Faros AI alternatives: 6 platforms for engineering throughput data

The six credible alternatives to Faros AI for engineering throughput data are Navigara, Jellyfish, DX, LinearB, Swarmia, and Waydev. Faros AI itself has the strongest published AI spend attribution of the group, and a team whose reporting problem is token cost should think hard before moving. The reason most teams end up on this page is simpler than a feature gap: Faros AI does not publish pricing.

What Faros AI publishes, and why it is hard to beat on AI spend

A platform team adopts three coding assistants across four squads in one quarter. Nobody signed a single contract for all of it. By the time the finance business partner asks what the assistants cost per unit of work delivered, the answer lives across three billing portals and a credit card statement, and the engineering director has to reconstruct it from memory.

Faros AI is built for exactly that reconstruction. It reports a Token Attribution Ledger, so token consumption is attributed rather than pooled, and frames the result as cost per verified outcome, putting spend on one side of a ratio and delivered work on the other. Of all the platforms in this comparison, its AI spend attribution is the strongest based on published evidence.

The data side is broad too. Faros AI claims 60 or more sources, which is the widest claim in the set and matters for orgs whose delivery signal is spread across many systems rather than concentrated in Git.

If the question you keep getting asked is what the AI tooling costs and what it is buying, Faros AI answers it more directly than anything else here, and a migration away from it needs a reason better than curiosity.

Why teams look for a Faros AI alternative anyway

Faros AI publishes no pricing. For an org of 40 engineers with a budget cycle that closes in three weeks, that is the whole story. No pricing in this category means a discovery call, a scoping exercise, a quote, and a director who needs a line item by Friday will shortlist the platforms whose numbers are on a web page.

Two other things are worth knowing before a shortlist gets written.

Faros AI publishes no stated position on individual-level reporting. Swarmia publishes “Say bye to leaderboards and stack ranking.” DX publishes “Never tie throughput metrics to individual performance.” Jellyfish publishes guidance against performance use of its metrics. Faros AI publishes none of those, which does not mean the product is misused, and does mean an engineering leader introducing it to a nervous team has no vendor sentence to quote in the first all-hands.

And it no longer publishes capitalization pages, and the old capitalization URLs no longer resolve to capitalization content, so that feature’s status is unclear. Capitalization is published by Jellyfish, DX, LinearB, Swarmia, and Waydev. If a controller needs that workflow, ask Faros AI directly rather than assuming.

The six alternatives and who each one fits

Navigara

Fits the leader who needs a before-and-after number on AI adoption rather than a spend report. Navigara scores each merged change based on what it was, then compares the current quarter against that same team’s pre-AI window rather than an industry average. Measurement runs at team and repository level, with no per-developer throughput view. Pricing is public: Explore: free for 14 days, up to 1,000 pull requests analyzed; Measure: $7 per developer per month for teams up to 30 developers; Pro: $30; Private Benchmark: $4,500 one-off; Enterprise: custom.

Does not publish a capitalization feature.

Jellyfish

Fits the org that capitalizes R&D and needs engineering expressed in dollars. Jellyfish joins Git and planning data to roster and payroll records, reports investment allocation percentages and R&D dollars, and publishes AI spend attribution at token level by tool, team, or initiative. It reports at the individual level, alongside published guidance against using engineering metrics for performance evaluation.

Does not publish pricing, so it swaps one enterprise sales cycle for another.

Swarmia

Fits the org where finance reporting is the main job. Investment Balance reports allocation in time and in money, fed by 22 HRIS and payroll integrations, and the capitalization add-on at $18 per developer-month is the only one in this set claiming a SOC 1-audited approach. AI cost is reported by person per tool at $5 per developer per month. Pricing is public: free to 9 developers, then $45 and $55.

Does not publish a composite score, deliberately, so the single before-and-after throughput number has to come from elsewhere.

DX (Atlassian)

Fits the org whose gap is developer experience rather than cost. DX uses a survey-heavy mix, and the DXI composite is a single score out of 100 derived from 14 survey-based drivers; DX does not publish the aggregation formula. Capitalization is published. Its published position on individual use is the firmest here: “Never tie throughput metrics to individual performance.” DX was acquired by Atlassian, announced 18 September 2025 and completed 10 November 2025, with the $1B figure coming from DX and press coverage rather than from Atlassian. It still operates under its own brand and has since published a commitment to data neutrality.

Publishes token-level AI spend by team, contributor, and tool. Does not publish pricing.

LinearB

Fits the delivery-flow conversation. Git and PR telemetry, trackers and CI/CD feed cycle time, DORA, and PR size, benchmarked against 8.1M or more pull requests. Capitalization is published. Pricing is public at $29 and $59 per user per month, on tiers built for teams of 50+ and 100+ developers. Reporting is team-first with role-based access, and LinearB concedes possible misuse.

Does not publish AI spend attribution. The only published cost mechanic is LinearB’s own AI credits, which is the wrong shape for a team trying to price third-party assistants.

Waydev

Fits the team that wants Git, PR, and Jira reporting, with prices on the website: $29 and $49 per active contributor per month, billed annually. Capitalization is published, and AI spend attribution is reported by organization, team, and developer. Its headline number is Impact, a composite metric derived partly from lines of code, reported at the organization, team, and developer levels.

Publishes one caution on metric use, “Don’t use metrics to micromanage,” which is thinner than what Swarmia and DX put in writing.

If the missing half of your AI ratio is what shipped before the assistants arrived, connect a repository and the pre-AI window scores on the first pass.

The seven platforms side by side

Capitalization sits outside that table because the picture is uneven. Jellyfish, DX, LinearB, Swarmia, and Waydev publish it. Swarmia is the only one claiming to have a SOC 1-audited approach. Faros AI’s status is unclear. Navigara does not publish it.

Which question actually decides this

Do you need a price this quarter? Faros AI, Jellyfish, and DX publish none, so any of the three means a sales cycle before a budget line exists. Navigara, Swarmia, and Waydev publish prices that a director can enter into a spreadsheet today. LinearB publishes pricing tiers for teams of 50+ and 100+ developers, which puts it below that headcount, regardless of the per-seat figure.

Is your reporting gap the cost side or the change side? Cost attribution is well served. Faros AI, Jellyfish, and Swarmia all report AI spend precisely, each in a different format. The change side is a separate measurement problem, because it needs a scored history of what shipped before the assistants arrived, and spend reporting cannot reconstruct that retroactively.

That gap matters more than it looks. In Navigara’s Q2 2026 study, the same 78-week window produced a +130% year-over-year change in performance per engineer on the open cohort and +82% on the fixed panel of 388 engineers who were active across all six quarters. Same data, two defensible cohort choices, a 48-point spread.

Year-over-year change in performance per engineer, two cohort definitions

Q2 2025 to Q2 2026. The fixed panel holds the same 388 engineers across all six quarters.

Two bars showing the same year-over-year comparison under two cohort definitions. The open cohort rises 130 percent and the fixed panel rises 82 percent, a 48-point spread that comes from cohort choice alone rather than from any difference in the underlying work.

Anyone presenting a before-and-after number needs to specify which cohort it came from, which is the sort of detail a CFO’s team will look for on the second read.

How nervous is your team about being measured? If the answer is very, the published stance is part of the purchase. DX and Swarmia give you a sentence to quote. Jellyfish gives you published guidance. Navigara lacks a per-developer throughput view. Faros AI and Waydev leave more of that work to whoever installs the tool.

Talk to us if you want to see what the pre-AI comparison looks like on your own repository history.