There’s been a growing emphasis in the cloud native community on investing in tools that improve developer experience. Platform engineering, accompanied with the rise of projects like Backstage, is all about making developers more productive by smoothing out the day-to-day friction in how they build and ship software. The CNCF TAG App Delivery’s Platform Engineering Whitepaper documents how organizations are formalizing this practice, and with that formalization comes increasing pressure to demonstrate that the investment pays off.
Whether you’re adopting a paid product or a free open source project, developer tools always come with a cost. Teams need to spend time evaluating them, integrating them into existing workflows, and maintaining them over time. Without a clear way to think about ROI, it can be difficult to justify these investments beyond a general sense that they “improve developer experience.”
As someone who’s worked in the developer tools space for over four years now, this is a question I’ve had to answer many times with customers. In this post, I’ll start by looking at some of the common ways teams measure the ROI of developer tools, and then cover how those approaches apply differently depending on team size, and which ones tend to be useful at each stage of growth.
Tools for measuring ROI
Internal surveys and feedback
Internal surveys and direct feedback are often the simplest way for teams to understand whether a developer tool is providing real value or not. They’re easy to run and don’t require you to set up complex systems to gather or study a lot of data.
This approach relies primarily on qualitative feedback, which is often undervalued when it comes to ROI calculations compared to quantitative metrics. Numbers feel objective and easier to report on, so they tend to win by default. But when it comes to developer tools, qualitative feedback is often the fastest way to surface real friction. Developers are usually very aware of what slows them down and what feels unnecessary in their day-to-day work.
The key here is to ask specific questions that focus on points of friction. For example:
- What’s the slowest or most painful part of your development workflow?
- What tools or processes do you find yourself actively working around?
- Where do you lose the most time during a typical development cycle?
- Has any recently adopted tool meaningfully reduced your day-to-day friction?
Keeping surveys short and focused is important since long, generic surveys tend to get ignored. It’s also critical that feedback leads to visible action because if developers repeatedly call out the same issues and nothing changes, surveys will stop being taken seriously.
Internal surveys won’t give you a precise ROI number, but they can quickly tell you whether a dev tool is actually making things easier or just adding another layer of complexity.
DORA metrics
DORA metrics are one of the most widely accepted ways to reason about engineering effectiveness at scale. They come out of years of research from the DevOps Research and Assessment group and focus on how reliably and quickly teams can deliver software.
The four metrics are:
- Deployment frequency: How often you deploy to production.
- Lead time for changes: How long it takes to go from a code change being started to it running in production.
- Change failure rate: What percentage of deployments cause a production issue.
- Mean time to recovery (MTTR): How long it takes to restore service when something goes wrong.
As you can probably gather from just reading their definitions, measuring these can be a lot tougher than simple internal surveys. Collecting DORA metrics reliably requires instrumenting your deployment pipeline to capture events: when a change starts, when it reaches production, and when an incident is opened and closed. OpenTelemetry, a CNCF graduated project, provides a vendor-neutral standard for this kind of telemetry. Many teams use it to instrument CI/CD pipelines and export deployment spans to observability backends for DORA tracking.
If your team already uses Argo CD or Tekton for GitOps and CI/CD, you already have a natural source of deployment events. Deployment frequency and lead time can be derived directly from sync events and pipeline runs without building a separate measurement layer. Similarly, Prometheus can be used to track change failure rate and MTTR as alerting on SLO breaches gives you a timestamp for when something broke, and resolved alerts give you time-to-recovery.
If you’re looking for a quantitative way to measure the ROI of a particular dev tool, comparing DORA metrics before and after adoption of the tool is one of your best bets.
That said, it’s important to remember that DORA metrics describe outcomes, not causes. They can show that lead time has improved or deployments have become more frequent, but if you adopted multiple tools or changed different processes during the same period, they won’t tell you which change actually drove the improvement. It’s also worth noting that the impact of some developer tools on DORA metrics isn’t instant and is only visible over time. For that reason, it’s best to isolate comparisons to a single tool where possible and track results over a sufficiently long period to draw meaningful conclusions.
DORA metrics work best to help validate the answer to questions like: “Did reducing CI time actually shorten lead time?” or “Did improving debugging and rollback tooling reduce MTTR over the last few quarters?” They shouldn’t be the only input for ROI calculations, but when combined with qualitative feedback and cost-based analysis, they provide a strong way to reason about the ROI of developer tools.
Cost-based analysis
Cost-based analysis is often the most straightforward way to reason about the ROI of a developer tool in terms of money. The idea is simple: estimate how much developer time the tool saves, convert that time into money, and compare it against the total cost of adopting and running the tool.
