Deployment Frequency as a Symptom, Not a Goal

Deployment frequency has become one of the most quoted metrics in modern software delivery.

It shows up in DORA reports, OKRs, and conference talks. Teams talk about "deploying to production dozens of times a day" as a badge of honour.

But there is a subtle trap:

Deployment frequency is a symptom of how healthy your system and team practices are, not a goal in itself.

When we turn it into a target without understanding what sits underneath, we risk optimising for the wrong thing.

What Deployment Frequency Really Tells You

At its best, deployment frequency is a proxy for several deeper properties of your system:

• Batch size – smaller, more frequent changes.
• Release friction – how hard it is to move a change from commit to production.
• Automation quality – how much of the path is scripted, repeatable, and reliable.
• Team confidence – how safe people feel about shipping.

High frequency is usually a sign that:

• changes are small enough to reason about,
• the pipeline is fast enough to keep feedback tight,
• and teams trust the system not to punish them for moving.

Low frequency can indicate the opposite – or simply that the domain does not need constant change. Context matters.

The Anti-Pattern: Chasing Deploys per Day

Problems start when organisations treat "deploys per day" as a top-level goal.

Common symptoms:

• Artificially splitting changes to increase the count.
• Rushing code through minimal review to hit targets.
• Shipping low-value tweaks just to "move the needle".

In these cases, the metric stops reflecting system health and starts reflecting gaming behaviour.

The risk is that we:

• ignore architectural bottlenecks because the numbers look good,
• create more incidents due to rushed or fragmented changes,
• and lose sight of why we wanted more frequent deployments in the first place.

Deployment frequency is useful precisely because it emerges from good practices. Forcing it directly can distort those practices.

Looking Under the Symptom

Instead of asking, "How do we increase deployment frequency?" a more useful question is:

"What is our current deployment frequency telling us about how we build and operate systems?"

Some examples:

• If changes accumulate into large, infrequent releases, that may point to:

  • slow or flaky test suites,
  • manual approval steps that are hard to schedule,
  • coupling between services that makes small changes risky.

• If teams deploy frequently but still feel anxious, that may indicate:

  • weak rollback strategies,
  • limited observability,
  • unclear ownership when something goes wrong.

The metric is a starting point for an architectural and process conversation, not the end of one.

Architectural and Platform Levers Behind Frequency

Several architectural and platform choices show up directly in deployment frequency.

1. Batch Size and Service Boundaries

• Well-chosen boundaries make it easier to release small, independent changes.
• Tightly coupled services or shared databases force larger, riskier deployments.

2. Pipeline Design and Automation

• Fast, reliable CI and CD stages encourage frequent, safe releases.
• Long-running, fragile pipelines incentivise batching work to "save" runs.

3. Environments and Release Strategies

• Techniques like blue-green, canary, and feature flags decouple deployment from exposure.
• When deployment and release are tightly coupled, teams delay changes until they feel "big enough" to justify the risk.

Working on these factors changes deployment frequency as a side effect of making the system healthier.

Using Frequency as a Feedback Signal

If we treat deployment frequency as a feedback signal, we can:

• track it over time alongside lead time, change failure rate, and MTTR,
• correlate drops or spikes with architectural changes or organisational shifts,
• use it to validate whether platform and process improvements are paying off.

For example:

• Introducing a new internal developer platform should, over time, reduce the effort to release. If frequency does not improve at all, that is a signal worth investigating.
• Refactoring a monolith into services might initially reduce frequency as teams adjust. Longer term, we would expect easier incremental releases if boundaries are well-chosen.

The key is to treat the number as a conversation starter rather than a scoreboard.

How We Talk About This with Teams at Fentrex

When we work with teams on DevOps and platform changes, we rarely start with "you should deploy X times per day".

Instead, we ask:

• How big are your typical changes?
• How hard is it to get a safe change into production today?
• Where do you feel the most friction or anxiety in the path to production?

From there, we look at:

• architecture (coupling, boundaries, dependencies),
• pipelines (speed, reliability, manual steps),
• and team practices (testing, reviews, on-call, incident response).

Deployment frequency then becomes one of several signals we watch as we make changes.

Questions to Ask About Your Own Frequency

If you are looking at your current deployment frequency and wondering what to do with it, a few questions can help:

• Is our current frequency deliberate and appropriate for our domain, or just a side effect of friction?
• What would we have to change in architecture or pipelines to make smaller, safer releases the natural choice?
• When frequency changes, can we explain why – in terms of system design, process, or organisation?

Using deployment frequency this way turns it back into what it was meant to be: a useful symptom of how well the system supports the way you want to deliver, rather than a goal to chase on its own.