Engineering teams are investing more in AI coding tools, and that means the platform you use to measure delivery performance needs to keep up. Jellyfish has been a go-to for engineering analytics, but it’s not the only option on your radar anymore.

GitKraken Insights gives you full-stack developer intelligence that connects DORA metrics, AI impact tracking, and code quality in one place. This article walks through six platforms worth evaluating, with clear criteria so you can match the right tool to your team’s needs.

Quick guide: 6 best engineering intelligence platforms for engineering managers

1. GitKraken Insights: The best engineering intelligence platform for tracking DORA metrics and AI coding tool impact together
2. DX: Survey-driven approach to measuring developer experience across teams
3. Swarmia: Connects Git and project tracker data for development workflow visibility
4. LinearB: Workflow automation paired with cycle time and throughput metrics
5. Faros AI: Aggregates data from multiple engineering tools into unified dashboards
6. Allstacks: Forecasting-focused platform that models project delivery risk

How we chose the best engineering intelligence platforms for measuring DORA metrics

Picking the right engineering intelligence platform depends on what you actually need visibility into. We looked at tools that go beyond vanity dashboards and give you metrics tied to real delivery outcomes.

Here’s what mattered most in our evaluation:

• DORA metric coverage: Does the platform track all four DORA metrics (deploy frequency, lead time, change failure rate, MTTR) so you can benchmark your team’s delivery health?
• AI impact measurement: Can you see how AI coding tools affect code quality, rework rates, and developer output?
• Setup time and onboarding: How quickly can your team go from zero to actionable data without a months-long implementation?
• Git host and tool integrations: Does the platform connect to GitHub, GitLab, Jira, Linear, and the CI/CD tools your team already relies on?
• Developer-friendly design: Are the dashboards built for engineering managers and team leads, not just executive presentations?
• Code quality signals: Can you monitor churn, duplication, and post-merge rework alongside delivery velocity?

The 6 best engineering intelligence platforms for engineering managers

1. GitKraken Insights: Best overall engineering intelligence platform for DORA and AI impact

GitKraken Insights tracks all four DORA metrics and connects them directly to AI coding tool adoption data. You get deploy frequency, lead time, change failure rate, and MTTR in one view, alongside metrics that show how tools like Claude Code, Cursor, and GitHub Copilot affect your team’s output.

What sets GitKraken Insights apart is the AI Adoption dashboard. It scores each developer on adoption depth and agent autonomy, then groups your team into cohorts so you can identify who’s leading, who’s learning, and where to invest next.

The platform also ranks every AI coding agent your team uses by cost, cycle time, PR size, and bugs shipped.

According to the 2025 DORA Report from Google Cloud, teams that measure AI’s effect on their delivery pipeline are better positioned to scale adoption responsibly. GitKraken Insights gives you that measurement layer out of the box, with a 15-minute setup and integrations across GitHub, GitLab, Jira, Linear, VS Code, and more.

GitKraken Insights features

• AI Agent Leaderboard: Ranks every AI tool and agent your team uses by shipped output, cost per PR, and production defect rate, so you know which investments are paying off
• AI Adoption and Autonomy Scores: Two scores per developer that track daily usage depth and how much work is handed off to agents, helping you spot rollout gaps early
• Repo Agent-Readiness Scoring: Every repository gets an automated score based on AI instruction files, CI configuration, test coverage, and build reliability, with action items when something’s missing
• DORA Metrics with Trend Context: All four DORA metrics displayed with rolling trend data so you can tell whether a spike is noise or a pattern that needs attention
• Code Quality Monitoring: Tracks churn, copy-paste vs. moved code, tech debt hotspots, and post-merge rework, connecting code quality directly to AI tool usage
• Voice of the Developer Surveys: Correlates team sentiment with performance data, giving you a leading indicator of burnout or velocity drops before they show up in the numbers

GitKraken Insights pros and cons

Pros:

• Combines DORA metrics, AI impact tracking, and code quality signals in a single platform trusted by 40 million developers
• 15-minute setup with automatic data collection from your existing Git repositories and CI/CD tools
• Developer-specific AI adoption views include private coaching recommendations, not just team-level surveillance dashboards

Cons:

• Advanced AI impact features like the Agent Leaderboard and Business Impact dashboard require the Enterprise plan
• Historical data depth varies by plan tier, with the entry-level plan covering 60 days
• Dev capacity forecasting is still marked as coming soon and not yet available for all users

2. DX: Survey-first measurement for developer experience programs

DX focuses on developer experience measurement through structured surveys and SDLC analytics. The platform collects sentiment data from your engineering team and pairs it with system-level metrics to surface where developer time goes.

DX’s survey methodology draws on published research into developer productivity frameworks.

