GitLab Feature Flags: A Comprehensive Guide to Safer Deployments

Understanding GitLab Feature Flags

A feature flag (also known as a feature toggle) is a powerful mechanism that allows you to enable or disable specific software features at runtime. By doing so, development teams can deploy new functionality continuously without exposing incomplete or high-risk changes to all users. Instead of tying release availability to code merges or deployments, a feature flag lets you toggle your features on or off for specific environments, user cohorts, or time frames.

In GitLab, Feature Flags are tightly integrated into the development workflow. Once configured, they allow developers to manage feature visibility through their GitLab CI/CD pipelines or application configuration. This means less friction when introducing new features, and more flexibility in responding quickly to issues if something goes wrong after deployment.

Key points:

• Feature flags decouple feature release from code deployment.
• GitLab’s native Feature Flags system simplifies development workflows.
• Continuous delivery is supported with less risk and more control.

Why Use GitLab Feature Flags

Software delivery can be complex and risky if every new commit automatically exposes new features to all users. By leveraging GitLab Feature Flags, you can address several core challenges:

1. Risk Reduction: Feature flags let you release features progressively, testing them internally or with a small subset of users first. If a bug surfaces, you can simply disable the flag instead of rolling back an entire deployment.
2. Faster Iterations: Because teams do not have to wait for a full release cycle, development and testing can happen in parallel. You can gather feedback early and often, improving the final product’s quality.
3. Simplified A/B Testing: You can toggle different versions of a feature for different user segments, collecting performance and engagement data in real-time. This leads to more data-driven decision-making.
4. Compliance and Governance: GitLab’s integrated approach to source code management (SCM) and CI/CD means you have a unified audit trail. You gain clarity on who toggled what feature and when, reducing compliance headaches and improving governance.

Setting Up Feature Flags in GitLab

Implementing GitLab Feature Flags involves both development and operational steps:

1. Enable the Feature Flag UI: Within the GitLab project’s settings, you’ll need to ensure the “Feature Flags” menu is visible. Depending on your GitLab plan, this may require specific permissions or configurations.
2. Create a New Flag: Provide a descriptive name, status, and strategy. You can define who should see the feature (e.g., a user cohort, an environment, or a specific namespace).
3. Update Your Application Code: Most commonly, you’ll integrate your application with GitLab’s Feature Flags through an SDK or an API call. This means adding conditional checks in your code to turn features on or off based on the feature flag state.
4. Manage Flag States: Use GitLab’s UI or its API to manage toggles in real-time. You can do canary releases, percentage rollouts (e.g., 10% of the traffic sees the new feature), or any other custom rollout strategy that suits your needs.
5. Monitor & Iterate: Once a feature is flagged and partially rolled out, monitor performance metrics and user feedback. If issues arise, you can revert quickly by toggling off the feature. If everything runs smoothly, incrementally expand the user base.

Pro Tip: Consider implementing guardrails in your CI/CD pipeline so that only certain roles or automated processes can toggle critical flags. This prevents unintended changes in production environments.

Best Practices for GitLab Feature Flags

To get the most out of GitLab Feature Flags, here are some proven best practices:

1. Use Descriptive Naming Conventions
   Name your flags in a way that clearly indicates their purpose. For instance, enable_redesigned_checkout_flow is more descriptive than flag123.
2. Sunset Old Flags
   Once a feature is stable and rolled out to all users, remove or archive the associated flag. Keeping flags that are no longer needed adds complexity to your codebase and can introduce confusion.
3. Automate Where Possible
   Automate the creation, management, and removal of flags with GitLab CI/CD pipelines. Automation reduces human error and enforces consistent flag-handling practices.
4. Monitor Performance
   Implement robust monitoring around flagged features. Whether you’re testing performance, functionality, or user experience, real-time observability helps you catch anomalies quickly.
5. Plan for Rollback
   If something goes awry, you should have a clear rollback strategy. With GitLab Feature Flags, this is as simple as turning off the toggle. However, be prepared with communication protocols so relevant stakeholders are informed immediately.

Leveraging Feature Flags for Progressive Delivery

Progressive Delivery is an approach that marries continuous integration and continuous delivery with advanced release strategies like canary deployments, blue-green deployments, and feature flags. By combining feature flags with progressive delivery techniques, teams can better control the blast radius of new changes:

• Canary Releases: Limit new features to a small subset of users initially. If no major issues arise, gradually increase the percentage.
• A/B Testing: Show Feature A to half of your users and Feature B to the other half, then measure and compare results.
• Gradual Rollouts: Roll out features in stages—first to internal testing, then to beta testers, and finally to all users.

With GitLab, you can automate these progressive delivery tactics using your existing CI/CD pipelines, ensuring each deployment is thoroughly validated before it scales to the entire user base.

