Assign every AI coding dollar to the team that spent it
Faros traces AI coding token spend to the teams, tools, models, and repositories behind it so you can implement accurate chargebacks and forecasting.
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Your AI coding bill arrives without allocations
AI coding providers bill the organization. Your budget is owned by teams. Connecting those dots means pulling a usage export from every tool, every month, and estimating the rest.
Unallocated tokens
Provider dashboards cannot report the team, service, or product that created the work
No chargeback path
Without per-team attribution, you cannot show a cost center what tokens it consumed
Unforecastable run rate
Human and agentic consumption, usage, and unit price move independently over time
Your AI spend should be higher
A blanket usage cap treats high-value work and wasteful token burn exactly the same. Caps do shrink the bill, but they slow down whoever reaches the ceiling first, which is usually the team shipping the most.
Know which spend is producing, which is not, and whose budget each one belongs to. Then shift your AI coding spend toward the work that ships the most per token.
See where the spend went, and where it’s headed
Faros connects AI token spend to the teams and services that generated it, so finance and builders are working off the same numbers.
See what’s driving AI coding spend
Break down token spend by team, tool, model, repository, and workload. Replace fragmented usage reports with one reliable view of where AI coding costs are accumulating.
Charge back what each team consumed
Create per-team statements built from session-level attribution, so that finance teams and builders see the same breakdowns.
Allocate budgets with evidence
Identify the projects and teams driving spend, and use efficiency patterns to plan, prioritize, and invest future AI coding spend.
Catch overruns while they're still small
Track each team's spend against its budget in real time, so that any team heading out of bounds surfaces immediately.
Forecast spend accurately
Decompose your spend history into the human and agentic builders driving that spend, so that each can be projected independently.
What’s new at Faros
The Speed Trap: 8 takeaways from our latest AI engineering research
AI made software development faster, but review gaps, QA bottlenecks, and rising incident volume reveal a new risk: the Speed Trap.
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Visualize your own spend, allocated
See how Faros attributes token spend across teams, tools, and models, and how closely the forecast tracks the invoice.