The platform also includes SDLC analytics that track cycle time, deployment frequency, and throughput. DX connects to Git hosts and project trackers to pull this data automatically.

DX features

• Developer surveys: Structured questionnaires that measure satisfaction, tooling pain points, and time allocation across your engineering organization
• SDLC analytics: Connects to Git and project management tools to track cycle time, deploy frequency, and throughput metrics
• Benchmarking: Compares your team’s survey results against aggregated industry data to identify relative strengths and gaps

DX pros and cons

Pros:

• Survey methodology grounded in published developer productivity research
• Pairs qualitative sentiment data with quantitative SDLC metrics in one dashboard
• Industry benchmarking allows comparison against anonymized peer data

Cons:

• Does not include dedicated AI coding tool impact metrics or agent adoption tracking
• Survey-dependent insights require consistent team participation to remain accurate
• SDLC analytics are narrower in scope than platforms with full DORA metric support and code quality analysis

3. Swarmia: Git and project tracker analytics for development teams

Swarmia connects your Git repositories and project trackers to surface engineering metrics like cycle time, review throughput, and DORA indicators. The platform organizes data by team, making it straightforward to compare delivery patterns across groups.

Swarmia also includes goal-setting features that let you define targets for specific metrics and track progress over time. The working agreements feature allows teams to set and monitor developer workflow standards around PR review time and WIP limits.

Swarmia features

• DORA metrics: Tracks deployment frequency, lead time, change failure rate, and MTTR pulled directly from your Git and CI/CD data
• Working agreements: Lets teams define and monitor standards for PR review time, work-in-progress limits, and cycle time targets
• Investment balance: Shows how engineering time splits between new features, maintenance, and operational work

Swarmia pros and cons

Pros:

• Working agreements feature gives teams a way to set and track delivery standards collaboratively
• Investment balance view breaks down time allocation between new features and maintenance work
• Connects to GitHub, GitLab, Jira, and Linear for centralized data collection

Cons:

• Does not include AI coding tool adoption metrics or AI impact measurement
• Code quality analysis like churn, duplication, and post-merge rework is not covered
• Bitbucket integration is not listed among supported Git hosts

4. LinearB: Workflow automation with engineering metrics

LinearB combines engineering metrics with workflow automation features like automated PR routing and review assignment. The platform tracks cycle time, throughput, and DORA metrics, and adds automation layers designed to reduce bottlenecks in the pull request process.

LinearB also includes a benchmarking feature that compares your team’s metrics against its aggregated dataset. The platform connects to GitHub, GitLab, Bitbucket, and project management tools like Jira.

LinearB features

• gitStream: An automation engine that routes and labels pull requests based on configurable rules, reducing manual triage work
• Cycle time breakdown: Decomposes PR lifecycle into coding, review, and merge stages so you can pinpoint where delays happen
• Team benchmarking: Compares your engineering metrics against LinearB’s aggregated dataset of development teams

LinearB pros and cons

Pros:

• Workflow automation features like gitStream can reduce manual PR triage for review assignments
• Cycle time breakdown helps identify which stage of the PR process causes delays
• Connects to GitHub, GitLab, Bitbucket, and Jira for data collection

Cons:

• Does not include AI coding tool impact metrics or agent adoption tracking
• Automation rules in gitStream require configuration and maintenance as team workflows change
• Does not track code quality metrics like churn, duplication, or post-merge rework

5. Faros AI: Multi-source data aggregation for engineering dashboards

Faros AI ingests data from a wide range of engineering tools, including Git hosts, CI/CD systems, incident management platforms, and project trackers, to build unified dashboards. The platform’s connector library is one of its distinguishing features, pulling from dozens of sources into a normalized data model.

Faros AI also includes an AI adoption module that tracks how your team’s use of AI tools is changing engineering workflows across your organization.

Faros AI features

• Connector library: Ingests data from 40+ engineering tools into a single normalized data model for cross-tool analysis
• AI adoption tracking: Monitors how AI tool usage is affecting engineering workflows and delivery metrics
• Custom dashboards: Allows creation of tailored views combining data from multiple sources into engineering-specific reports

Faros AI pros and cons

Pros:

• Connector library covers a wide range of engineering tools for centralized data aggregation
• Normalized data model allows cross-tool analysis that combines Git, CI/CD, and incident data
• Custom dashboard builder lets teams create views tailored to their specific reporting needs

Cons:

• Broad connector setup can require significant initial configuration time across multiple tools
• Does not include developer sentiment surveys or qualitative experience measurement
• AI adoption tracking is a newer addition and has fewer granular metrics than dedicated AI impact tools

6. Allstacks: Predictive delivery analytics for project risk

Allstacks focuses on predictive analytics, using historical data from your engineering tools to model delivery timelines and flag project risk. The platform ingests data from Git, project trackers, and CI/CD systems to generate forecasts about when features and releases are likely to ship.

