LinearB alternatives: what to look at if you need AI cost attribution

If AI cost attribution is the requirement, the shortlist is Faros AI, Jellyfish, Swarmia, Waydev and Navigara. Faros AI publishes the group’s strongest capability, including a Token Attribution Ledger. LinearB’s AI cost attribution is not published, and its only published cost mechanic is LinearB’s own AI credits, so a team whose renewal hinges on a token figure has to look past it. Everything else LinearB does well is unaffected by that gap.

What LinearB does well, and it is a lot

LinearB reads Git and pull request telemetry alongside trackers and CI/CD systems, and reports on cycle time, DORA metrics, and pull request size.

The distinguishing asset is the comparison set. LinearB benchmarks against 8.1M+ pull requests, a large reference population that is hard for a newer entrant to match. If the question is whether a 4-day cycle time is good, that comparison answers it with more authority than an internal average can.

LinearB is team-first with role-based access, and concedes that a manager could misuse the data. Pricing is published at $29 and $59 per user per month, which puts it in the minority that let a buyer price the thing without a call. The tiers are built for teams of 50+ and 100+ developers, which is the practical catch, and they exclude most orgs with under 50 engineers.

None of that changes if you need token attribution. It simply does not publish one.

Why the token figure became a requirement

The seat line was no longer the whole bill.

An agentic workflow that reads a large codebase before writing anything consumes tokens at a rate unrelated to the number of people who hold licenses. Spend scales with how the tools are used, which means a 30-engineer team can produce a larger bill than a 200-engineer team running chat-style completion. Finance notices this in the second quarter and asks for attribution, usually by team, sometimes by tool, occasionally by person.

Navigara publishes no benchmark for what that spend should be, so any figure quoted here would be invented. What the published research does show is why the return side needs its own measurement. Across 699 engineers, 137,592 qualifying commits, and 65 public repositories in the Q2 2026 study, commits per engineer increased by 14.9% year over year, while performance per commit increased by 100.4%. Attribution tells you what the tools cost. Only a scored baseline tells you what changed.

Carry both caveats when citing it: public repositories only, and the study does not quantify what share of the level shift, if any, is attributable to assistant adoption.

What each alternative attributes

Faros AI is the strongest published capability in the category, and it deserves the top slot even though it competes with us. It claims 60+ sources, publishes a Token Attribution Ledger, and reports a cost-per-verified-outcome figure. If the sole requirement is token attribution depth, start here. It no longer publishes pricing or capitalization pages, and the old capitalization URLs no longer resolve to capitalization content, so that feature’s status is unclear.

Jellyfish publishes AI spend attribution at the token level, reported by tool, team, or initiative, and joins it to roster and payroll so the figure lands in dollars that finance can reconcile. It reports at the individual level and has its own published guidance against using metrics for performance evaluation. No published pricing.

Swarmia publishes AI cost by person per tool, with the AI cost module priced at $5 per developer per month on top of $45 or $55 tiers. Its broader headline is “Investment Balance: allocation by time and money,” and its stance is explicit: “Say bye to leaderboards and stack ranking.” Free to 9 developers and the only platform here claiming a SOC 1-audited capitalization approach.

Waydev publishes AI spend attribution by tool, team, and seat, at $29 or $49 per active contributor per month, billed annually. Its headline Impact score is a composite derived in part from lines of code, a methodological choice worth understanding before you adopt the number.

Navigara measures token and tool costs against the change in throughput it produced, comparing the current quarter to the team’s own pre-AI window. Attribution runs at team and repository level, and there is no per-developer view. Pricing is published. It does not publish a capitalization feature.

Side by side

The column worth reading twice is attribution level. Only Swarmia publishes spend attributed to a named person, and DX publishes token-level spend broken out by contributor. If your org has decided against per-individual reporting, that decision narrows this list before price does.

If the cost figure is already in hand and the missing half is what it bought, connect a repository and the pre-AI window scores in the first pass.

When to keep LinearB

Keep it when cycle time and DORA are the numbers your org runs on and the benchmark comparison is load-bearing.

A director whose mandate is to reduce lead time from six weeks is better served by LinearB’s stage-level breakdown across 8.1M+ pull requests than by a token ledger. Pipeline measurement is what LinearB is built for, and the AI cost gap is orthogonal to that job.

The reasonable configuration for a team above the 50-developer minimum is often LinearB for delivery-pipeline measurement plus something else for the AI cost and baseline question. Two tools answering two questions beats one tool asked to answer a question it does not publish an answer to.

How to choose

Do you need per-person attribution, or does per-team satisfy finance? Ask finance directly. Most requests that arrive as “per developer” are satisfied at the team level, and answering that first eliminates half the list.

Do you need the cost side, the return side, or both? Four of these five publish strong cost attribution. The return side needs a scored pre-AI baseline, which is a different measurement.

Are you above the team sizes those tiers are built for? LinearB’s 50+ and 100+ team-size tiers and Swarmia’s free tier to 9 developers mean team size, not features, decides several of these.

Talk to us if you want the throughput side measured against your own history.