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Wrap your SDK
Use meterAnthropic() and meterOpenAI() to wrap your existing clients in one line. Production traffic continues without disruption.
AI FinOps
AI Cost Tracking for OpenAI and Anthropic with Per-Feature Attribution
Stop budgeting AI on monthly invoices. TurboFinOps proxies your OpenAI and Anthropic clients with a drop-in SDK that streams every call into a per-feature ledger and recommends model swaps that pay for themselves.
What blocks savings today
AI invoices arrive monthly with no breakdown by feature, customer or model.
Engineering ships an Opus call by accident and only finance notices, four weeks later.
You cannot prove AI cost per customer to bill, charge back or churn-protect.
What TurboFinOps changes
Wrap your existing OpenAI / Anthropic SDK with one line and start streaming usage events.
See per-feature, per-customer and per-trace cost within sub-minute latency.
Get specific recommendations: "Feature /summary used Opus 4.7 for 80% small completions — switch to Sonnet 4.6 saves $1,240/mo."
Workflow
Built for governed execution, not passive reporting.
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Stream usage events
Every API call emits a non-blocking usage event with model, tokens, cached tokens, feature label and end-user reference.
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Cost computed at ingest
Versioned per-1k-token pricing applies the rate that was in effect when the call ran, so historical reports stay canonical.
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Act on recommendations
Model-switch recommendations identify features where a smaller model would save money with negligible quality loss.
Core capabilities
Each capability is designed to help technical teams validate impact, preserve control and prove outcomes.
Drop-in SDK for OpenAI + Anthropic
Per-feature, per-customer, per-trace attribution
Versioned model pricing (Anthropic 4.x/3.x, OpenAI 4o/4.1/o-series)
Cached-token aware cost calculation
Opus → Sonnet model-switch recommender
Daily cost trend + token volume charts
FAQ
Why is invoice-level AI cost tracking not enough?
Invoices are monthly, total-only and exclude failed retries. They cannot answer "which feature, which customer, which model" — and that is the only granularity that actually drives optimization decisions.
Will the AI meter add latency to my calls?
No. The SDK buffers events in memory and flushes asynchronously every 5 seconds or when the batch fills (100 events). Network failures re-buffer, never throw.
How does the model-switch recommender work?
It identifies features where >70% of calls produce <800 output tokens — a signal that a smaller model can handle the workload with negligible quality loss. It then quotes the actual savings in USD using current versioned pricing.
TurboFinOps
Start with one cloud scope. Prove savings fast.
Connect AWS, Azure, or GCP and get actionable findings, score trends, and auditable remediation paths in minutes.
Built for FinOps, governance and audit workflows