUD services and customer-facing APIs. A handful of Quarkus or native Spring Boot services sit at the edges where cold start matters: serverless functions, event handlers, scale-to-zero workloads. A stable set of Jakarta EE applications on Open Liberty or Payara handles the deeply-integrated systems that have been running reliably for years and would cost more to rewrite than to maintain. Java 21 is the floor across all of it, with a planned migration to Java 25 LTS over the next 12 to 18 months.
This is not an architectural compromise. It is the correct answer for organizations that have grown over time and have services with genuinely different operational profiles. The mistake is treating the mix as a problem to solve rather than an environment to operate.
Four questions worth asking before any new service
When a team proposes adding a new service to the fleet, four questions separate good decisions from defaults:
What does the rest of our fleet run, and is there a specific reason this service should differ? If yes, name the reason. If no, match the fleet.
What's the deployment target, and does cold start materially affect cost or user experience? If yes, native compilation is on the table. If no, the JVM is fine.
What does the service need to integrate with, and which framework's ecosystem makes that easiest? This is usually the strongest signal.
Who's going to operate this at 2 a.m., and what do they already know? The answer almost always points back to the existing fleet.
These questions matter more than any framework comparison because they're the questions a senior platform engineer asks before writing the first line of code. The frameworks themselves have converged enough that the operational fit dominates the technical fit.
From stack choice to delivery velocity: standardize with AI-powered CD and GitOps
The four questions at the end of the previous section all point at the same operational problem. A platform team running a mixed-framework Java fleet faces the same delivery bottleneck regardless of which frameworks are in the mix: ticket-ops and pipeline sprawl that compound with every new service.
The frameworks have converged. The pipelines have not. Most enterprise Java teams still operate one CI/CD configuration for Spring Boot, a different one for Quarkus, a third for Jakarta EE on Open Liberty or Payara, and a long tail of bespoke automation for whatever legacy systems are still in flight. Every new service adds operational surface area. Every framework upgrade creates a coordination problem.
This is the layer where AI-powered continuous delivery and GitOps practices stop being aspirational and become structural. Pull-based deployments through GitOps eliminate the manual approval steps that previously gated Spring Boot rollouts but not Quarkus ones. Policy as Code guardrails enforce the same release strategies, security requirements, and resource limits across every framework in the fleet. Automated verification catches deployment anomalies against each service's own baseline, whether that baseline is a 3-second JVM startup or a 50-millisecond native cold start. Intelligent rollbacks protect production without requiring on-call engineers to remember which framework needs which recovery playbook.
The platform decision is no longer which Java framework to standardize on. It's how to operate the mix you already have without paying a coordination tax on every change.
Frequently asked questions
What is the difference between Java SE and Jakarta EE in 2026?
Java SE is the language, JVM, and core libraries every Java application runs on. Jakarta EE is a set of standardized APIs (CDI, Jakarta Persistence, Jakarta REST, Servlet, Jakarta Data, and others) that extend Java SE for enterprise applications. In 2026, the choice is rarely between Java SE and Jakarta EE directly. It's between frameworks and runtimes (Spring Boot, Quarkus, Helidon, Micronaut, Open Liberty, Payara, WildFly) that all sit on Java SE and most of which implement or interoperate with the Jakarta EE specifications.
Is Java EE the same as Jakarta EE?
Jakarta EE is the direct successor to Java EE under new governance at the Eclipse Foundation. Oracle transferred Java EE to Eclipse in 2017 and the platform was renamed because Oracle retained the "Java" trademark. Java EE 8 (2017) was the last release under the old name. Jakarta EE 8 (2019) was the same platform under the new name. Jakarta EE 11 (2025) is the current stable version.
What is the javax to jakarta namespace migration?
Starting with Jakarta EE 9 in 2020, every Jakarta EE package was renamed from javax.* to jakarta.*. An import that used to read import javax.persistence.Entity now reads import jakarta.persistence.Entity. Spring Boot 3 (late 2022) and Spring Boot 4 both require the new namespace, which means any Spring Boot 2.x application upgrading to 3.x has to migrate every affected import. Tools like Eclipse Transformer and OpenRewrite automate most of the migration, but it remains the gating event for many platform upgrades happening in 2026.
Should I use Spring Boot or Jakarta EE for a new microservice in 2026?
For most greenfield services, Spring Boot is the path of least resistance because of its ecosystem and hiring advantages. Choose a Jakarta EE runtime like Quarkus when cold start time and memory footprint are your dominant operational costs, when you need native compilation as a first-class concern, or when procurement requires multi-vendor specification compatibility. The technical capabilities have largely converged. The decision is mostly about ecosystem fit, deployment target, and what your platform team already operates well.
What's the performance difference between Spring Boot and Quarkus?
On the JVM, a typical Spring Boot service starts in 2 to 5 seconds and runs in 200 to 400 MB, while a Quarkus service starts closer to 1 second and runs in 150 to 250 MB. As GraalVM native binaries, both Spring Boot (via Spring AOT) and Quarkus start in 30 to 100 milliseconds and run in 30 to 80 MB. The real performance difference shows up in cold-start-sensitive deployments like serverless and scale-to-zero workloads, where native compilation moves from a nice-to-have to a requirement.
Which Java version should I target in 2026?
Java 21 LTS is the production baseline for most enterprise Java fleets, and Java 25 LTS (released September 2025) is what platform teams are migrating to over the next 12 to 18 months. Java 17 should be treated as the floor, not the target. Avoid non-LTS releases (currently Java 26) for production unless you have a specific reason to track preview features, since support windows for non-LTS versions are six months. Both Spring Boot 4 and Jakarta EE 11 support Java 21 with first-class enhancements when running on Java 25.
Can Jakarta EE and Spring Boot services run in the same Kubernetes cluster?
Yes, and most enterprise Java fleets do exactly this. The technical compatibility is straightforward because both stacks produce standard container images and both expose health, metrics, and logs through OpenTelemetry-compatible instrumentation. The harder problem is operational consistency: enforcing the same release strategies, observability conventions, and governance policies across both stacks. Policy-as-code and unified delivery pipelines solve this regardless of which frameworks are in the mix.
Is Java EE dead?
Java EE under that name ended in 2017, but the platform is alive and actively developed under the Jakarta EE name at the Eclipse Foundation. Jakarta EE 11 shipped in 2025 with new specifications including Jakarta Data and first-class virtual thread support. Modern runtimes like Quarkus, Helidon, Open Liberty, Payara, and WildFly implement Jakarta EE specifications in cloud-native form. The "Java EE is dead" narrative was specifically about heavyweight application servers like WebLogic and WebSphere full profile, which are an active migration target rather than a forward choice.
Automated Release Management: From CABs to Continuous Delivery
Find out how policy-driven pipelines, continuous delivery and AI-assisted verification are replacing manual CAB processes with automated release management.
Dewan Ahmed
May 14, 2026
Time to read
CABs optimize for perceived safety, not actual risk reduction. Batched releases, surface-level reviews, and meeting cadence latency create the very risks they are intended to mitigate.
Policy as code, automated quality gates, and continuous change tracking enforce the same rules on every change, consistently and at scale.
Speed and safety are not a trade-off if you build the right controls. Smaller, iterative releases with automated verification limit the blast radius and shorten recovery time.
The thing with Change Advisory Boards is that the intent was always good. Get smart people in a room, look at the evidence, and make sure nothing catastrophic goes out the door. In theory, that's hard to argue with.
It doesn't scale in practice. Things happen between meetings. Teams rush to hit the window. The CAB meeting may not catch every risky deployment, but at least everyone can feel good about the process before the incident happens.
Automated release management asks a different question entirely. Not "did a human approve this?" but "has this change actually proven it's safe?" Governance moves into the pipeline itself, running the same checks on every change at whatever speed your teams ship.
That's exactly what Harness Continuous Delivery is built for: policy-driven pipelines, automated assurance, and governance that scales with your teams.
Automated Release Management: What Is It?
Automated release management replaces manual review and approval steps with automated quality gates, policy enforcement, and deployment orchestration.
Rather than routing change decisions through a central committee, automated systems evaluate each change against defined criteria like test coverage, security scans, rollback definitions and compliance checks, then approve or block it based on objective results.
