Mean Time Between Failures in CI/CD: How To Use It Without Slowing Down Delivery | Harness Glossary

At Harness, we've seen a lot of teams struggle with MTBF. Some use it well, while others don't realize it and end up releasing things more slowly and with more risk. Our platform helps engineering teams keep track of MTBF and other delivery metrics. It does this by using automation, canary deployments, and AI-powered verification to make things more reliable without slowing them down. It doesn't matter if you're using Harness or not; it's important to know how to use MTBF correctly.

What does "Mean Time Between Failures" mean, anyway?

MTBF, or Mean Time Between Failures, is a measure of reliability that shows how long a system can run before it breaks down again. In simple terms, how long does your service usually last before something breaks and needs to be fixed?

This is the formula:

MTBF = Total Uptime Over A Period ÷ Number Of Failures In That Period

If your service runs for 1,000 hours over a quarter and you have five production incidents that stop it from working normally, your MTBF is 200 hours.

On paper, it's not too hard. But this is where teams get stuck: optimizing for MTBF alone can actually take you the wrong way.

Why Mean Time Between Failures Matters For Software Teams

MTBF has been around for a long time in the world of hardware. It's used to plan maintenance and cut down on downtime. It does the same thing in software, but there's an important difference: you also need to think about how your deployments, changes, and delivery methods affect that failure rate.

A higher MTBF is usually a good thing for engineering teams that ship software today:

• Fewer problems that customers see during a certain time
• More predictable on-call rotations (your engineers will be grateful)
• A stronger case to the leaders that frequent deployments are actually safer

Here's the deal: you can really compare how reliable something is before and after you make changes to your stack or process if you can figure out how often failures happen. Are you going to use continuous delivery? Adding flags for features? Do you need to refactor a critical service? MTBF gives you a starting point to compare to.

How To Measure MTBF In A CI/CD Environment

The good news is that you don't need a fancy continuous delivery setup to start keeping track of MTBF. Before you roll out CD, it's smart to measure it so you have a real baseline to compare it to.

What you'll need:

1. Observability: Logs, metrics, and traces that let you know when a production failure has happened, not just when someone else notices it.
2. Incident Management: A consistent way to keep track of incidents that are failures, including when they start and end
3. Service-Level Tracking: Don't add up all the MTBFs for everything. That just hides where the real danger is. Track it for each service or app.

The basic steps:

1. Explain what "failure" means for each service. It could be SLO violations or P1/P2 incidents. Whatever makes sense for your situation.
2. Add up the total uptime for that service over a set period of time, like a month or a quarter.
3. Count the number of failures that meet the requirements in that same time frame.
4. Use the MTBF formula and keep an eye on the number over time.

Once you have CI/CD set up, link those incident records to certain changes and pipelines. That's when the metric really starts to work: you can see exactly how MTBF changed before and after important delivery changes.

MTBF, Change Failure Rate, And DORA Metrics

Let’s be honest now: MTBF alone isn't the best way to tell how well your engineering team is really doing. The failure rate of changes is usually more informative.

But they do work together. This is how the metrics work:

• Mean Time Between Failures (MTBF): How often your system breaks down
• Change Failure Rate: How often a change (typically a deployment) leads to a failure
• Mean Time To Recovery (MTTR): How long it takes to fix something when it breaks
• Lead Time: The time from code commit to production deployment. The lead time from feature request to feature delivery is also important, but DORA’s DevOps focus measures just the pipeline. 
• Deployment Frequency: The frequency of production deployment. 

With Change Failure Rate and Deployment Frequency, teams have the portion of MTBF contributed by change. Typically, this is the majority. Any failures caused by other sources, such as power outages or dependency failures, aren’t accounted for in the DORA metrics.

If you only look at MTBF, you might "optimize" by deploying less often. Failures are farther apart on paper. But what about in real life? You're sending out bigger, more dangerous changes. Every release is more likely to lead to an incident. Your net risk goes up, not down. Combined, the DORA metrics reward both speed of increased frequency and reduced risk - fewer failures with reduced impact.

This is what a healthier approach looks like:

• Improve your tests, automation, and deployment plans to lower your change failure rate and MTTR.
• Keep the same number of deployments or increase it so that you ship smaller, safer batches.
• Keep an eye on MTBF to make sure that failures don't happen more often as you speed up.

You can think of MTBF as a complement to the DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recovery) that make up a metric ecosystem. It adds to them; it doesn't take their place. 

With Harness's Software Engineering Insights module, you can keep track of all of these at once, so you won't be in the dark about any one area.

