Oobeya vs Apache DevLake
Compare a product platform with an open-source data foundation.
Evaluate Oobeya and Apache DevLake across delivery analytics, data customization, operational ownership, and the engineering workflows your team needs.
vsApache DevLakeFocus your evaluation
- DORA and source mapping
- Customization and maintenance ownership
- Product workflows and AI visibility
Start with the decisions your team needs to make.
Apache DevLake is an open-source platform for collecting, transforming, and analyzing DevOps data, with prebuilt dashboards and extensible metrics. Compare that foundation with Oobeya’s product workflows, team scorecards, symptoms, AI analysis, and deployment support requirements.
Capability comparison
Oobeya and Apache DevLake, side by side.
Compare the published product scope, then validate the details with the same real-world scenario.
Scroll horizontally to view the full comparison.
| Area | Oobeya | Apache DevLake | What to validate |
|---|---|---|---|
| Delivery analytics | Production-aware DORA metrics and lead time context within engineering dashboards. | Prebuilt DORA and other engineering dashboards on collected DevOps data. | Check source mappings, deployment detection, and metric definitions using the same releases. |
| Customization | Connected tool configuration, team context, and configurable metric aggregation. | Extensible collection and transformation framework, with SQL-based metric and dashboard customization. | Estimate the work to implement and maintain your required definitions and reports. |
| Investigation workflow | Team scorecards, work types, review symptoms, quality, and AI context. | Dashboards and a data model that teams can extend for their analytics requirements. | List what is available out of the box and what your team would build. |
| AI measurement | AI impact, attribution with Blamely, and usage and cost monitoring. | Evaluate the available sources and custom analysis needed for your AI measurement requirements. | Inspect assistant data collection and attribute ongoing engineering effort to any custom pipeline. |
| Deployment & ownership | Cloud, on-premise, and air-gapped options with commercial deployment discussions. | Installation options include Docker Compose, Kubernetes, Helm, and Temporal; customization and operations require assigned owners. | Compare hosting, upgrades, connector maintenance, monitoring, and support arrangements. |
Official product references
Updated
Oobeya in practice
Explore the evidence behind a trend.
Switch between the views relevant to this evaluation. Select metrics and investigate the work behind the headline numbers.
DORA Metrics
Explore featureDORA Metrics for Production
Software delivery performance for production pipelines
- Lead Time for Changes Medium
- 1w 4d↑ 52.1%
Lead time increased; review where changes wait before production.
- Deployment Frequency High
- 0.91↓ 48.0%
Production release frequency declined 48% versus the previous period.
- Change Failure Rate Elite
- 2.4%↑ 2.4%
Change failures increased; review recent releases that required remediation.
- Time to Restore Service High
- 10h 42mService recovery time
Production service recovery averaged 10h 42m in this period.
Deployment Frequency
Production deployments fell from 36 in August to 22 in September, while remaining above April’s level.
Maya Patel
Alex Morgan
Sam Taylor
Jordan LeeTry the same scenario
Estimate what it takes to operate the solution
Use the same repositories, issue tracker, and production pipelines for both evaluations.
Configure the data sources and validate a production delivery metric.
Identify which team views, reports, and investigations are ready and which require implementation.
Compare ongoing ownership for hosting, upgrades, integrations, security review, and support.
Common questions
Evaluating Oobeya and Apache DevLake
Need to discuss your team or technical setup? Contact us.
What should we evaluate when comparing Oobeya and Apache DevLake?
Start with dora and source mapping, customization and maintenance ownership, product workflows and ai visibility. Use the same teams, repositories, time period, and real engineering scenario to assess the evidence and investigation workflow.
Does Oobeya support on-premise and air-gapped deployment?
Yes. Oobeya supports cloud, on-premise, and air-gapped deployment. Review technical requirements, integration access, permissions, and operational ownership with your security and platform teams.
Can we compare AI adoption with engineering results?
In Oobeya, review AI code share and assistant usage alongside review time, rework, quality, and delivery trends. Higher AI usage alone does not establish a productivity improvement, and correlation does not prove causation.
How should we verify the comparison?
Use the linked official sources as a starting point. Confirm current product scope, plan limits, integrations, deployment options, and commercial terms directly with Apache DevLake and Oobeya. Validate the capabilities that matter using your own evaluation scenarios.
How should we compare cost and rollout effort?
Use the same scope for both evaluations. Include licensing, hosting, integration setup, onboarding, upgrades, maintenance, and support. Separate included product capabilities from any custom implementation effort.
What should we bring to an Oobeya evaluation?
Bring your toolchain, deployment constraints, shortlist, and two or three concrete questions. A review bottleneck, production delivery trend, or AI rollout question helps focus the walkthrough on the decisions your team needs to make.
Continue your evaluation
All comparisonsEvaluate Oobeya with your own engineering questions.
Bring your toolchain, shortlist, and deployment requirements. We’ll focus the walkthrough on the decisions your team needs to make.