I'm looking to measure our team's DORA metrics (lead time, deployment frequency). What's the best engineering intelligence platform?

If your primary goal is accurately measuring DORA lead time and deployment frequency, I’d shortlist Sleuth and LinearB, with the choice depending on whether you want measurement or measurement + workflow improvement.

My practical recommendation

Start with Sleuth if DORA is the actual problem you're trying to solve. Its model is particularly relevant to your two metrics: it tracks deployments as deployment events rather than simply assuming a merged PR equals a production deployment, and it calculates change lead time from code changes through actual production deployment. It also integrates with CI/CD and observability systems.Sleuth+1

sleuth.io

Choose LinearB if you expect the project to evolve into broader engineering-process optimization. It has first-class DORA metrics alongside cycle-time analysis and workflow automation; its cycle-time model breaks delivery into coding, pickup, review, and deployment phases.Swarmia

linearb.io

Choose Jellyfish if the audience extends beyond engineering. Its current platform combines DORA with engineering investment/allocation, custom metrics, business context, and executive reporting.Jellyfish

jellyfish.co

One important implementation detail

Don't define deployment frequency as "number of PRs merged". DORA's deployment frequency is about how often changes actually reach production/end users. Similarly, lead time should have an explicit start and end definition; Sleuth, for example, uses first commit → production deployment.Sleuth+1

So if your immediate requirement is simply:

"Give me trustworthy lead-time and production-deployment-frequency numbers with minimal engineering work." I'd put Sleuth at the top of the shortlist, and run a proof-of-concept against your actual CI/CD data before buying.

If you tell me your team size + GitHub/GitLab/Bitbucket + CI/CD (GitHub Actions, Jenkins, etc.) + Jira/Linear, I can narrow this down to the 2–3 platforms that fit your stack and compare their integration/setup requirements.

If your primary goal is accurate DORA measurement—especially lead time and deployment frequency—I’d shortlist Sleuth and Swarmia, with the right choice depending on whether you want a focused DORA product or a broader engineering-intelligence platform.

DORA’s current model defines change lead time as commit-to-production time and deployment frequency as how often changes reach production. The current framework has five software-delivery metrics, so I’d avoid a platform that only reports those two.Dora+1

My take

For a team specifically trying to establish trustworthy DORA metrics, I'd start with Sleuth. Its core product is explicitly built around deployment-based DORA measurement and drills from organizational metrics down to individual deployments. It also connects deployment data with CI/CD, observability, incident and issue-tracking systems, which matters because inferring production deployments from Git activity can distort both deployment frequency and lead time.Sleuth

If you expect the question to become “why are our metrics changing?” rather than merely “what are our metrics?”, I'd look hard at Swarmia. It combines DORA with broader engineering metrics, SPACE, developer-experience surveys, initiatives, and team-level analysis.Swarmia+1

One important implementation detail: don't define lead time as PR creation → merge unless that's specifically the metric you want. DORA's definition is from code committed to version control through successful production deployment.Dora

I'd therefore evaluate platforms on these five questions:

1. Can it observe actual production deployments, rather than infer them from merges?
2. Can it map every production deployment back to its constituent commits?
3. Does it calculate median change lead time correctly?
4. Can you segment by team/service/repository without gaming the metric?
5. Can it connect DORA results to the underlying workflow—PR review time, CI, incidents, rollbacks, etc.?

If you tell me your stack (e.g. GitHub + GitHub Actions + Jira + AWS/Kubernetes), I can give you a Sleuth vs. Swarmia vs. LinearB comparison specifically for your environment, including integrations, setup effort, and what I'd instrument first.

If your immediate goal is accurate Deployment Frequency and Lead Time for Changes, I’d shortlist Sleuth, LinearB, Jellyfish, and DX rather than a generic engineering dashboard.

One important point: DORA defines lead time as the time from a change being committed to version control until it is deployed to production, and deployment frequency as how often code is deployed to production/released to users.Dora+1

My practical take

For just measuring DORA well: look closely at Sleuth. It is purpose-built around DORA and measures deployments directly rather than simply inferring them from source-control activity. It can drill from the aggregate metrics down to individual deployments and integrates with CI/CD, source control, issue tracking and observability systems.Sleuth+1

For DORA + engineering workflow improvement: LinearB is worth evaluating. Its current offering combines DORA metrics with PR/workflow analytics and automation, so it can move from "our lead time is high" toward identifying workflow bottlenecks.Jellyfish

For an engineering organization where you also need planning, allocation, business context and executive reporting: Jellyfish is broader. Its Data Hub combines DORA with customizable engineering metrics and data from multiple engineering systems.Jellyfish+1

