DevPrism VS Hatica
Compare DevPrism VS Hatica and see what are their differences
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DevPrism
LinearB
Swarmia
Typo
Faros.ai
Waydev
Hatica
LinearB
Swarmia
Waydev
GitPrime
Haystack Analytics
Athenian
Teamplify
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.
Hatica equips engineering teams with work visibility dashboards, actionable insights and effective workflows to drive team productivity and engagement in remote and in-office environments alike. Free forever plans to help you get started quickly.
Features: Engineering metrics dashboards 100+ metrics from 20+ apps including Github, Jira, Slack, Zoom, Google Workplace Remote work insights Aggregated work overview, sprint and retro dashboards DORA metrics, CI/CD performance insights and code review analytics Collaboration analytics Team Goals based on dev metrics Async stand-ups and developer check-ins via Slack and Email Code quality metrics Automated Code reviews
DevPrism HaticaDevPrism'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.
Till we started using Hatica, most managers had 10s of tabs open, sifted through each one, and had to piece together work to get a picture of whatโs happening at work. With Hatica, these tabs are gone, and is replaced with one app! Especially the activity dashboards that show all activity along with check-ins from our team. Practically solved all our needs!
This is a young product with ambitious plans to become a comprehensive engineering metrics platform. This means, we can expect great surprises and some room for improvement.
The founders vision is clear and it shows in every release of the product. Plus, with such frequent feature releases, they might just achieve their vision! Responsive founders make the process of reporting bugs and requesting features a breeze and actually see it implemented in the app in a blazing fast turnaround time
Hatica provides all inputs needed for an engineering team! From gauging whether work load is balanced, to understanding peopleโs actual work hours, to finally looking at code churn - Hatica provides all of these in one place! Would love to see a TV mode so that we can present these dashboards in our workforce planning meetings.
LinearB - 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.
Swarmia - 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.
Typo - Your all-in-one engineering intelligence platform to optimise software delivery - Better Code, Faster Deployments, Productive Dev Teams!
Waydev - Waydev analyzes your codebase from Github, Gitlab, Azure DevOps & Bitbucket to help you bring out the best in your engineers work.
Faros.ai - Get Git and Jira Analytics in Under 10 Minutes
GitPrime - GitPrime uses data from any Git based code repository to give management the software engineering metrics needed to move faster and optimize work patterns.