That does not get rid of governance. It brings governance into the delivery pipeline and consistently applies it to all changes, not just the ones that make it onto a CAB agenda.
Automated release management paired with a continuous delivery platform allows teams to deploy frequently, recover quickly, and audit completely, with no meeting necessary.
The Traditional Model: Why CABs Can't Keep Up
The CAB model made sense when software changed slowly and release cycles were long. Cross-functional stakeholders would review evidence packets, testing results, deployment plans, security scans and determine if a release was safe to promote.
The problem is that the model doesn't scale well as the speed of delivery accelerates. Some patterns keep repeating themselves:
Surface inspection. CAB members typically don't have deep, application-level context on the changes they are approving. Reviews are about whether the evidence packet looks complete, not if the change is actually safe.
Grouped risk. Changes build up between cycles and ship together in larger releases. The bigger the release, the bigger the blast radius when things go wrong.
Delayed compounding. Delays build up across teams and sprints waiting for the next CAB slot. Delivery speed becomes a function of the meeting cadence, not the capability of the team.
Big overhead for engineers. Senior engineers spend hours compiling evidence packets and presenting to committees, time that could be spent shipping.
DORA's research provides a useful gut-check here: high-performing engineering teams deploy far more frequently than their peers with lower change failure rates, not higher. It's not approval volume that matters; it's pipeline discipline.
The fundamental problem is not that governance is bad. It is that a meeting-based governance model cannot keep up with a continuous delivery operating model.
From Approval Gates to Automated Assurance
The difference in automated release management boils down to a different question at the heart of the process.
Old model: Who approved this? New model: What did this change prove before we shipped it?
That reframe yields a meaningfully different architecture. Governance takes place on every change, not at scheduled times. Pass/fail criteria are deterministic, not subjective. Compliance is an output of the pipeline, not a prerequisite to enter it.
Building an Automated Release Management Pipeline
1. Continuous Change Tracking
All changes must be traceable without requiring manual compilation. Version control becomes the single source of truth. CI systems automatically generate commit history, build artifacts and deployment-linked changelogs as part of normal pipeline execution. By default, the audit trail is there.
Harness GitOps takes this a step further, using Git as the single source of truth for the state of the deployment. All configuration changes are versioned, all deployments are tracked, and drift is detected automatically.
2. Automated Quality Gates
Validation moves from presentations to execution. Quality gates run on every change: unit and integration tests, end-to-end validation, security and compliance scans, and performance checks. These are not release-window activities. They are part of the standard CI/CD pipeline, running continuously on every change that moves through.
Harness Powerful Pipelines supports multi-stage pipeline orchestration across complex environments with built-in test intelligence and conditional execution logic. Quality gates run fast and don't create unnecessary bottlenecks.
3. Policy as Code
CAB rules get codified in an automated release management model. No critical vulnerabilities before production promotion. Minimum thresholds for test coverage. Mandatory rollback procedure definitions. These policies are automatically enforced in the pipeline. Pass, and the change proceeds. Fail, and it's reliably blocked at scale, with no human bottleneck in the critical path.
That's what policy as code is all about: governance that's version-controlled, auditable and applied the same way every time.
Harness DevOps Pipeline Governance lets teams define and enforce pipeline policies in one place. Compliance is not something you check at the end. It's something the pipeline enforces throughout.
4. AI-Assisted Deployment Verification
Even with strong quality gates, production deployments carry residual risk. Test environments do not always mirror what production surfaces.
Harness AI-Assisted Deployment Verification automatically analyzes deployment health using ML to compare metrics, logs and traces against baseline behavior. When something drifts, it surfaces the signal quickly, enabling rollback before an incident escalates. This closes the loop between deployment and validation, making the pipeline genuinely self-correcting, not just self-approving.
Managing Complex, Interdependent Releases
In practice, systems rarely exist in isolation. One change can affect backend services, APIs, web apps, mobile apps and edge targets all at once. In tightly coupled systems, changes to one component can cause another to break, and partial deployments can be risky without careful coordination.
Traditional coordination uses spreadsheets, emails, and war rooms. Modern automated release management means orchestration: platforms that model service dependencies, trigger pipelines in the right order, and ensure all components pass quality gates before release. Multi-team coordination becomes a single-action, end-to-end deployment.
Harness Continuous Delivery has built-in support for orchestrated multi-service deployments with dependency mapping and conditional promotion logic. Deploy Anywhere extends this to cloud, hybrid, on-prem and edge environments without requiring separate toolchains for each target.
Harness pipelines also support canary deployments and GitOps-based progressive delivery for rollout strategies tailored to deployment risk.
Reducing Coupling Over Time
Managing interdependent releases is a good start. The goal is to reduce the coupling itself so teams can ship independently without synchronized multi-team deployments. Three practices tend to accelerate that:
Contract testing. Services define and verify their contracts, so a change in one will not silently break others.
Feature flags. Feature flags decouple code deployment from feature activation. Code ships continuously; features turn on when they're ready. They also act as a safety net post-deployment. If a feature causes unexpected behavior in production, it can be disabled instantly without a full rollback.
Backward-compatible APIs. Designing for backward compatibility means downstream services do not have to be updated simultaneously with upstream changes.
Together, these patterns move teams toward the continuous delivery ideal: frequent, small, independent releases, each of which is safe on its own.
What Automated Release Management Delivers
The results of replacing CAB-driven processes with policy-driven pipelines and automated assurance are measurable:
More velocity. Releases are continuous, not fixed to a cadence. Time to production goes from weeks to hours.
Consistent quality. All changes go through the same validation, not just the ones that make it onto a CAB agenda.
Less risk. Smaller incremental releases mean a smaller blast radius. Automated rollback means quicker recovery when things go wrong.
Complete auditability. All changes, gates, policy checks and deployments are automatically documented with no manual evidence collection required.
Harness CD Visualize DevOps Data surfaces deployment frequency, change failure rates and mean time to recovery in real time. These are the DORA metrics that measure delivery health with zero instrumentation overhead.
Build Controls That Don't Slow You Down
CABs were created for a slower world, where a weekly review meeting could credibly keep up with the cadence of releases. That world is long gone for most engineering organizations today.
The takeaway here is this: automated release management doesn't remove governance. It rebuilds governance as a system that is fast, consistent, auditable and embedded directly in the delivery pipeline. The teams that move fastest aren't the ones with the loosest controls. They're the ones with controls that don't slow them down.
If you're ready to move from approval bottlenecks to automated assurance, Harness Continuous Delivery is built for exactly that.
Automated release management is the practice of using automated quality gates, policy enforcement and deployment orchestration to replace manual approval steps in the software release process. Rather than routing changes to a committee, the pipeline evaluates each change against predefined criteria and approves or blocks it based on objective results.
What is the difference between automated release management and a CAB process?
A CAB relies on scheduled human review to approve changes before they go into production. Automated release management takes that validation and builds it into the pipeline itself, running the same checks on every change instead of batching them for periodic review. The result is faster delivery with more consistent governance.
What are quality gates in a release pipeline?
Quality gates are automated checkpoints a change must pass before moving to the next stage. Common examples include test coverage thresholds, security scan results, and performance benchmarks. A change that fails a gate is blocked automatically, without human intervention.
What is policy as code?
Policy as code is the practice of expressing governance rules in version-controlled configuration files rather than documents or meeting agendas. The pipeline then automatically enforces those rules on every deployment, making compliance consistent and auditable by default.
What is the role of feature flags in automated release management?
Feature flags decouple code deployment from feature activation. Teams can ship code continuously without exposing unfinished features to users, and can disable a feature instantly if it causes issues in production, without triggering a full rollback.
What deployment strategies work best with automated release management?
Incremental strategies like canary deployments work well because they limit the blast radius of any given change. Paired with automated verification, the pipeline can catch problems early in the rollout and halt or roll back before they affect all users.
How does Harness support automated release management?
Harness Continuous Delivery provides end-to-end pipeline orchestration, built-in policy governance, GitOps-based change tracking, AI-assisted deployment verification, and real-time DORA metrics. It's designed to replace manual release processes with automated systems that scale across any environment.
The AI Productivity Paradox: We're Measuring the Gains and Missing the Costs
AI is making engineering teams faster, but much of the work behind those gains still goes unmeasured. New Harness research explores the hidden costs of AI productivity.
Trevor Stuart
May 13, 2026
Time to read
For the past year, I've been hearing a version of the same thing from engineering leaders: AI tools are working, productivity is up, the business case is there. And yet, something about the picture still feels incomplete. So we decided to go find out how widespread that feeling actually is. We surveyed 700 engineers and managers across five countries, and published the results in the State of Engineering Excellence 2026.
89% of engineering leaders say developer productivity has improved since deploying AI. It's a clean story. AI is working. Engineering teams are moving faster.
But, we also found that 81% of those same leaders say code review time has gone up since deploying AI. Significantly up, in a lot of cases. And, developers estimate that roughly a third of their day is now consumed by AI-related work that remains largely invisible to traditional productivity metrics.
So which is it? Is AI making engineering teams more productive, or simply shifting effort into places they don’t yet measure? After sitting with this data for a few weeks, the answer is both. That's the more honest read, even if it's less satisfying.
The gap between generating code and shipping value
AI has been very good at increasing output. Simultaneously, it has not automatically delivered more shipped value.
I talked to a customer recently, a large enterprise engineering org, and they were genuinely proud of how much their output metrics had improved. Lines of code written, PR velocity per developer, tickets closed, features delivered. All of it up. Then we dug into what was actually making it to production, and the numbers looked much less clean. A meaningful share of AI-generated code was not getting to production.
Most organizations can tell you how much AI code was accepted. Very few can tell you how much of it actually landed in production, and that's the number that matters. Hard dollars spent on agent compute that never shipped anything isn't a productivity story. That's a visibility gap, and it's one most organizations aren't measuring today.
What "invisible work" actually looks like
The 31% figure, the estimated share of developer time now consumed by AI-related work that appears in no metric, probably sounds abstract until you break down what it actually is.
It's a developer sitting with a pull request for 45 minutes because the AI-generated code is technically correct but written in a style nobody on the team recognizes, and they need to fully understand it before they can approve it. It's debugging a subtle edge case that the AI missed, which takes longer to track down than writing the function would have. It's working with 10 agents in parallel on 10 different tasks. None of this makes it into velocity or cycle time, and even code review metrics only catch a fraction of it.
What this data shows is that organizations are running a business where the costs are partially off the books. You can show your CFO a 20% productivity improvement and that's true. You just can't show them what it cost to get there.
High confidence in a broken system is its own problem
The finding that surprised me most: 89% of engineering leaders say their current metrics accurately reflect AI's impact. And 94% say key factors like tech debt, validation time, and developer burnout are missing from those same metrics.
When there's no established standard for measuring something, people default to trusting the frameworks they already know. Not because they've validated them for the new environment, but because they're familiar. High confidence in an incomplete system is a coping mechanism, not an accuracy signal.
The lesson: confidence in your measurement system should go up as you add instrumentation, not stay high when important dimensions of the work are still invisible. When 94% of leaders acknowledge gaps and only 6% think they're equipped to close them, that's not a minor calibration issue. That's a signal worth taking seriously.
The trust problem is structural, not individual
54% of practitioners fear individual performance evaluations based on AI productivity data. Managers, by contrast, show far greater comfort with these systems: they are nearly four times more likely than developers to report having no concerns at all.
Measurement systems almost always get built top-down, by the people who won't be measured by them. The practitioners who experience the day-to-day pressures of AI adoption, and who understand where invisible overhead actually lives, are rarely involved in defining the frameworks used to measure it. The result is a system that captures what leadership can see and misses what developers actually experience.
What developers said they need is straightforward: keep improvement data separate from performance evaluation, be transparent about what's being measured, and involve them in defining the metrics. None of that is technically hard. It requires organizational commitment. When measurement feels like surveillance, you don't get accurate data. You get people performing for the system instead of working in it.
What we're doing about it at Harness
The productivity gains from AI are real. The problem is that organizations are making multi-year investment decisions with dashboards built for a different era, and the gap between what those dashboards show and what's actually happening widens as AI adoption scales.
This is a problem we’ve been thinking deeply about at Harness. We’re working on new capabilities in Software Engineering Insights (SEI) that are designed to give engineering leaders visibility into the full picture: not just how much code is being generated, but how much of it is shipping, what the review and validation overhead actually looks like, and where AI spend is producing returns versus producing churn.
We believe the next generation of engineering measurement needs to be built for AI-native workflows, and we’ll be sharing more about that direction in the coming weeks.
Getting the measurement right isn't a reporting exercise. It's what makes the productivity gains from AI sustainable.
Download the full State of Engineering Excellence 2026 report [here].
Transform complex multi-service releases from coordination chaos to structured, auditable processes with end-to-end visibility and automation.
Vishal Vishwaroop
May 7, 2026
Time to read
Modern software delivery has evolved far beyond single-service deployments. Today's releases span dozens of services, multiple teams, and complex approval workflows—coordinated through spreadsheets, Slack channels, and manual checklists scattered across tools. When a production release involves deploying ten microservices across three environments, enabling five feature flags, running security scans, collecting approvals from four stakeholders, and coordinating with three different teams, the question isn't whether you can ship—it's whether you can track what shipped, when it shipped, and who approved it.
Release Orchestration solves this. It provides a unified framework for modeling, scheduling, automating, and tracking complex software releases across teams, tools, and environments—giving you end-to-end visibility from planning through production deployment and monitoring.
Why Release Orchestration Matters
Without orchestration, enterprise releases become coordination nightmares. Status lives in spreadsheets that go stale within hours. Coordination happens through email threads spanning dozens of messages. There's no single source of truth for what was deployed, when, or by whom. Manual checklists drift out of sync. Approval workflows rely on memory and goodwill. And when something goes wrong at 2 AM, reconstructing what happened requires археology across multiple systems.
Release Orchestration transforms this chaos into structured, auditable, repeatable processes. Model your release blueprint once—defining phases, activities, dependencies, and approval gates—then execute it repeatedly with different configurations. Automate pipeline-backed steps while retaining manual sign-offs where governance requires them. Track activity-level status, phase-level progress, and overall release health in real time. Enforce approvals, capture sign-offs, and maintain a full audit trail linking code to deployment to business outcome.
The result? Releases that used to require days of coordination now run faster with complete visibility and zero spreadsheets.
How Release Orchestration Works
Release Orchestration introduces a structured, visual approach to modeling and executing releases. Define Processes—reusable blueprints composed of Phases (Build, Testing, Deployment) and Activities (automated pipelines, manual approvals, or nested subprocesses). Release Groups define cadences and automatically generate releases. The Release Calendar provides unified visibility across all releases. The Activity Store and Input Store promote reusability—define once, execute many times with different configurations. And ad hoc releases let you execute any process on demand when you need flexibility outside your regular schedule.
At its core, Release Orchestration delivers the foundational capabilities enterprise teams need: process modeling with visual editors, scheduled and recurring releases through release groups, real-time execution tracking with dependency management, comprehensive audit trails for compliance, and AI-powered process creation that transforms natural language descriptions into structured workflows. These capabilities form the foundation for enterprise release management at scale.
What's New: Capabilities Launching with Release Orchestration
Release Orchestration launches with a comprehensive set of capabilities designed for enterprise release management. Here's what you can do today.
Ad Hoc Releases: Create Releases On-the-Go
Not every release fits a scheduled release. Customer-specific deployments, unscheduled maintenance, and process testing need one-off releases. Ad hoc releases let you create and execute releases on demand-select a process, configure timing, provide inputs, and optionally run immediately. Test new processes in isolation, handle customer deployments without disrupting your calendar, or orchestrate emergency maintenance with full tracking and audit capabilities.
Modern releases deploy multiple services across multiple environments. Release Orchestration's input system handles this through variable mapping—define global variables like releaseVersion and `targetEnvironment` once, and they flow automatically to all activities. Deploy to QA with "QA Inputs," production with "Production Inputs"—same process, different configurations. This eliminates repetitive data entry, ensures consistency, and scales from three services to thirty without growing complexity.
Release Orchestration integrates with Harness's centralized notification framework, delivering alerts when releases start, pause for input, complete, or fail. Route notifications to Slack, email, PagerDuty, Microsoft Teams, or webhooks. Platform teams managing multiple releases shift from reactive monitoring to proactive awareness—get notified immediately when action is required.
Reporting: Download Detailed Execution Reports
Compliance reviews and post-mortems require detailed records. Release Orchestration provides downloadable Excel reports with complete execution history—every activity, status, timestamps, approvals, and inputs used. Generate reports for individual releases (sprint retrospectives) or release groups (quarterly audits). Activity-level detail meets compliance needs; process-level overviews serve executive summaries. All execution data is captured in the audit trail, allowing you to reconstruct exactly what happened during any release.
As releases scale, filters help you focus. Filter by source (ad hoc vs recurring), status (in progress, completed, failed), time window (this sprint, Q1 2026), environment (production, staging), or scope (specific orgs/projects). Platform teams filter to ad hoc releases for one-off deployments. Release managers filter by status for in-progress releases. Compliance teams filter by date range for audit periods. Transform an overwhelming calendar into a focused view of exactly what you need.
Production incidents don't wait for your release cadence. Release Orchestration supports hotfix workflows that fast-track emergency releases while maintaining governance. Mark releases as hotfixes to distinguish them in calendars and reports. The system detects execution conflicts—if a hotfix targets an environment where a release is running, you get visibility to coordinate decisions. Hotfixes use the same process structure, ensuring that approvals and audit trails are maintained. The hotfix designation flows through reports and logs, documenting emergency procedures for post-incident reviews. Speed meets governance.
Manual Activities: Approvals and Sign-Offs in Context
Not everything can be automated. Security reviews, architectural approvals, and stakeholder sign-offs require human judgment. Release Orchestration treats manual activities as first-class citizens with the same visibility and dependency support as automated activities. Manual activities pause execution until someone provides input—an approval, verification, or checklist confirmation. Notifications alert the responsible person; they review the context and complete the activity, optionally leaving notes. Manual activities can depend on automated activities (approval after deployment) or vice versa (deployment after approval). All completions appear in audit trails and reports for compliance documentation.
Release Orchestration provides primitives—processes, phases, activities, dependencies, inputs—that compose to match how your organization ships software. Model microservice releases with parallel deployments and end-to-end tracking. Define compliance-driven releases with approval gates at critical checkpoints. Create streamlined hotfix workflows for emergencies. Coordinate feature flag enablement with deployments. Assign phase owners for multi-team coordination with notification-driven handoffs. The system scales from simple three-phase releases to complex workflows with fifty activities and nested subprocesses.
AI-Powered Process Creation
Harness AI transforms natural language descriptions into structured processes. Describe your workflow—"Create a multi-service release with phases for build, testing, deployment, and monitoring. Assign owners for Development, QA, and DevOps,"—and AI generates the complete structure with phases, activities, and dependencies. Refine the generated process by adding activities, adjusting dependencies, and configuring inputs. This reduces process modeling time from hours to minutes, making it practical to create specialized processes for different release types.
Release Orchestration provides real-time tracking at three levels: activity (running, succeeded, failed, waiting), phase (overall progress), and process (end-to-end status). The execution graph shows phases as nodes, dependencies as arrows, and color-coded status on each activity. Drill into pipeline executions from the release view with one click. See approval history for manual activities—who approved, when, and with what notes. This unified view eliminates the need to check multiple systems. Platform teams can see at a glance which releases are progressing smoothly, which are awaiting approval, and which need attention. [Learn more →](https://developer.harness.io/docs/release-orchestration/execution/activity-execution-flow)
Get Started with Release Orchestration
Release Orchestration is available now in Harness. Contact Harness Support to enable the module for your account. Once enabled, explore Processes (model release blueprints), Release Calendar (schedule and track releases), ActivityStore (reusable activities), and Input Store (configuration sets). The getting started guide walks you through creating your first AI-powered process, adding activities, and executing a release.
We're actively developing additional capabilities: deeper analytics and insights (release velocity metrics, phase duration trends, failure pattern analysis), advanced dependency modeling (cross-release dependencies, environment-level locking), enhanced collaboration (in-line comments, Slack-native monitoring), a template marketplace for common release patterns, and API/GitOps for managing processes as code. The roadmap prioritizes capabilities that help teams ship faster with greater confidence.
Transform Your Release Process
Software delivery has evolved far beyond single-service deployments, but release management tooling hasn't kept pace. Spreadsheets, email coordination, and manual checklists don't scale to modern microservice architectures, multi-team workflows, and compliance requirements. Release Orchestration provides the unified framework enterprise teams need to model, automate, and track complex releases across teams, tools, and environments.
Define reusable processes. Execute them with different inputs. Track activity-level progress. Enforce approvals and capture sign-offs. Maintain complete audit trails. All in one place, integrated with the pipelines and deployment workflows you already use.
Ready to see it in action? Explore the Release Orchestration documentation or reach out to your Harness account team to discuss how Release Orchestration can transform your release workflows.
The future of release management isn't about doing the same manual coordination faster—it's about orchestrating releases as structured, repeatable, auditable processes. That future is available today.
Git tags for pipeline versioning, AI-assisted policy authoring, DAG execution, and enhanced observability for scaling pipeline automation.
Vishal Vishwaroop
May 7, 2026
Time to read
Welcome to our Q1 2026 Pipeline update! This quarter brings eight major enhancements that make pipeline development faster, validation easier, and governance stronger. From Git tags for immutable pipeline versions to AI-assisted policy authoring, these capabilities address the most common friction points teams encounter when scaling pipeline automation across their organizations. This update complements our Continuous Delivery & GitOps update released today, which covers expansions to the deployment platform and AI-powered verification.
Git Experience Enhancements
Pipeline development workflows gain significant GitX improvements this quarter, bringing immutable versioning, flexible testing, and pre-commit validation directly into your Git-based workflows.
Git Tags for Pipeline Executions
Pipelines stored in Git can now be triggered and executed from Git tags, not just branches. This unlocks release workflows where pipeline versions align with semantic versioning tags in your repository—when you tag a release as `v2.1.0` in Git, run that exact pipeline version via the UI or API. Tags provide immutable references to specific pipeline states, making it easy to replay historical pipeline configurations for compliance audits, debugging, or managing multiple product versions in parallel.
Pipeline chaining now supports branch selection for child pipelines, not just the default master branch. When configuring a Pipeline stage, specify which branch of the child pipeline to execute, enabling proper testing of parent-child pipeline integrations before merging to production. This is crucial when output variables from the child pipeline are only available in a feature branch, or when you're testing coordinated changes across multiple chained pipelines.
A new validation API lets you check pipeline YAML before committing changes to your repository. The API validates YAML syntax, schema conformance, entity references (Services, Environments, Connectors, Templates), RBAC permissions, OPA policy compliance, and expression syntax—all without actually running the pipeline or updating it in Harness. This closes a critical gap in GitOps workflows: changes made directly in GitHub bypass Harness validation, enabling teams to validate bulk updates in feature branches before merging and to catch configuration errors early.
DAG Support
Directed Acyclic Graph (DAG) execution support moves to Phase 2 with full UI integration. Define complex step dependencies in which multiple steps can run in parallel but must complete before downstream steps begin, within a single stage. DAG support enables sophisticated deployment patterns, such as parallel infrastructure provisioning followed by application deployment, or concurrent test suite execution with a final aggregation step. The visual graph makes it easy to understand execution flow and identify bottlenecks, while the declarative YAML representation keeps configuration simple.
Observability & Notifications
Pipeline observability and notification capabilities expand to give platform teams better visibility into queue states and more granular control over failure alerting.
Queued Pipeline Visibility
A new Account Settings page surfaces all queued pipelines across your entire account, showing queue position, org/project filters, and estimated execution order. The queue view includes bulk abort capabilities for queued pipelines and is available to Account Admins. For teams using pipeline queues to manage deployment locks or shared resource access, this visibility eliminates the mystery of why a pipeline is waiting and how long it's likely to remain queued.