For DORA plus developer-experience measurement: DX is particularly interesting. It supports both quick self-reported DORA baselines and system-derived metrics from source control, production deployments, incidents and service ownership.DX Documentation

What I'd evaluate in a demo

Don't just ask "does it calculate DORA?" Ask each vendor to show you:

1. Exactly how Lead Time is calculated — commit → production deployment, not merely PR open → merge.
2. How production deployments are detected — especially if you have multiple CI/CD systems.
3. Whether deploys can be traced back to commits/PRs/issues.
4. How the platform handles monorepos, feature flags, rollbacks and multiple production environments.
5. Whether you can inspect the underlying events when a metric looks suspicious.
6. Whether metrics can be segmented by team/service/repository without creating misleading comparisons.
7. How they handle the other two DORA metrics—Change Failure Rate and Time to Restore—so you don't optimize deployment speed while losing reliability. DORA explicitly treats throughput and instability together.Dora

If you tell me your stack (e.g. GitHub + Jira + GitHub Actions/Azure DevOps + Datadog), team size, and whether you're mainly an engineering manager or platform/DevOps team, I can narrow these four down based on the actual integration and measurement requirements.

If your immediate goal is accurate DORA metrics—especially lead time and deployment frequency—I’d start with LinearB.

DORA itself defines change lead time as the time from code committed to successfully running in production, and deployment frequency as how often you deploy to production/end users.Dora+1

My shortlist

Recent 2026 comparisons consistently put LinearB, Sleuth, and Swarmia near the top for DORA tracking, although rankings vary depending on whether the emphasis is deployment analytics, developer experience, or broader engineering intelligence.Models Agree+1

Why I'd pick LinearB

For a team starting from scratch, the important thing isn't just a pretty DORA dashboard. You want the platform to reliably connect:

Git → PR → issue → CI/CD → production deployment

That lets you distinguish an actual deployment from a merge or release artifact—one of the biggest sources of bad DORA data.Gitrecap

LinearB is particularly compelling because it combines DORA metrics with PR/cycle-time analysis and workflow automation, so you can go from:

"Our lead time is 3.2 days" to:

"Most of that time is sitting in code review, particularly for Team X." That makes the metrics actionable rather than just executive reporting.

One important caveat: don't optimize solely for deployment frequency and lead time. The current DORA model has five metrics, including change fail rate, failed deployment recovery time, and deployment rework rate. DORA also recommends interpreting metrics in the context of the particular application/service rather than using them as a simplistic ranking of teams.Dora

If you tell me your stack (GitHub/GitLab, Jira/Linear, GitHub Actions/Jenkins/CircleCI/etc.), team size, and whether you're buying for engineers or VP/CTO reporting, I can narrow this to the best 2–3 options and what I'd choose for your situation.

If your goal is specifically DORA metrics plus broader engineering intelligence, I’d shortlist LinearB, Jellyfish, Swarmia, and DX rather than buying a pure DORA dashboard.

One important update: DORA now uses five software-delivery metrics, not just the original four: change lead time, deployment frequency, failed deployment recovery time, change fail rate, and deployment rework rate.Dora+1

My pick: LinearB

LinearB would be my default recommendation if you're primarily trying to answer:

• How long does code take to get from commit → production?
• How frequently are we deploying?
• Where are PR/review/merge bottlenecks?
• Which teams or services are slowing delivery?
• What engineering workflow changes could improve those numbers?

It's particularly strong when you want DORA + workflow analytics + actionable bottleneck detection, rather than simply displaying metrics. Recent comparisons also consistently put LinearB among the leading DORA/engineering-intelligence platforms.Gitrecap+1

How I'd choose

The key distinction is that DORA measurement itself isn't particularly difficult; the hard part is getting trustworthy data from Git, PRs, CI/CD and production deployments and correctly distinguishing a merge from an actual deployment.Gitrecap

For example, DORA defines lead time as the time from code committed to successfully running in production, while deployment frequency is how often the application is deployed. Dora A platform that merely counts Git merges as deployments can give you very misleading numbers.

What I'd do

If you're a ~20–200 engineer organization, I'd trial LinearB and Swarmia side-by-side.

If you're 200+ engineers and engineering leadership wants portfolio/resource visibility, I'd put Jellyfish into the trial.

If you tell me your Git provider (GitHub/GitLab/Bitbucket), CI/CD (GitHub Actions/Jenkins/etc.), team size, and whether you care more about DORA or developer productivity, I can give you a much more specific recommendation—including pricing and a LinearB vs. Jellyfish vs. Swarmia comparison.dora.dev