Jellyfish alternatives: 6 engineering intelligence platforms compared

Six platforms come up when a team shops for a Jellyfish alternative: Navigara, DX, LinearB, Swarmia, Faros AI, and Waydev. Which one fits depends on whether you need engineering work expressed in dollars, developer experience measured by survey, or throughput measured against your own pre-AI history. Jellyfish does the first of those better than anything else in the set, so the useful question is which of the three jobs you are actually buying for.

Why teams look for an alternative to Jellyfish

Jellyfish reads Git, the planning system, and the people data. The roster and payroll join is the product thesis, and it holds up: it expresses engineering work in dollars, the unit a finance partner can reconcile to and the unit an R&D capitalization schedule requires. Jellyfish also publishes AI spend attribution at the token level, reported by tool, team, or initiative. For capitalization and for board investment reporting, it is the strongest instrument in this comparison.

The reasons teams still look elsewhere are fit problems, and they are specific.

Allocation accuracy tracks planning hygiene. The percentages come partly from the planning system, so an org with drifting epics gets a number it cannot defend in the room where it matters. Think about the Thursday review where a director opens with an allocation slide and the CFO asks what the maintenance share bought. If the epics behind that slide were retitled twice mid-quarter, the director knows it and so does everyone on the call.

Jellyfish publishes no pricing, which in this category means a discovery call before a number. A 40-engineer org that wants to price a tool this afternoon needs to look at platforms that publish pricing tiers.

Jellyfish reports at the individual level. The company has published its own guidance against using engineering metrics for individual performance evaluation, and both statements are true. Some orgs will not install a per-person view at all, regardless of what the vendor guidance says.

And allocation answers where investment went. A leader who has been asked whether throughput changed after the AI tooling rollout needs a different measurement, because the share of the quarter spent on features can hold steady while the value of what shipped moves a long way.

The six alternatives, one at a time

Navigara

Navigara scores each merged change and compares the current quarter to that same team’s pre-AI window. The comparison point is your own history rather than an industry average, which matters when your stack and team composition differ from the average.

Fits an org of roughly 20 to 100 engineers that adopted AI coding tools in the last few years and needs a defensible number on whether throughput moved. Measurement runs at team and repository level, and repository history is the only required input, so the setup does not depend on anyone’s ticket discipline.

Navigara does not publish a capitalization feature, does not join roster or payroll data, and has no per-developer throughput view. Pricing is published: Explore free for 14 days up to 1,000 pull requests analyzed, Measure $7 per developer per month for teams up to 30, Pro $30, Private Benchmark $4,500 one-off.

Navigara’s own Q2 2026 study shows why the distinction between activity and value is worth paying for.

Where the year-over-year change came from

Activity per engineer moved a little. Value per commit moved a lot.

Two columns comparing year-over-year change in commits per engineer at about 15 percent against performance per commit at about 100 percent, showing that almost all of the movement came from the value of each change rather than from the count of changes.

DX, an Atlassian company

DX runs a survey-heavy mix. The DXI composite is a single score out of 100 built from 14 survey-based drivers. DX does not publish the aggregation formula; the method captures developer experience, friction, and sentiment, which repository data cannot see at all. Atlassian announced the acquisition on 18 September 2025 and completed it on 10 November 2025. The $1B figure comes from DX’s own announcement and press coverage rather than from Atlassian, which did not disclose terms. DX still operates under its own brand and has since published a commitment to data neutrality.

Fits an org whose current problem is friction: slow builds, painful onboarding, a review queue nobody wants to talk about. DX’s published position is “Never tie throughput metrics to individual performance”, which makes it an easier install in a team that is nervous about measurement.

DX publishes an AI cost management report covering token-level spend, broken out by team, contributor, and tool. It publishes no pricing. Capitalization is a published DX feature.

LinearB

LinearB reads Git and PR telemetry, trackers, and CI/CD data, and reports cycle time, DORA, and PR size, benchmarked against 8.1M+ pull requests. That benchmark is a real asset, and the pricing is published: $29 and $59 per user per month, on tiers built for teams of 50+ and 100+ developers.

Fits a delivery-flow problem. If the complaint is that changes sit in review for days, this is the category’s most direct instrument, and capitalization is published too.

AI cost attribution is not published by LinearB. The only published cost mechanic is LinearB’s own AI credits. Reporting is team-first with role-based access, and the company concedes that misuse is possible rather than claiming the design prevents it.

Swarmia

Swarmia is the closest like-for-like to Jellyfish on the allocation job. It reads Git, trackers, CI/CD, and surveys, and connects to 22 HRIS and payroll systems, which gives it the same ability to express engineering work in both time and money. Investment Balance is the headline view. There is deliberately no composite score.

Fits an org that wants the Jellyfish reporting shape with pricing on the website: free for up to 9 developers, then $45 and $55; capitalization $18 and AI cost $5 per developer per month. Swarmia is the only platform here claiming a SOC 1-audited approach to capitalization. AI cost is published by person per tool, and the stated position is “Say bye to leaderboards and stack ranking”.

What you give up is a single number to put on a slide, which some boards ask for.

Faros AI

Faros AI claims to use 60+ sources, frames its AI cost reporting as cost per verified outcome, and publishes a Token Attribution Ledger. On AI spend attribution, it has the strongest published capability of this set.

Fits an org with a large AI tooling bill and a mandate to attribute it. If the question in front of you is which spend produced which result, start here.

Faros AI publishes no pricing and has no stated position on individual reporting. It no longer publishes capitalization pages, so its capitalization status is unclear.

Waydev

Waydev reads Git and PR telemetry plus Jira, and reports Impact, a composite score derived partly from lines of code. AI cost attribution is published by tool, team, and seat, and pricing is $29 or $49 per active contributor per month, billed annually. Capitalization is published.

Fits a leader who wants one composite number and per-person detail, with a published price.

The lines-of-code component is worth understanding before you install it, because a score partly derived from volume rewards volume. Waydev’s only published caution is “Don’t use metrics to micromanage”.

Side by side

Capitalization is published by Jellyfish, DX, LinearB, Swarmia, and Waydev. Navigara does not publish a capitalization feature.

If the number you keep being asked for is whether throughput changed after the AI rollout, connect a repository and the pre-AI window scores in the first pass.

When staying with Jellyfish is the right call

An org that capitalizes R&D, has a finance partner who needs engineering expressed in dollars, and already runs a disciplined planning system should stay. The payroll join does work no repository-only tool can do, and switching to save on a subscription at the cost of losing the capitalization schedule is a bad trade.

The same applies to a team whose board asks for investment mix every quarter and has learned to read the Jellyfish view. Swarmia can produce a comparable allocation report at a published price, and moving still costs you a quarter of reconciliation. If the current reporting survives scrutiny, keep it.

How to choose

Three questions decide it.

Does finance need engineering expressed in dollars? If yes, the shortlist is Jellyfish and Swarmia, with Swarmia having published pricing and a claimed SOC 1-audited capitalization approach. Navigara is out of this question entirely.

Who is allowed to see per-person numbers? Jellyfish, Waydev, and Faros AI can report at the individual level. DX, Swarmia, and LinearB publish positions against individual performance use. Navigara has no per-developer throughput view. Decide this before the demo, because it is hard to walk back once the data exists.

Is the question you are being asked about allocation or about change? Allocation instruments report where the quarter went. A baseline instrument reports whether the value of what shipped moved, which is the question that follows an AI tooling rollout. Plenty of 300-engineer orgs run one of each, because the two reports answer to different people.

Talk to us if you want the baseline comparison run against your own repository history before you commit to anything.