Common Pitfalls When Using MTBF With CI/CD

We've seen teams that are new to MTBF make the same mistakes over and over:

• Improving MTBF on its own. "Our Mean Time Between Failures is great. We only fail a few times a year!" To keep it that way, let's deploy less often. That's how you get huge weekend releases that keep everyone up all night.
• Not paying attention to how often things change. You shouldn't judge all services in the same way. A batch job that runs every three months and a checkout service that is always up to date have very different levels of risk. Set the right MTBF goals based on how often each service changes.
• Permanent slowdowns after events. When something goes wrong, it's easy to want to add manual gates and approval chains. But those should only be temporary guardrails while you work on your automation and tests, not permanent roadblocks. In fact, some research suggests that manual checks can increase change failure rates. Be cautious with these techniques and seek to eliminate them with automation.

The goal isn't just to have a high MTBF. It's high MTBF and high deployment frequency, with a low change failure rate and quick recovery times.

Improving MTBF With Modern Continuous Delivery

Instead of trading speed for stability, you want to lower risk so you can change more often without worrying about it.

With modern CD methods and tools like Harness, you have the building blocks you need to make this happen.

Important patterns that make a difference:

• Automated Deployments: Get rid of those one-time SSH sessions and scripts and use pipelines that can be used over and over again. Harness lets you deploy without scripts on Kubernetes, VMs, and other types of systems. Fewer steps for people to take means fewer chances for small mistakes to lead to problems.
• Progressive Delivery: The canary and blue-green strategies limit your blast radius by moving a small group of traffic first, keeping an eye on the metrics, and then promoting when everything looks good. Harness makes it easy to set up canary deployments by automatically shifting traffic and checking for errors. No complicated scripting is needed.
• Automated Verification and Rollback: This is where things get interesting. Harness uses AI to look at logs and APM telemetry during and after deployments to find problems that could mean a failure. If something doesn't look right, it quickly rolls back before a blip turns into a long outage. To make this work, the platform works with the observability tools you already have.
• Guardrails, Not Blockers: Open Policy Agent (OPA), approval workflows, and freeze windows are all tools that can be used strategically to enforce governance without stopping teams in their tracks.

These practices work together to move MTBF in the right direction, and they also let you shorten lead time and increase the number of deployments.

Balancing Speed And Reliability In Practice

Let us consider two different versions of the same app.

• Scenario one: To do a deployment, an operator has to log into a number of servers by hand and run a few commands on each one. There could be 5 to 10 commands on 5 to 10 hosts. That's a lot of chances for a typo, a missed step, or a moment of "wait, did I already run that?" to cause a problem in production. MTBF goes down because the process itself makes mistakes more likely.
• The second scenario: A CD pipeline does the same job. Automated: building, testing, deploying, the canary phase, observability checks, and possible rollback. People care more about what changed and why than about which command to run next. It becomes easy to guess how the change will work.

In the second case, you get both a higher MTBF and a lower change failure rate at the same time. That's the point.

Continuous Delivery and CI features in Harness are built around this idea: automate the risky parts so your engineers can focus on design, debugging, and experimentation instead of deployment work.

Frequently Asked Questions About Mean Time Between Failures (MTBF)

Before you put MTBF on your engineering dashboards, let's answer some common questions.

How long should software services last?

To be honest? There is no one answer that works for everyone. The number of acceptable failures depends on your field, your users' expectations, and how your system is set up. A healthcare system and a consumer app will have very different levels of tolerance. Instead of chasing some random benchmark, work on getting your own MTBF to go in the right direction.

Should we slow down deployments to make MTBF longer?

No, almost always. Slowing down by making more changes in batches usually increases the chance that any one deployment will fail, even if MTBF looks better for a while. In the long run, smaller, more frequent releases with good automation are safer.

What makes MTBF different from MTTR and the change failure rate?

Here's a quick summary:

• MTBF shows you how often things go wrong
• MTTR tells you how long it takes to get back on track when they do.
• The "change failure rate" tells you how often deployments lead to those failures.

To really understand the trade-offs between speed and reliability, you need all three of these things, plus deployment frequency and lead time.

Use MTBF To Prove Reliability As You Speed Up

Mean Time Between Failures isn't a magic number, and it shouldn't make you slow down your delivery to a crawl. When used correctly, it becomes a way to show that your investment in CI/CD, automation, and progressive delivery is paying off in terms of real gains in reliability.

Keep an eye on MTBF along with the change failure rate, MTTR, and deployment frequency. Use continuous delivery tools and methods to get them all moving in the right direction. That's how you can sleep well and ship things quickly at the same time.

Are you ready to see how this works in real life? Book a demo with Harness to find out how automated deployments, canary strategies, and AI-powered verification can help you ship faster and improve your MTBF.