Integrating GitLab Feature Flags with Other Tools

Feature flags don’t exist in a vacuum. Integrations with other tools can amplify their effectiveness:

1. Issue Tracking
   Link feature flags to issues in GitLab or Jira. This allows you to map feature rollout (or rollback) to specific feature requests or bug reports, closing the loop between development and issue resolution.
2. Monitoring and Observability
   Integrate with tools like Prometheus, Grafana, or Datadog to monitor metrics in real-time. This insight helps you decide whether to continue rolling out a feature or revert it.
3. Collaboration and ChatOps
   Connect GitLab Feature Flags to chat platforms like Slack or Microsoft Teams. Team members can monitor or even toggle flags directly from chat, accelerating response times and promoting collaboration.
4. Security Scans
   Before toggling a new feature live, ensure security scans run automatically. GitLab’s built-in scanning capabilities or third-party solutions help detect vulnerabilities before a feature reaches production.
5. Developer Portals
   If your organization uses an Internal Developer Portal (IDP), incorporate feature flag management within that portal. Harness’s Internal Developer Portal, for example, provides a central place to manage software delivery services, including feature flags, across all teams.

How Harness Feature Management & Experimentation Elevates Your Release Strategy

While GitLab’s built-in Feature Flags are a solid choice for many teams, there are scenarios where you need more advanced capabilities, deeper analytics, or a platform that unifies all aspects of software delivery.

Harness Feature Management & Experimentation, part of the Harness AI-Native Software Delivery Platform™, connects feature flags to critical impact data, ensuring you always know if your software changes are making things better or worse. Here’s how Harness can enhance your feature flag strategy:

1. Comprehensive Platform
   Harness offers an integrated suite of solutions—Continuous Integration, Continuous Delivery, Infrastructure as Code Management, Chaos Engineering, Service Reliability Management, and more. Managing feature flags becomes part of a broader, AI-powered ecosystem.
2. Deeper Insights
   Harness Feature Management & Experimentation includes real-time analytics that automatically correlate user behavior and system performance with feature toggles. This data-driven approach helps you make better rollout decisions.
3. User Targeting at Scale
   Target specific users, teams, or geographies with ease. Dynamic targeting rules and built-in performance checks let you segment user bases without writing complex scripts.
4. Automated Guardrails
   Harness AI can detect abnormal system metrics or user engagement patterns linked to a specific flag. If something spikes or dips, you’ll receive proactive alerts and can automate the rollback if needed.
5. Unified Governance
   With Harness, you gain single-pane-of-glass visibility for every aspect of your software delivery lifecycle, from code commits to production flags. This unified governance helps ensure compliance, security, and auditability.

By combining GitLab’s SCM and CI/CD strengths with Harness’s advanced Feature Management & Experimentation, teams can design a modern, robust pipeline that maximizes release velocity while minimizing risk—truly achieving engineering excellence and improved developer experience.

In Summary

GitLab Feature Flags simplify the process of shipping code in smaller, safer increments. They allow teams to toggle individual features on or off, making progressive delivery, A/B testing, and rapid rollbacks far more streamlined. By following best practices such as using descriptive names, automating rollout strategies, and actively monitoring performance, you can harness the full potential of feature flags.

For organizations that need advanced targeting, real-time analytics, and seamless integration across the entire software development lifecycle, Harness Feature Management & Experimentation provides a holistic, AI-native solution. It ensures you have continuous insight into the performance and impact of every feature—and the ability to pivot quickly if needed.

FAQ

What are GitLab Feature Flags used for?

GitLab Feature Flags allow you to enable or disable specific features at runtime. This helps teams roll out changes gradually, perform canary testing, and quickly rollback if necessary, reducing the risk associated with deploying new or experimental features.

How do I set up a feature flag in GitLab?

To set up a GitLab Feature Flag, go to your project settings and enable the feature flag menu (if needed). Create a new flag by specifying its name, status, and rollout strategy. Then, integrate it into your application via GitLab’s APIs or SDKs, inserting conditional checks that toggle features on or off based on the flag’s state.

What is the difference between GitLab Feature Flags and Harness Feature Management & Experimentation?

GitLab’s built-in Feature Flags provide a way to toggle features within the GitLab environment, focusing on incremental releases. Harness Feature Management & Experimentation, part of an AI-native software delivery platform, offers deeper analytics, automated guardrails, and a unified governance model across the entire software delivery lifecycle.

Can I use GitLab Feature Flags for A/B testing?

Yes. GitLab Feature Flags enable you to segment user populations and roll out features to different groups. This provides a foundation for A/B testing by comparing metrics (like performance or user engagement) across various user cohorts.

What are some best practices for managing feature flags?

Follow descriptive naming conventions, remove old flags once features are fully rolled out, automate flag creation and removal in your CI/CD pipeline, monitor flagged features in real-time, and plan for quick rollbacks to minimize disruptions.

Do I need a specific GitLab plan for using Feature Flags?

GitLab’s Feature Flags are typically available in higher-tier plans or self-managed installations with the right licensing. Check GitLab’s documentation or your account details to confirm availability and required permissions.

Why should I consider Harness for feature flag management?

Harness provides an AI-native platform that spans Continuous Delivery, Continuous Integration, Feature Management & Experimentation, Chaos Engineering, and more. Its analytics, smart guardrails, and unified governance capabilities help you gain deeper visibility into feature performance, automate rollback strategies, and streamline compliance—further reducing risk while accelerating delivery.