TempoStart with the data contract
Deployment frequency and change lead time require a trustworthy definition of production. Change fail rate and failed deployment recovery time require a connection between deployments and incidents, rollbacks, fixes, or another explicit failure model. Deployment rework rate requires teams to identify unplanned deployments made in response to production incidents.
Before evaluating visualizations, test whether a tool can represent monorepos, multiple services, custom release processes, and the incident source your teams already use. Bad attribution produces precise-looking numbers that describe the wrong system.
- Source control and pull-request identity.
- Production deployment timestamp and service identity.
- The commits or pull requests included in each deployment.
- Failure, incident, remediation, and recovery timestamps.
- Planned versus unplanned deployment classification for rework rate.
- Team or service ownership for meaningful aggregation.
DORA metrics tools compared
The table summarizes public product documentation reviewed on August 4, 2026. It focuses on operating model and measurement emphasis; verify integrations and commercial terms with each vendor before buying.
Tempo focuses on deployment frequency and end-to-end delivery lead time with PR-level traceability. Many product pages still describe the legacy four-key model, so ask every vendor how deployment rework rate is represented.
| Tool | Measurement emphasis | Operating model | Best fit |
|---|---|---|---|
| Tempo | Deployment frequency and end-to-end delivery lead time, with PR-level traceability | Hosted analytics with optional local AI-attribution CLI | Smaller GitHub teams prioritizing PR flow and evidence-based AI coding analytics |
| LinearB | Advertises the traditional four with benchmarks; verify deployment rework rate | Hosted platform | Organizations combining delivery measurement with PR workflow automation |
| Swarmia | Advertises the traditional four with deployment insights; verify deployment rework rate | Hosted platform | Teams that want DORA, broader engineering metrics, and team-level improvement loops |
| DX | Documents traditional DORA via self-reported or system data; verify deployment rework rate | Hosted enterprise platform and data layer | Organizations combining quantitative delivery data with developer experience and business context |
| Apache DevLake | Open-source DORA dashboards; verify current five-metric coverage in your implementation | Self-hosted with Docker or Kubernetes and Grafana | Teams that value control and customization and can own setup and maintenance |
Use five questions to select a tool
A feature checklist hides the hardest implementation work. Ask each vendor to calculate the same recent deployment and trace the result back to source events. The explanation should survive contact with your actual release process.
- Can it identify production consistently across every deployment path?
- How are pull requests and commits attributed to services and teams?
- How are change failures and recovery events created or inferred?
- Can engineers drill from an aggregate into the events behind it?
- Who will maintain mappings when repositories, services, and workflows change?
Know when DORA is necessary but insufficient
DORA describes delivery performance. It does not explain developer friction, whether engineering effort advanced the right initiative, or how AI-assisted work changed review load and code quality constraints.
Choose the additional measurement layer from the decision you face. Developer-experience surveys help explain friction; investment data helps connect work to priorities; AI attribution helps compare assistant adoption with delivery outcomes.
Frequently asked questions
What are the five DORA metrics in 2026?
The current model uses change lead time, deployment frequency, failed deployment recovery time, change fail rate, and deployment rework rate. DORA introduced the fifth metric, deployment rework rate, in 2024.
Why do many DORA tools still advertise four metrics?
The original four keys became the industry shorthand and many product pages still use that model. Current DORA guidance uses five metrics, so buyers should verify how each tool measures deployment rework rate.
Can GitHub data alone calculate all five DORA metrics?
Usually not. Repository and pull-request data can support part of lead time, but complete DORA measurement needs production deployment data and a reliable failure or incident source.
Is Apache DevLake a free DORA metrics tool?
Apache DevLake is open source and self-hosted. Software licensing is not the only cost: teams still own infrastructure, connector configuration, data quality, dashboards, and ongoing maintenance.
Does Tempo support all five DORA metrics?
Not yet. Tempo currently measures deployment frequency and lead time to production. Change fail rate, failed deployment recovery time, and deployment rework rate are not implemented, so the product does not claim complete DORA coverage.
Sources and further reading
- DORA software delivery performance metricsThe current five-metric model and primary guidance from the DORA research program.
- LinearB DORA metrics platformOfficial product description for DORA coverage, benchmarks, and workflow features.
- Swarmia DORA metricsOfficial product description for DORA views, deployment insights, and working agreements.
- DX DORA setup documentationOfficial documentation for self-reported and system-based DORA measurement.
- Apache DevLake introductionOfficial overview of the open-source data platform, connectors, DORA implementation, and hosting model.