Faros AI vs Jellyfish: Data Integration or Investment Reporting

Both are enterprise platforms, both sell to the same buyer, both quote rather than publish pricing. The comparison comes up constantly and the two products are aimed at different problems.

Jellyfish translates engineering activity into business language for people outside engineering. Faros AI unifies data from across a messy toolchain and, since 2023, has staked its position on answering the AI return question.

Based on vendor documentation, buyer reviews and public sources. Not a hands-on pilot.

Positioning

What Faros is built around

Data consolidation first. It connects to over a hundred tools — version control, issue trackers, CI/CD, incident management, AI coding assistants — and normalises them into one model. Buyer reviews consistently name this as the standout: pulling data from genuinely disparate parts of a stack and linking it into something coherent.

That matters at organisations where the toolchain is heterogeneous. If four business units use four issue trackers and two CI systems, the consolidation work is the product.

The AI position is the differentiator. Faros shipped AI impact analysis in October 2023, ahead of the market, and has built out token intelligence since: tracing AI spend to outcomes with attribution per session, per pull request, per team and per model, across Copilot, Cursor, Claude, Devin and others. The claim is causal modelling rather than raw activity counts.

Its April 2026 AI Engineering Report, built on two years of telemetry from 22,000 developers across more than 4,000 teams, found bugs per developer up 54% and incidents per pull request more than tripling at high AI adoption. That became an industry talking point.

Read that number with the caveat attached. It is vendor research published by a company selling AI impact measurement, on its own customer telemetry. The direction is consistent with independent findings — DORA's 2025 research and LinearB's benchmarks both point the same way — but a self-published figure from an interested party is not the same evidence class, and anyone quoting the 54% in a board deck should say where it came from.

A buyer signal worth knowing: the once-promoted Faros Community Edition has quietly disappeared from GitHub. That doesn't reflect on the commercial product, but if an open-source path or self-hosted evaluation was part of your plan, it isn't there.

What Jellyfish is built around

Explaining engineering to finance. Investment allocation, business-context mapping, virtual time cards, capacity forecasting — capabilities that exist to answer the question a CFO asks and developers never do. It was named a Leader in the inaugural 2026 Gartner Magic Quadrant for Developer Productivity Insight Platforms.

The pricing picture is better documented than most in this category. Vendor data puts the median annual contract at $35,920 across 91 purchases as of July 2026, with deals observed from around $16,500, while 50–150 seat contracts commonly land between $50,000 and $120,000. No published list price, no free trial, no self-serve signup, and deployments commonly reported at four to eight weeks.

The pricing structures differ, and it matters

This is the most practical difference between them.

Jellyfish prices per engineering seat with a contract minimum. Your bill scales with headcount, and the whole product comes with it.

Faros sells pre-built intelligence modules — Engineering Productivity, Software Quality, DevOps Maturity, Initiative Tracking, AI Copilot Evaluation, R&D Cost Capitalization — with the per-module price falling as you add more, or the platform licensed whole.

What that means for a buyer: modular pricing lets you start narrow, which is genuinely useful if you have one funded question. It also means the quote depends on scope negotiation rather than headcount alone, so two organisations of the same size can pay very different amounts. Get the module list and the discount curve in writing before comparing the headline number to a per-seat quote from anyone else.

Choosing

Faros if your toolchain is fragmented and consolidation is itself the problem, or if AI investment reporting is the funded question and you want per-model, per-team attribution. Also if R&D cost capitalisation is a requirement — it's a module rather than an afterthought.

Jellyfish if the recurring pain is a CFO or board asking where engineering investment went, your stack is reasonably standard, and you want the category's most established answer to that specific question.

Neither if you're under roughly 200 engineers. Both are calibrated for organisations where a six-figure platform is a small fraction of engineering spend. Below that, the features carrying the price are answering questions you don't have.

Frequently asked

Can Faros do investment allocation like Jellyfish? It has Initiative Tracking and R&D cost capitalisation modules covering related ground. Jellyfish's business-context mapping and capacity forecasting are the more developed answer to the executive reporting question specifically.

Can Jellyfish measure AI impact? It reports on delivery metrics that move after adoption. It operates on metadata, so it cannot attribute changes to specific assistants at the code level. Faros built its position on exactly that gap.

Which is cheaper? Not comparable without quotes, because the pricing models differ structurally. Get both, and normalise Faros's module scope against Jellyfish's all-in seat price before comparing.

Do either publish pricing? No. Both are quote-only. Expect 15–30% movement for volume and multi-year commitments, and budget four to eight weeks for deployment on either.