Allstacks also includes engineering metrics for cycle time and throughput, but its primary differentiator is the forecasting engine that helps you communicate delivery expectations to stakeholders.

Allstacks features

• Delivery forecasting: Uses historical patterns to model expected ship dates and flag risks before deadlines are missed
• Risk scoring: Assigns a risk level to active projects based on current velocity trends and scope changes
• Value stream mapping: Tracks how work flows through your engineering pipeline from ticket to deployed code

Allstacks pros and cons

Pros:

• Forecasting engine helps teams communicate expected delivery dates to product and business stakeholders
• Risk scoring surfaces projects at risk of delay based on velocity trends, not just gut instinct
• Value stream mapping tracks work from ticket creation to deployment

Cons:

• Does not track DORA metrics as a native feature set
• Does not include AI coding tool impact metrics or agent adoption tracking
• Forecasting accuracy depends on consistent historical data, which newer teams may not have

Comparison table: The best engineering intelligence platforms for DORA and AI impact

Platform	AI Impact Tracking	All 4 DORA Metrics	Code Quality Metrics
GitKraken Insights	✓	✓	✓
DX	✗	✗	✗
Swarmia	✗	✓	✗
LinearB	✗	✓	✗
Faros AI	✓	✗	✗
Allstacks	✗	✗	✗

What should you look for in an engineering intelligence platform?

The right platform depends on the questions your leadership team asks most often. If your CTO wants to know how AI tools are affecting delivery velocity, you need a platform that connects DORA metrics to AI adoption data, not just one or the other in isolation.

Look for a tool that tracks all four DORA metrics alongside code quality signals like churn and post-merge rework. Platforms that only cover cycle time and throughput leave gaps when you need to report on deployment stability or incident recovery.

Integration depth matters too. A platform that connects to your Git host, CI/CD pipeline, project tracker, and AI coding tools will give you a more accurate picture than one that requires manual data exports or CSV uploads.

How do DORA metrics help engineering teams adopt AI tools responsibly?

DORA metrics give you a baseline for delivery performance before and after introducing AI coding tools. If your deploy frequency increases but your change failure rate spikes alongside it, that’s a signal worth investigating.

Tracking DORA metrics alongside AI adoption data lets you answer questions like: Are AI-assisted PRs merging faster? Is rework increasing on AI-generated code? Are specific agents producing more production defects?

GitKraken Insights connects these data points automatically. You can compare AI productivity metrics against your DORA baseline by team, by tool, and by developer, so you’re making rollout decisions based on evidence rather than assumptions.

Why GitKraken Insights is the best engineering intelligence platform for DORA and AI impact

Most engineering intelligence platforms track either delivery metrics or AI adoption. GitKraken Insights connects both in a single view. You see how AI coding tools affect your DORA metrics, code quality, and developer experience, all from one dashboard.

GitKraken Insights also gives you granular AI agent rankings. Every tool your team uses gets scored by cost, output, cycle time, and production defect rate. That means you can walk into a budget review with the data to back up your AI tool investments, per team, per tool, per developer.

If you’re evaluating engineering intelligence platforms and need a solution that covers DORA metrics, AI impact, and code quality without a six-month rollout, GitKraken Insights is built for exactly that.

FAQs about Jellyfish competitors for engineering analytics

What is an engineering intelligence platform?

An engineering intelligence platform collects data from your Git repositories, CI/CD tools, and project trackers to surface metrics about delivery performance. GitKraken Insights tracks DORA metrics, AI coding tool impact, and code quality signals in one place, giving engineering managers visibility into how their teams ship software.

Why do engineering teams look for Jellyfish alternatives?

Teams often look for Jellyfish alternatives when they need dedicated AI impact tracking alongside DORA metrics. GitKraken Insights fills that gap with AI adoption scores, agent leaderboards, and repo readiness assessments that Jellyfish does not include natively.

Can DORA metrics measure the impact of AI coding tools?

DORA metrics give you a delivery performance baseline. Pairing them with AI-specific data shows whether AI tools are improving or degrading your pipeline. GitKraken Insights automates this comparison by connecting DORA data directly to AI adoption and code quality metrics.

How long does it take to set up an engineering intelligence platform?

Setup time varies widely. Some platforms require months of configuration and custom integrations. GitKraken Insights connects to your Git repositories and starts surfacing data in about 15 minutes, with no complex onboarding required.

What DORA metrics should engineering managers track?

Engineering managers should track all four: deployment frequency, lead time for changes, change failure rate, and mean time to recovery. Together, they show how fast and how reliably your team ships. GitKraken Insights displays all four with trend context so you can spot patterns early.