Centralized notifications now support step-specific failure triggers, not just stage-level or pipeline-level failures. Configure notifications to fire only when a particular critical step fails—like a production deployment step or a compliance validation check—reducing alert noise and ensuring teams get notified about failures that actually matter. This granular control means you can route different failure types to different teams or channels: a failed security scan notifies the security team, while a failed deployment step notifies the on-call engineer.
OPA policy capabilities receive significant AI-powered enhancements and full GitX integration, making governance more accessible and easier to scale across organizations.
AI-Assisted Policy Authoring
An AI assistant helps write OPA policies, reducing the expertise barrier for policy creation. Describe your governance requirements in natural language, and the assistant generates the corresponding Rego policy with explanations of how it works. This democratizes policy authoring beyond Rego experts, enabling security teams, compliance officers, and platform engineers to codify governance requirements without deep OPA expertise.
OPA policies now support the full GitX experience, including branch switching, bidirectional sync, and package name management. Policies can be developed and tested in feature branches before rolling out to production, with PR workflows providing change review and approval. This brings the same infrastructure-as-code benefits you have for pipelines and templates to your governance layer, enabling version control, change tracking, and collaborative policy development.
New APIs support evaluation by both policy set IDs and entity-type/action pairs, giving teams greater flexibility in structuring and applying policies across their organizations. This enables more sophisticated policy architectures in which different evaluation strategies can be applied to distinct workflows or organizational structures.
The features highlighted in this update are available now in Harness Platform. Ready to see them in action? We've created a comprehensive video playlist that walks through these capabilities, featuring live demos and configuration guides.
From Git-based pipeline versioning to AI-assisted policy authoring, this quarter delivers capabilities that streamline development workflows, improve validation practices, and strengthen governance controls. Whether you're managing dozens or thousands of pipelines, these enhancements reduce configuration overhead and align with how modern platform engineering teams scale automation across their organizations.
Be sure to also check out our companion post covering [Continuous Delivery & GitOps innovations](#)—including AI-powered verification, Azure Container Apps support, Windows deployment enhancements, and more.
Explore the documentation links throughout this post to dive deeper into each feature, or reach out to your Harness account team to discuss how these capabilities can accelerate your pipeline development and governance workflows.
What's coming next? Q2 2026 will bring advanced pipeline debugging capabilities, expanded expression engine functionality, and continued investment in GitX experience improvements. Stay tuned for more updates - we're just getting started.
AI-powered verification, Azure Container Apps support, Windows deployment enhancements, and GitOps workflow improvements for modern software delivery.
Vishal Vishwaroop
May 7, 2026
Time to read
Welcome back to the quarterly update series! If you've been following along, you've seen how Q3 2025 brought [deeper control and strengthened integrations], while Q4 2025 [closed the year strong] with platform upgrades and quality-of-life improvements. The first quarter of 2026 builds on these foundations with AI-powered continuous verification that eliminates configuration overhead, expanded deployment platform support, and GitOps workflow enhancements that align with how teams actually ship software.
Deployments
Azure Container Apps Deployments
Native support for Azure Container Apps brings serverless container orchestration to your Azure workloads with the full Harness deployment experience. Azure Container Apps provides a fully managed platform for running microservices and containerized applications with automatic scaling based on HTTP traffic or events, and now you can deploy to it with the same confidence and control you have for Kubernetes, ECS, and other platforms.
Harness gives you two deployment strategies designed for Azure Container Apps' architecture. Choose Basic deployments for immediate traffic cutover when you need speed, or leverage Canary deployments with progressive traffic shifting (20% → 70% → 100%) using Azure Container Apps' built-in revision management to validate new versions under real production load. The platform includes an automated rollback that captures container app state before deployment, enabling instant recovery if issues arise. Authentication is flexible—support for both Azure OIDC (keyless authentication) and Service Principal methods means you can deploy across subscriptions using a single connector, with full support for Azure Container Registry (ACR) and Docker Hub as artifact sources.
This year, we're focusing heavily on Windows deployments to address the performance and scalability challenges that enterprise Windows teams face every day. The two enhancements shipping this quarter are just the beginning—we're bringing the same innovation velocity to Windows deployments that you've come to expect across all Harness platforms. Stay tuned for more Windows Deployment capabilities throughout 2026 that will continue to streamline your deployment processes and eliminate friction in enterprise Windows environments.
Windows Session Reuse eliminates redundant connection overhead by enabling delegate-wide session pooling, cutting connection setup time from 30-60 seconds to instant reuse in JEA environments. When a command step executes, Harness checks the pool for an existing idle session to the target host with matching credentials and reuses it immediately, dramatically reducing pipeline execution time for workflows with multiple command steps.
Multi-Host Deployment with Dynamic Targeting extends Windows Deployment credential setup to dynamically target different hosts, enabling true parallel execution across multiple Windows servers. Configure multiple host groups within a single credential configuration, and Harness automatically routes commands to the appropriate servers based on your deployment strategy. This unlocks centralized credential management while maintaining the security boundaries required in JEA environments, enabling teams managing large Windows server fleets to deploy faster with reduced credential sprawl.
Smarter Amazon Elastic Container Service Management
Amazon ECS deployments get two powerful new capabilities that bring operational flexibility and automation to your container workloads.
Standalone ECS Scaling
Standalone ECS Scaling lets you scale services up or down without triggering a full deployment, enabling operators to respond to real-time demand without triggering change management processes. The new ECS Scale step lets you modify desired task counts on demand—whether you're responding to traffic spikes, performing maintenance windows, or testing capacity limits—without redeploying your application.
ECS Scheduled Actions enable time-based scaling policies directly within your ECS service deployments, eliminating the need to manage scheduled actions separately in the AWS console while keeping your entire ECS configuration under version control. Define scheduled actions to automatically adjust desired task counts at specific times—scale up services before anticipated morning traffic, scale down during off-peak hours, or align capacity with predictable business patterns.
Terraform deployments now include automatic security protections that prevent accidental exposure of sensitive data throughout your pipeline workflows.
Masking Sensitive Terraform Outputs
Terraform outputs marked as `sensitive = true` are now automatically masked in the Harness UI, preventing accidental exposure of credentials, API keys, and other secrets in pipeline execution logs and output tabs. When Terraform outputs are marked as sensitive, Harness respects that designation and redacts the values wherever they appear—you can still reference these outputs in downstream steps using expressions, but the actual values remain encrypted and hidden from view.
This quarter's focus on continuous verification centers on eliminating configuration overhead through AI automation and expanding observability platform integrations. From zero-config deployment health analysis to Git-based configuration management, these capabilities make verification accessible to more teams while reducing the time to production-ready monitoring.
AI-Powered Continuous Verification
Alongside AI Verify, AI-assisted health source configuration makes traditional verification setup effortless through a guided workflow that discovers available signals from your observability platform, classifies them by deployment impact, and generates verification-ready configurations. Describe your service and monitoring goals in natural language, and the Configuration Agent automatically discovers relevant metrics, organizes them into intelligent categories, and generates the queries and thresholds for you—with human checkpoints for selection and refinement at every stage.
Fine-tune configurations with simple natural language inputs or create custom composite metrics on the fly. What used to take hours now takes minutes.
AI Verify eliminates the manual setup complexity that has traditionally slowed the adoption of continuous verification. No more baseline configuration, threshold tuning, or monitored service management. AI Verify deploys lightweight data-collection plugins into your Kubernetes cluster that collect, aggregate, and provide observability data while stripping personally identifiable information before it leaves your environment.
The plugins gather logs and metrics from your observability platforms and perform statistical and algorithmic anomaly detection. Large language models then contextualize these anomalies against your deployment verification criteria, filter false positives based on business-criticality, and synthesize natural-language root-cause insights with actionable remediation suggestions—all without requiring explicit baseline data. This shifts continuous verification from weeks of configuration work to immediate, intelligent monitoring that understands your services from day one.
