
LinearB
Swarmia
Typo
Faros.ai
Hatica
Waydev
DevPrism correlates AI adoption with DORA delivery speed, code quality and ROI - then agents investigate the anomalies and act, at the level of autonomy you choose.

Create ChatGPT Application in seconds
Website, pricing, platforms and company facts side by side.
|
|
|
|
|---|---|---|
| Website | devprism.io | open-gpt.app |
| Pricing | — | |
| Platforms | — | |
| Company | Startup from France · 1 - 9 employees · 2026 | — |
| Listed in | — |
In their own words, as submitted to SaaSHub.


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, Bitbucket, Jira, Linear, SonarQube and Codacy - and...
No description of https://open-gpt.app/ yet.
What each product offers, as listed by its team.


Possible disadvantages
An editorial look at what each product does well and who it suits.


No analysis of DevPrism yet.
Overall verdict
Why this product is good
Recommended for
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing DevPrism and https://open-gpt.app/.
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
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.
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, GitLab or Bitbucket 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.
Share your experience with using DevPrism and https://open-gpt.app/. For example, how are they different and which one is better?
When comparing DevPrism and https://open-gpt.app/, you can also consider the following products.

LinearB delivers software leaders the insights they need to make their engineering teams better through a real-time SaaS platform. Visibility into key metrics paired with automated improvement actions enables software leaders to deliver more.
Compare LinearB to DevPrism or https://open-gpt.app/:

Swarmia is an engineering productivity software trusted by 600+ engineering teams worldwide. Use key engineering metrics to unblock the flow, align engineering with business objectives, and drive continuous improvement.
Compare Swarmia to DevPrism or https://open-gpt.app/:

Your all-in-one engineering intelligence platform to optimise software delivery - Better Code, Faster Deployments, Productive Dev Teams!
Compare Typo to DevPrism or https://open-gpt.app/:

Get Git and Jira Analytics in Under 10 Minutes
Compare Faros.ai to DevPrism or https://open-gpt.app/:

Engineering Analytics to boost developer productivity
Compare Hatica to DevPrism or https://open-gpt.app/:

Waydev analyzes your codebase from Github, Gitlab, Azure DevOps & Bitbucket to help you bring out the best in your engineers work.
Compare Waydev to DevPrism or https://open-gpt.app/: