Client-side attribution
Wrap supported LLM clients with a single line so each call can be attributed to a customer without changing the vendor routing path.
TokenOps is a unit-economics platform for AI companies that attributes LLM spend by customer, reconciles vendor invoices, and shows per-customer gross margin. SDK wrappers, MCP tools, and workflow tools included.
TokenOps is a unit-economics platform for AI companies that turns LLM usage into per-customer cost and gross-margin data. It captures events at the client layer, reconciles those events against monthly vendor invoices, and shows how spend maps to the customers who incurred it.
The product is positioned for teams that need to answer profitability questions from actual usage rather than spreadsheet exports. It supports one-line wrappers for major LLM providers, exposes the data through a dashboard, API, and MCP tools, and is offered through an early-access flow with a free trial on real telemetry.
Source examples on the page show customer-level cost, revenue, and margin views, plus workflows for building per-customer P&Ls, reconciling invoices, detecting anomalies, and simulating price changes. The workflow framing suggests TokenOps is aimed at finance, operations, and engineering teams that need recurring visibility into LLM unit economics.
Wrap supported LLM clients with a single line so each call can be attributed to a customer without changing the vendor routing path.
Capture events, price them against tokens, and reconcile monthly vendor invoices so effective cost can be compared with the invoice total.
Break down spend, revenue, and margin by customer so teams can see which accounts are profitable and where loss-making usage is concentrated.
Expose financial data through a 30-tool MCP catalog plus four composite workflow tools for use in Claude Desktop, Cursor, Codex, or other MCP-aware clients.
Use composite workflows for per-customer P&L, invoice reconciliation, anomaly detection, and price-change forecasting.
Support TypeScript and Python SDK usage, including edge runtimes, with wrappers for Anthropic, OpenAI, Amazon Bedrock, Google AI, Vercel AI, and Azure OpenAI.
Track which customers are profitable by joining captured LLM usage to customer revenue and margin data for a given period.
Compare invoice totals with captured events to understand effective rate, drift, and savings across vendors and billing periods.
Watch per-customer cost run-rate and detect unusual spikes before the monthly invoice arrives.
Use MCP-aware assistants to answer finance questions such as customer margin, loss-making accounts, or cost breakdowns in plain English.
Model the impact of new seat-based or usage-based pricing against recent usage to see margin effects and potential churn risk.
TokenOps captures LLM events at the client layer, attributes spend by customer, reconciles vendor invoices against those events, and exposes the results through the dashboard, API, and MCP tools.
The source shows one-line wrappers for Anthropic, OpenAI, Amazon Bedrock, Google AI, Vercel AI, and Azure OpenAI, with support for TypeScript and Python.
You can install the SDK, wrap your LLM client, and pass a customerId on each call. The page says the first event can be seen in under five minutes.
TokenOps says it offers a 30-day free trial on your real telemetry with no card required. After the trial, it says plans are sized to usage, but no pricing details are published on the page.
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