Harness Continuous Verification now supports Dynatrace Query Language (DQL) for querying timeseries metrics from Dynatrace Grail, their next-generation data lakehouse. Craft sophisticated metric analysis using aggregation functions, enable dimension-based data splitting for per-instance continuous verification, and combine multiple data sources in a single query. This extends beyond the traditional Full Stack Observability model, giving you direct access to custom metric queries rather than relying solely on predefined metric packs.
GitOps workflows gain AI-powered intelligence, unified notifications, and enhanced PR capabilities this quarter. These improvements streamline application management, improve operational visibility, and align GitOps workflows with how teams naturally collaborate through pull requests.
AI-Powered GitOps Operations
AI-powered operations management brings natural language queries and intelligent automation to GitOps applications, AppSets, and clusters. Ask questions like "What applications are out of sync?" or "Which syncs failed in the past 24 hours?" and get instant answers drawn from your entire GitOps deployment landscape. The AI agent can also trigger operations—such as syncing all applications managing non-prod services with a single command or generating pipeline snippets for common GitOps workflows. This transforms dashboards and manual queries into conversational operations management, making GitOps accessible to platform teams, developers, and operators alike.
GitOps applications now integrate with Harness's centralized notification framework, bringing the same notification capabilities available for pipelines to your GitOps workflows. Track application sync events—start, complete, success, and failure—alongside ApplicationSet creation, sync, and error events through Slack, email, PagerDuty, Microsoft Teams, or any webhook-compatible system. Configure notification rules at the account, organization, or project level using the same interface you already use for pipeline notifications.
GitOps PR-based workflows get two key improvements. The Update Release Repo step can now block until the raised PR is merged, eliminating the need for separate Merge PR steps and manual approval stage coordination—the step creates the PR, waits for review, and proceeds once merged. Squash and Merge Support brings native squash-and-merge strategies to the Merge PR step, working with GitHub App tokens and following your repository's configured merge strategies to maintain a clean, linear repository history.
The features highlighted in this update are available now in Harness CD and GitOps. Ready to see them in action? We've created a comprehensive video playlist that walks through these capabilities, featuring live demos and configuration guides.
From AI-powered verification that understands your deployments from day one to Windows performance breakthroughs and GitOps workflow enhancements, this quarter delivers capabilities that eliminate configuration overhead, expand platform coverage, and align with how modern teams ship software.
Explore the documentation links throughout this post to dive deeper into each feature, or reach out to your Harness account team to discuss how these capabilities can accelerate your delivery workflows.
What's coming next? Q2 2026 will bring deeper integrations with cloud-native platforms, expanded AI capabilities across the deployment lifecycle, and continued investment in developer experience improvements. Stay tuned for more updates—we're just getting started.
Harness Expands Infrastructure as Code Management with Native Terragrunt Support and Multi-IaC Innovation
Harness IaCM now delivers native Terragrunt support—enabling teams to unify Terraform, OpenTofu, and orchestration workflows with built-in governance, visibility, and scale.
Teams can now manage Terraform, OpenTofu, and Terragrunt in a single platform without fragmented tooling.
Built-in governance, policy enforcement, and approvals streamline secure infrastructure operations.
End-to-end visibility and drift detection improve reliability across complex, multi-environment deployments.
The launch marks a major step toward a unified, multi-IaC control plane for modern infrastructure teams.
Bringing First-Class Terragrunt Support to IaCM
“We’ve been operating in a hybrid environment with both OpenTofu and Terragrunt, and Harness has made it much easier to bring those workflows together into a single, consistent platform with IaCM. The addition of Terragrunt support is a valuable step toward simplifying how we manage infrastructure at scale.”
— Lead Platform Engineer, Enterprise Customer
Infrastructure as Code is now a standard for modern cloud operations, with most enterprises using IaC to provision and manage environments. However, as adoption grows, so does complexity. Teams are no longer managing a handful of environments. They are operating across multiple regions, accounts, and services, often at massive scale.
This is where traditional approaches begin to fall short.
As organizations scale their infrastructure, Terraform alone is often not enough. Teams adopt Terragrunt to manage complex, multi-environment deployments, but they are often forced to stitch together fragmented tooling that lacks visibility, governance, and consistency.
At Harness, we are changing that.
Today, we are excited to announce native Terragrunt support in Harness IaCM, bringing it to full parity with Terraform and OpenTofu while delivering capabilities that go beyond what is available in standalone tooling. This is more than support. It is about making Terragrunt a first-class platform for enterprise infrastructure management.
Orchestrate complex Terragrunt environments with full visibility across all units
Apply cost estimation, approvals, and policy enforcement natively
Detect and manage drift across environments with granular insights
View infrastructure changes at the resource level across orchestrated deployments
Terragrunt has become a critical layer for managing infrastructure at scale because it simplifies how teams structure and reuse configurations across environments. Harness builds on that foundation with deep, native integration, enabling platform teams to operate with both flexibility and control.
This is especially important for enterprises where a single deployment spans multiple environments and services. Harness abstracts that complexity while maintaining governance, auditability, and consistency.
Extending IaCM to a Multi-IaC Future
Terragrunt is part of a broader shift toward multi-tool infrastructure strategies.
Modern teams are no longer standardized on a single IaC tool. Instead, they operate across:
Terraform and OpenTofu for provisioning
Terragrunt for orchestration
CDK for developer-driven infrastructure
Ansible for configuration and automation
This creates challenges around consistency, visibility, and governance. Harness IaCM is built for this reality. We are evolving IaCM into a unified control plane for multi-IaC workflows, where teams can manage different frameworks with a consistent experience, shared policies, and centralized visibility.
This means:
Eliminating fragmented pipelines across tools
Standardizing governance across environments
Gaining full visibility into infrastructure state and changes
Instead of managing infrastructure in silos, teams can now operate from a single platform across the entire lifecycle.
We are continuing to support modern frameworks like AWS CDK, enabling developer-centric infrastructure workflows alongside provisioning, configuration, and orchestration tools.
AI-Driven Automation
We are introducing intelligence into IaC workflows to simplify tasks such as drift management and optimization. This helps teams reduce manual effort and operate more efficiently at scale.
Together, these investments move IaCM toward a unified, multi-IaC platform that combines flexibility, governance, and automation. Terragrunt has become essential for managing infrastructure at scale but until now, it hasn’t had a platform that truly supports it. As infrastructure continues to grow in complexity, our focus remains the same. Helping teams move faster, reduce risk, and scale with confidence no matter which IaC tools they use.
Infrastructure as Code Management: Terragrunt & Multi-IaC
Discover how Infrastructure as Code management evolves with native Terragrunt support and multi-IaC innovation. Learn more about Harness IaCM.
Mrinalini Sugosh
April 29, 2026
Time to read
What happens when your Infrastructure as Code management strategy works perfectly in dev, scales reasonably well in staging, and then quietly fractures across seventeen production workspaces because nobody documented which Terragrunt wrapper goes with which AWS account? You spend Friday afternoon reverse-engineering DRY patterns that made sense six months ago, wondering why your team is managing three different IaC execution engines with four incompatible workflow philosophies.
This scenario isn't hypothetical. It's the reality of organizations that adopted IaC incrementally, layer by layer, without a unified management approach. One team standardized on OpenTofu for new infrastructure. Another maintained legacy Terraform configurations because migration felt risky. A third discovered Terragrunt and used it to wrangle complexity across AWS regions, but now those wrappers exist outside any centralized governance model. Each decision was rational in isolation. Together, they created an orchestration problem masquerading as a tooling problem.
The actual challenge isn't choosing between Terraform, OpenTofu, or Terragrunt. It's managing their outputs, enforcing policy consistently across execution contexts, and ensuring that infrastructure changes don't outpace your ability to understand what's deployed.
The Hidden Complexity of Multi-IaC Environments
Most platform teams don't set out to run multiple IaC tools simultaneously. They inherit Terraform state from acquisitions, adopt OpenTofu for licensing predictability, and introduce Terragrunt because someone needed to stop copying backend configurations across 40 AWS accounts. The tools themselves aren't the problem. The problem is that each tool introduces its own state management assumptions, module resolution logic, and workflow expectations.
