7 posts tagged with "engineering-intelligence"

Most engineering analytics platforms ask for a weekend before you see your first chart. OAuth into GitHub. Hook up Jira. Wait for the next deploy to backfill DORA. Convince Security to approve a webhook. Meanwhile the original question, "are we actually shipping faster after the reorg?", is two sprints stale.

A Chrome extension flips the order: install, log in, and you have signal from GitHub, GitLab, and Jira inside 30 seconds. No backend, no integrations review, no Looker license. That speed comes with sharp limits. This post covers what such an extension actually does, where it wins, and the slice of work it will never see.

Three names get pasted into every Engineering Intelligence shortlist in 2026: Pluralsight Flow, Jellyfish, and LinearB. Three different histories, three different buyers, three completely different bets on what an EI platform should be. And yet the average mid-market engineering leader spends two weeks evaluating all three and walks away unsure which one fits.

The confusion isn't accidental. All three vendors describe themselves with overlapping language ("engineering intelligence", "DORA metrics", "data-driven engineering") while internally optimizing for very different ICPs. The 2023 DORA State of DevOps Report (Forsgren et al., Google Cloud) flagged this exact problem: the tooling category had outpaced the buyer's mental model. Most teams pick the wrong platform not because the platforms are bad, but because the platforms aren't even competing on the same axis.

This piece untangles it. No vendor pitch. We'll name where each wins and where each is wrong for you.

Roughly 80% of engineering intelligence vendors added "AI" to their marketing between 2024 and 2026. GitHub's Octoverse 2024 reported that generative AI tooling overtook the rest of the developer-tools category in adoption. Every dashboard suddenly has an "ask AI" box, every quarterly release ships an "AI insights" tile. We tested the platforms that matter, and most "AI features" turn out to be the same SQL query with a paragraph of LLM-generated prose taped on top.

This is a working leader's guide — what each AI feature actually does, where it earns its keep, and where it produces statistically wrong but very confident answers.

Forrester named "Engineering Intelligence" a distinct software category for the first time in 2024. Eighteen months later, we count at least 40 vendors competing for the same buying committee — VP Engineering, CTO, CFO, sometimes a Chief of Staff. The pitch is identical across all of them. The data quality, deployment model, and pricing transparency are not.

We tested 15 of them. Some are excellent. Some are expensive wrappers around git log. This is the buyer's guide we wish we had when we built our own platform.

Four dashboards. One platform. From a single engineer's day to organisation-wide cost. Below — what each view shows, in screenshots.

Faros AI takes a data aggregation approach to engineering intelligence — connecting 50+ tools through open-source connectors (an alternative to Apache DevLake), normalizing the data into a unified model, and presenting it through Grafana dashboards. PanDev Metrics takes an all-in-one platform approach with integrated analytics, IDE tracking, and financial features. Same goal, very different architectures.

The engineering intelligence market has matured significantly. What started as simple git analytics has evolved into a diverse ecosystem of platforms measuring developer productivity, delivery performance, engineering costs, and developer experience. This guide covers the 15 most relevant tools in 2026, organized by category.