TokenSpend
Pricing
- Model
- Usage-Based
Summary
The finance team asks how much you spent on AI last quarter, and you have a number — but no story about what it shipped. TokenSpend exists to close that gap.
TokenSpend routes requests to Claude, GPT, Gemini, and open models through a single endpoint, then ties the resulting usage back to Git branches, commits, and merged pull requests. The spend review breaks into four buckets — shipped, in-flight, unmatched, and other — so you know which AI spend became code and which evaporated. Attribution confidence is shown on every record, not buried. The wall appears at the edges: spend that never touches a PR shows up as unmatched after 30 days, and teams doing significant non-PR work — scripts, notebooks, ad-hoc queries — will see that bucket grow with no way to resolve it.
Bottom line: The right pick for an engineering org that needs to show finance exactly what their Claude spend shipped; a poor fit if your team's AI usage lives outside pull request workflows, where unmatched spend accumulates without resolution.
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Pros
Sign in to edit- Single routing endpoint across 52 models from 14 vendors, so switching models or adding a new provider does not require changes to calling code across every team.
- PR-level spend attribution tied to branch and SHA, which means you can show finance not just what was spent but what percentage became merged code — without building a custom reconciliation script.
- Confidence scoring on every attribution record, so unresolved spend is visible and flagged rather than silently folded into totals that overstate your shipped rate.
- Metadata-only capture — token counts, timestamps, and git refs — so the platform can be deployed across a fleet without routing proprietary source code or prompts through a third-party service.
- Audit-ready export of reconciliation logs, which means FinOps and finance teams get a structured artifact instead of a screenshot of a dashboard when budget reviews come around.
Cons
Sign in to edit- Spend that never touches a pull request — data science notebooks, ad-hoc CLI prompts, internal tooling scripts — lands in the unmatched or other bucket after 30 days with no resolution path. Teams with significant non-PR AI usage will see that bucket grow and have no mechanism inside the tool to categorize or tag it further.
- SHA-level attribution requires installing the capture plugin beyond the Quick Start integration. Teams that stop at the GitHub App connection get correlation, not confirmed attribution — a meaningful accuracy gap for any finance audience expecting audit-grade numbers.
- SOC-2 certification is on the roadmap, not in place. Security-first organizations in regulated industries that require a current SOC-2 report before onboarding a SaaS tool will need to wait or route through a different vendor that already holds the certification.
- No self-hosted deployment option exists. Teams under data-residency requirements or air-gap policies that prohibit outbound metadata to third-party SaaS cannot use the platform at all — the architecture requires routing through TokenSpend's endpoint.
About
- Platforms
- Web
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-08-17T00:23:44.258Z
Best For
Who it's for
- Engineering teams using multiple LLMs
- Organizations needing spend attribution to code
- FinOps and finance teams monitoring AI budgets
What it does well
- Track AI spend across models and teams
- Reconcile usage to merged pull requests
- Measure ROI of AI coding tools
- Generate audit-ready reconciliation reports
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is TokenSpend free?
- TokenSpend has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is TokenSpend open source?
- No — TokenSpend is a closed-source tool. Source code is not publicly available.
- Does TokenSpend have an API?
- Yes. TokenSpend exposes a developer API. See the official documentation at https://tokenspend.dev for details.
- What platforms does TokenSpend support?
- TokenSpend is available on: Web.
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The finance team asks how much you spent on AI last quarter, and you have a number — but no story about what it shipped.
TokenSpend routes requests to Claude, GPT, Gemini, and open models through a single endpoint, then ties the resulting usage back to Git branches, commits, and merged pull requests. The spend review breaks into four buckets — shipped, in-flight, unmatched, and other — so you know which AI spend became code and which evaporated. Attribution confidence is shown on every record. Spend that never touches a PR shows up as unmatched after 30 days.
How it works
The vendor states that metadata-only capture records token counts, timestamps, and git refs. SHA-level attribution requires installing the capture plugin beyond the Quick Start integration. Teams that stop at the GitHub App connection get correlation, not confirmed attribution.
Who it is for / who should skip it
Engineering teams using multiple LLMs and organizations needing spend attribution to code will see value from the single routing endpoint and PR-level attribution. FinOps and finance teams monitoring AI budgets can generate audit-ready reconciliation reports. Teams with significant non-PR AI usage such as notebooks or ad-hoc scripts should skip it, as that spend lands in the unmatched or other bucket after 30 days with no resolution path inside the tool.