As an example, consider a team where the average cost of an engineer is around $150,000 per year. If a developer tool saves even 30 minutes per engineer per day by reducing CI wait time or environment setup friction, that equates to roughly $700 per developer per month in recovered time. When evaluating a tool that costs $X per developer per month, you can compare those estimated savings against the subscription cost and operational overhead (which can also be estimated based on the time required to set up and maintain the tool) to determine whether the investment makes sense. While this type of calculation relies on assumptions, it can serve as a useful directional indicator before investing in more rigorous measurement.
For teams running workloads on Kubernetes, OpenCost provides a standardized way to measure the infrastructure cost of running those workloads. This is especially relevant when evaluating tools that affect cluster utilization: a tool that eliminates long-running ephemeral test environments, for example, has a direct, measurable infrastructure cost reduction that OpenCost can surface alongside the engineering time savings.
Doing this sort of back-of-the-envelope calculation won’t give you a precise ROI, but it’s often enough to tell whether a developer tool is worth what it costs. If the numbers only work under very optimistic assumptions, that’s a red flag. If they work comfortably even with conservative estimates, you likely have a strong case before you look at more detailed metrics.
You do need to acknowledge, though, that doing this sort of cost analysis depends on assumptions. Time saved is difficult to measure precisely, and it’s easy to present numbers that look more certain than they really are. For that reason, it is best to treat cost analysis as a preliminary check to see if the cost of a tool is worth it or not, rather than as a precise ROI calculation.
What works best at each size
Some of the approaches we discussed above work better than others, depending on the size of the company you’re in. As organizations grow, the people who sign the check have less visibility into how the tool they’re buying is actually being used, and the kinds of signals they need to make decisions change. Let’s now see which approaches tend to work best at different stages, along with some other general tips for each team size.
For small teams (~50 people)
For small teams, trying to formally measure ROI is often more work than it’s worth. At this size, you still have direct visibility into how fast things are being shipped, what’s breaking, and where time is being lost. You can talk to engineers, track the progress of issues, and understand pretty quickly whether things are moving in the right direction or not. Because of this, simple methods like internal surveys and feedback calls are the best indicators of ROI. If CI is slow, local development is painful, or deployments are brittle, you’ll usually see it directly or hear about it. Engineers will complain, velocity will visibly drop, or quality will suffer. You don’t need a productivity score to know something is working or not.
If you try to adopt complex processes, a lot of time will end up being spent gathering data, debating numbers, and adjusting processes, without actually improving outcomes. For small teams, that time is almost always better spent shipping.
For medium sized teams (~50-200 people)
As teams grow, it becomes harder to “just try something and see how it goes.” Reversing a decision about how work gets done is no longer trivial, since adopting a new tool usually means onboarding many teams, updating documentation, and supporting multiple workflows. At this size, you need more confidence that an investment is worth the effort before rolling it out widely.
The most useful approach is usually a combination of qualitative feedback and a small number of concrete signals. Introducing the tool to a few smaller teams and then running internal surveys can be an effective first step. Once you’ve seen results there, rolling it out to the broader organization while setting up ways to calculate more detailed metrics (like DORA) can help you understand whether the adoption scales. Cost analysis also starts to matter at this stage, since the numbers involved are significantly larger.
For large teams (200+ people)
At this scale, the adoption process starts to take weeks if not months, and there’s no easy way to know the benefits at scale beforehand. Collecting meaningful DORA metrics usually requires a broad rollout across teams. At a small POC level, the signal is often too weak or takes too long to surface, which makes the metrics unreliable for early decisions. As a result, decisions before full adoption tend to rely more on experience and informed judgment. Cost-based analysis becomes especially important early on, since the cost of something not working out can be very large.
DORA metrics are most useful here, but only after you’ve already made the investment to roll the tool out internally. With enough teams and deployment volume, improvements are easier to measure and tend to show up more clearly in lead time, deployment frequency, and recovery time. For large organizations, it’s better to have a standardized ROI evaluation process that includes a detailed cost analysis, rather than relying on rough calculations like we did in the section above.
Putting it all together
The “correct” way to measure the ROI of developer tools depends on how much visibility you have into day-to-day work and how costly it is to make and reverse decisions at your current scale. All three approaches we discussed can give you useful signals about whether investing in a tool is worth it or not, but at the end of the day, you still need to use your judgment to decide which approach makes the most sense for your situation and which should be prioritized.
This post was originally published on the MetalBear blog.