Terragrunt, for instance, exists specifically to solve Terraform's verbosity problem. It lets you define backend configurations once and reference them across environments. It supports dependency graphs so you can deploy a VPC before attempting to create subnets. These capabilities are valuable, but they also mean your actual infrastructure logic now spans two layers: the Terraform or OpenTofu code that defines resources, and the Terragrunt configuration that orchestrates execution.
When you lack centralized Infrastructure as Code management, those layers drift independently. Someone updates a Terragrunt dependency graph without realizing it breaks a downstream workspace. Another engineer modifies an OpenTofu module but forgets that three different Terragrunt configurations depend on its output structure. You don't discover these issues until a deployment fails in production, and the postmortem reveals that nobody had visibility into the full dependency chain.
Why Workflow Sprawl Defeats Governance at Scale
The typical response to multi-IaC complexity is to standardize on one tool and deprecate the others. That works if you're early in your IaC journey. It's impractical if you're managing hundreds of workspaces across regulated environments where compliance audits expect immutable infrastructure definitions and audit trails for every state change.
Here's what actually happens: platform teams create custom CI/CD pipelines for each tool. Terraform runs in Jenkins. OpenTofu runs in GitHub Actions. Terragrunt configurations use a shell script someone wrote during an incident. Each pipeline implements drift detection differently. Policy enforcement exists as scattered OPA rules that don't share a common evaluation context. When an auditor asks, "How do you prevent unapproved infrastructure changes?", the honest answer is, "We run some checks in some places, and we hope teams remember to use them."
This isn't negligence. It's what emerges when Infrastructure as Code management tooling doesn't natively support the reality of polyglot IaC environments. Teams need a system that treats OpenTofu, Terraform, and Terragrunt as execution details, not architectural boundaries. The workflow layer—plan generation, policy evaluation, approval gates, state locking—should remain consistent regardless of which engine interprets the configuration.
Infrastructure Automation Tools Need Orchestration, Not Just Execution
Running `terragrunt apply` successfully doesn't mean your infrastructure is well-managed. It means Terragrunt successfully invoked OpenTofu or Terraform and applied a configuration. The actual management work—validating inputs, enforcing cost policies, detecting drift, promoting changes through environments—exists outside the execution layer.
This is where most homegrown solutions collapse under their own weight. You build a wrapper script that runs Terragrunt with the right flags. Then you add pre-commit hooks for policy checks. Then you integrate Sentinel or OPA, but only for workspaces that someone remembered to configure. Then you add Slack notifications so people know when drift occurs, but the notifications don't include enough context to act on them. Eventually, you have a Rube Goldberg machine that works until it doesn't, and debugging requires institutional knowledge that exists in one person's head.
The fundamental issue is that IaC workflow optimization requires thinking beyond execution engines. You need orchestration that understands module dependencies, workspace relationships, and policy boundaries. You need variable management that doesn't require copying YAML files between repositories. You need drift detection that runs automatically and surfaces meaningful deltas, not raw Terraform output dumped into a log file.
Terragrunt Support as a First-Class Workflow Primitive
Treating Terragrunt as an afterthought—something teams bolt onto existing Terraform or OpenTofu pipelines—misses its architectural intent. Terragrunt exists because managing backend configurations, passing outputs between modules, and orchestrating multi-account deployments shouldn't require copying boilerplate across dozens of directories. When Infrastructure as Code management platforms support Terragrunt natively, they acknowledge this reality: the DRY principle applies to infrastructure orchestration, not just resource definitions.
Native Terragrunt support means the platform understands dependency graphs without requiring custom parsing logic. It means workspace templates can reference Terragrunt configurations directly, rather than forcing teams to flatten everything into monolithic Terraform modules. It means policy enforcement applies before Terragrunt invokes the underlying execution engine, catching invalid configurations before they generate failed plans.
This matters most in organizations running multi-region or multi-cloud architectures. A typical pattern: one Terragrunt configuration defines networking across AWS regions, another manages Kubernetes clusters, a third provisions databases. Each configuration depends on outputs from the others. Without native orchestration, teams either write brittle shell scripts to sequence these dependencies or accept that deployments sometimes fail halfway through because someone applied changes out of order.
Multi-IaC Tools Require Unified State and Policy Management
The real test of an Infrastructure as Code management platform isn't whether it runs OpenTofu or Terraform. It's whether it provides consistent state visibility, policy enforcement, and audit trails across both. If your platform requires separate workflows for each execution engine, you've automated the mechanics but not the governance.
Consider policy evaluation. A reasonable security requirement: no S3 buckets should allow public read access. With fragmented tooling, you implement this rule multiple times. Once for Terraform workspaces using Sentinel. Again for OpenTofu configurations using OPA. A third time for Terragrunt-managed infrastructure, where you're not sure which policy engine applies because Terragrunt is just orchestrating calls to Terraform or OpenTofu. When an audit occurs, you can't prove consistent enforcement because there's no unified policy evaluation layer.
The same fragmentation affects drift detection. Terraform Cloud detects drift for Terraform-managed resources. Your OpenTofu workspaces might run scheduled reconciliation jobs, or they might not—it depends on whether someone configured them. Terragrunt configurations drift silently unless you've built custom tooling to periodically run `terragrunt plan` and parse the output. The result: partial visibility across your infrastructure estate, where "managed by IaC" becomes aspirational rather than descriptive.
OpenTofu Integration and Terraform Alternatives in Practice
Organizations exploring Terraform alternatives often focus on licensing or community governance. Those considerations matter, but they don't address the operational question: how do you manage infrastructure deployed with multiple execution engines without creating parallel workflow systems?
OpenTofu integration means more than "we can run OpenTofu commands." It means workspaces provisioned for OpenTofu behave identically to Terraform workspaces at the orchestration layer. Variable sets apply consistently. Policy evaluation uses the same rule sets. Drift detection runs on the same schedule. Approval workflows follow the same governance model. The execution engine becomes an implementation detail, not a workflow boundary.
This distinction matters during migrations. Teams don't flip entire infrastructure estates from Terraform to OpenTofu overnight. They migrate incrementally, starting with non-critical workspaces and expanding as confidence grows. If your Infrastructure as Code management platform treats each engine as a separate silo, you're managing two parallel systems during the transition. If the platform abstracts execution details behind a unified orchestration layer, the migration becomes a configuration change, not an architectural overhaul.
IaC Orchestration Beyond Engine Selection
The hard problems in infrastructure management aren't technical; they're organizational. How do you ensure that 40 engineers across six teams follow the same approval process for production changes? How do you enforce cost policies without blocking legitimate deployments? How do you maintain audit trails that satisfy compliance requirements without turning every infrastructure change into a bureaucratic ordeal?
IaC orchestration platforms solve these problems by decoupling policy from execution. Instead of embedding governance rules in CI/CD pipelines—where they're invisible, untestable, and easy to bypass—you define them once at the platform level. Instead of writing custom scripts to sequence Terragrunt dependencies, you describe the dependency graph declaratively and let the platform handle execution order. Instead of building bespoke drift detection logic, you configure detection schedules and let the platform surface meaningful deltas.
This approach doesn't eliminate complexity. It consolidates complexity into a layer designed to manage it. Your IaC configurations remain simple: modules that define resources, Terragrunt wrappers that eliminate boilerplate, workspace configurations that specify execution context. The orchestration platform handles everything else: state locking, policy evaluation, approval workflows, audit logging, drift remediation.
Harness IaCM: Orchestration for Multi-IaC Environments
Harness Infrastructure as Code Management approaches these challenges by treating the execution engine as a deployment detail, not an architectural constraint. Whether you're running OpenTofu, Terraform, or Terragrunt, the orchestration layer remains consistent: standardized pipelines for plan generation and apply operations, unified policy enforcement across all workspaces, centralized drift detection that surfaces actionable insights.
Native Terragrunt support means dependency graphs defined in Terragrunt configurations are understood and respected during execution. You don't need custom scripts to sequence deployments across modules or AWS accounts. The platform interprets dependencies and orchestrates execution accordingly, applying changes in the correct order while maintaining state consistency.
The Module and Provider Registry provides a centralized source of truth for infrastructure components, whether they're Terraform modules, OpenTofu modules, or Terragrunt configurations. Variable Sets and Workspace Templates eliminate configuration duplication, letting you define environment-specific values once and reference them across workspaces. Default plan and apply pipelines ensure consistent execution patterns without requiring custom CI/CD configurations for each workspace.
Policy enforcement happens before execution, not after. Whether you're using Terraform, OpenTofu, or Terragrunt, policies evaluate against the generated plan before any infrastructure changes occur. Drift detection runs automatically, comparing deployed infrastructure against IaC definitions and surfacing discrepancies through a unified interface. Audit trails capture every state change, policy evaluation, and approval decision, providing the compliance evidence required in regulated environments.
For teams managing infrastructure across multiple clouds, regions, or execution engines, Harness IaCM provides the orchestration layer that makes polyglot IaC environments manageable. The platform doesn't force you to standardize on a single tool. It provides governance, visibility, and workflow consistency regardless of which engine interprets your configurations.
Governance Enables Velocity in Multi-IaC Environments
The promise of Infrastructure as Code—reproducible deployments, version-controlled infrastructure, collaborative development—only materializes when you have consistent orchestration across execution engines. Running Terraform in one pipeline, OpenTofu in another, and Terragrunt through a shell script doesn't scale. It creates workflow fragmentation that defeats governance and slows teams down.
Effective Infrastructure as Code management platforms abstract execution details behind unified workflows. They treat Terragrunt as a first-class orchestration primitive, not an afterthought. They provide native support for OpenTofu alongside Terraform, recognizing that organizations migrate gradually, not overnight. Most importantly, they enforce policy, detect drift, and maintain audit trails consistently across all workspaces, regardless of which engine runs the actual infrastructure changes.
The technical lesson: orchestration complexity belongs in platforms designed to manage it, not scattered across custom scripts and fragmented CI/CD pipelines. The operational lesson: governance doesn't slow teams down when it's embedded in the workflow rather than bolted on afterward. Multi-IaC environments are manageable when you have the right orchestration layer. Without it, you're just running tools in parallel and hoping they don't conflict.
A visual comic story exploring the challenges of fragmented artifact management and how a unified, secure approach brings clarity, control, and trust to modern software delivery.
Shibam Dhar
April 29, 2026
Time to read
Introducing the Artifact Registry Comic Book. Enjoy the read? Subscribe at the bottom so you don’t miss the next issue.
Why GitOps for MongoDB Matters: A Case for Harness DB DevOps
Learn how GitOps for MongoDB brings version control, pull requests, and pipeline automation to database changes with Harness DB DevOps.
Jesse Wang
April 28, 2026
Time to read
The Gap: Modern DevOps Stops at the Database
Most development teams today build everything around Git, and deploy with GitOps principles.
Code sits in version controlled environments, changes go through PRs, and deployments are handled through modern CI/CD. That part is pretty standard at this point, especially when using a modern DevOps platform like Harness.
MongoDB fits into that developer world and workflow pretty naturally. Data is stored in documents that look a lot like JSON, the format many developers already use in application code and APIs. Under the hood, MongoDB stores those documents as BSON, which is essentially a binary form of JSON that supports additional data types like dates, object IDs, and binary data. That means developers get a familiar model to work with, while MongoDB gets a format that is efficient for storing and querying application data.
Looks just like JSON, with native types like ObjectId and dates powered by BSON.
The tradeoff is that structure isn’t always defined upfront. Schemas change over time, and not always in a clean or consistent way.
Collections can contain documents with different shapes. Index changes can directly impact performance. These aren’t problems on their own, but they require discipline to manage safely.
MongoDB changes are often handled outside the standard development workflow, whether that’s by developers, platform teams, or database teams.
Teams rely on application-level updates or one-off scripts to backfill data, modify structures, or create indexes. These approaches work, but they’re not always consistently versioned in Git. Execution can vary across environments, and review or validation is often informal.
The result is limited visibility into what changed, when it changed, and how it was applied. Over time, that leads to inconsistencies between environments and increased risk during deployment.
Flexibility is powerful, but without proper controls it introduces risk.
To solve this, teams need to bring MongoDB changes into the same workflow they already trust for application code: Git-driven, reviewable, and automated.
What GitOps for MongoDB Actually Looks Like
GitOps for MongoDB isn’t about changing how Mongo works. It’s about changing how changes are managed.
Instead of handling updates through scripts or application logic alone, database changes are treated like application code. Index creation, schema validation rules, and migration scripts are all defined in Git and tracked over time. This includes MongoDB’s native schema validation rules, which can be versioned and applied consistently across environments.
Changes need to go through pull requests, just like any other code change. This allows developers, platform teams, and DBAs to review what’s being modified before anything runs in an environment.
From there, pipelines handle the validation and deployment. Changes are applied consistently across environments, rather than being run manually and potentially differently each time.
In practice, this means a new field, an index, or a backfill isn’t just a script someone runs once. It’s a versioned change that can be reviewed, tested, and repeated.
This isn’t about forcing rigid schemas onto MongoDB. It’s about making changes visible, consistent, and easier to manage as systems grow.
Harness DB DevOps provides the structure to do this. With Harness, we define changes as changesets, store them in Git, and deploy them through pipelines with built-in validation and policy checks.
To demonstrate how this works, we will walk through a practical MongoDB change from start to finish.
Example: Managing a MongoDB Change End-to-End
Here’s a simple example: A team needs to add a new userPreferences field to the users collection and create an index to support a new query.
Instead of writing a script and running it manually, we define the change and commit it to Git.
1. Define the change in Git A developer creates the update as a changeset. That includes the logic to add or backfill the new field, along with the createIndex operation needed for performance. The change is committed alongside application code, like any other update.
2. Open a pull request From there, the change goes through a pull request. Other developers or DBAs can review what’s being changed before anything runs. If something looks off, it gets caught here instead of in production.
3. Let the pipeline take over Once the change is approved, the pipeline takes over.
The Pipeline
Before anything gets applied, the change is validated and previewed against the target environment. This helps catch issues early, whether it’s a conflict, a bad query pattern, or something that could impact performance.
This is especially important for heavy operations like index creation on massive collections, where resource contention and performance degradation are real risks. Instead of running those changes manually, pipelines can enforce safe rollout strategies like rolling index creations across replica sets, without manual intervention.
Policies are enforced as part of that same process, with required approvals, environment rules, and other guardrails checked automatically so teams aren’t relying on someone to manually verify every step.
Once everything passes, the change is deployed through the pipeline and applied consistently across environments, moving from dev to staging to production in a controlled way. No one is logging into a database to run scripts by hand.
Now, everything is tracked. You can see what was applied, where it was deployed, when it happened, and who approved it, with a full history available if something needs to be reviewed or rolled back later.
Sound familiar? This workflow should sound a lot like application delivery, where changes are versioned, reviewed, validated before deployment, and visible after.
From Manual Oversight to Guardrails
Traditionally, database changes have been tightly controlled by DBAs. They review scripts, approve changes, and sometimes execute them manually in each environment. That model helps reduce risk, but it doesn’t scale as teams grow and release more frequently.
With a GitOps approach, that control doesn’t disappear, it moves earlier in the process.
Instead of reviewing every individual change, database teams define policies and standards up front. Those rules are then enforced automatically through pipelines. Every change must pass the same checks before it reaches an environment, without requiring manual intervention each time.
In practice, this means:
Required approvals are enforced automatically
Deployment rules are validated before execution
Production environments can have stricter controls than lower environments
Policies are applied consistently across every change
The role of the database team evolves from gatekeeper to system designer. Rather than being involved in every deployment, they define the guardrails that ensure every deployment is safe.
Developers still move quickly, but now within a controlled, repeatable system.
Faster delivery Developers don’t wait on manual reviews or execution. Changes move through the same workflow as application code.
Consistent environments Changes are applied the same way every time, reducing drift between dev, staging, and production.
More predictable deployments Validation and policy checks happen before execution, catching issues earlier and reducing surprises.
Built-in auditability Every change is versioned, reviewed, and tracked, with full visibility into what changed, when, and why.
MongoDB's flexibility doesn't eliminate the need for structure - it just shifts the responsibility for maintaining consistency from the database itself to your development processes.
If your application is managed through Git, your database should be too.
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