GitPrime
Waydev
LinearB
Haystack Analytics
CodeClimate
Swarmia
OKAY HQ
SonarQube
DevPrism
LinearB
Swarmia
Typo
Faros.ai
Hatica
Waydev
DevPrism is an engineering intelligence platform for engineering managers, VPs of Engineering and CTOs who need a factual view of how their teams deliver software.
It connects to the tools a team already uses - GitHub, GitLab, Azure DevOps, Jira, Linear, SonarQube and Codacy - and computes DORA delivery metrics, the SPACE framework and developer experience surveys. There is no data pipeline to build and nothing to install on developer machines.
On top of those metrics it correlates three axes that are usually measured separately: delivery speed, product quality (churn, duplication, coverage, code smells) and the cost of AI coding assistants such as GitHub Copilot, Cursor, Claude Code, Codex and Devin Desktop. Spend is reported per pull request and per team rather than as a licence total.
Beyond dashboards, DevPrism acts on what it finds. It predicts which pull requests are likely to stall by comparing them against the team's own velocity baseline, detects quality regressions, and applies policies that write back to GitHub, Azure DevOps or GitLab - in suggest, approve or automatic mode, at the level of autonomy the organisation chooses. Alerts and weekly digests are delivered to Slack, Microsoft Teams, Google Chat or email.
The interface is available in English, French, Spanish and German. Data is hosted in the European Union and the platform is GDPR native. A free plan covers up to seven managed contributors, human or AI, with no credit card required.
GitPrime DevPrismGitPrime is recommended for engineering managers, team leads, and project managers who are looking for data-driven insights to understand and enhance the productivity of their software development teams. It's particularly useful for medium to large teams where it's critical to evaluate performance metrics objectively and address inefficiencies proactively.
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DevPrism's answer:
Engineering leadership in software organisations that have already rolled out AI coding assistants: VPs of Engineering, CTOs and Engineering Managers who are asked to justify the spend and to show what changed in delivery and quality.
Platform and developer experience teams use it too, for the delivery metrics and the developer experience surveys. It fits teams from a handful of developers on the free plan up to several hundred contributors.
DevPrism's answer:
Three axes that usually live in separate tools are correlated on the same data: delivery speed (DORA), product quality (churn, duplication, coverage, code smells) and the cost of AI coding assistants, reported per pull request rather than as a licence total.
The second part is what the correlation is for. Agents investigate why a metric moved, and a policy engine can act on the answer - assigning a reviewer, flagging a risky pull request, escalating a stale one - writing back to GitHub, Azure DevOps or GitLab at the level of autonomy the organisation grants, from observation only to automatic remediation.
Pricing follows managed entities rather than headcount: humans, AI assistant seats and autonomous agents are counted separately, because not every contributor is a person any more.
DevPrism's answer:
Because the question most teams now have is not "how fast do we ship" but "what did the AI investment change, and what did it cost". DevPrism answers it on your own data: adoption of GitHub Copilot, Cursor, Claude Code, Codex and Devin Desktop next to delivery metrics, quality signals and token spend, per team and per period.
It also acts. Beyond dashboards, a policy engine applies rules in three execution modes - suggest, act with approval, or act automatically - with dry run enabled by default, so automation is introduced at the pace the organisation is comfortable with.
Practical points: a free plan for up to seven managed contributors with no credit card, an interface in English, French, Spanish and German, and hosting in the European Union with a GDPR native design.
DevPrism's answer:
It started from a gap encountered while leading engineering teams. Copilot, Cursor and Claude Code get rolled out, productivity feels better, and then someone asks for the return on the investment - and there is nothing to show. Suggestion acceptance rate measures usage, not impact, and no dashboard connects AI usage to delivery outcomes.
Meanwhile the data already exists, scattered across GitHub, Jira, SonarQube and the assistants themselves, and teams rebuild the same spreadsheet every quarter. DevPrism was built to bring those sources together and, past the measurement, to act on what they reveal.
DevPrism's answer:
Backend in C# on .NET, with ASP.NET Core minimal APIs, Entity Framework Core, MediatR and Hangfire for background processing. PostgreSQL with the pgvector extension stores both relational data and embeddings for semantic search; Redis handles distributed caching.
The web application is React with TypeScript, built with Vite and styled with Tailwind CSS. Everything runs on Azure Container Apps in the France Central region, orchestrated with .NET Aspire.
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