FinOps for AI
FinOps for AI applies the discipline you already use on cloud infrastructure to your fastest-growing line item: AI. As spend on Anthropic, OpenAI, Amazon Bedrock and Google Vertex climbs, GetFinOps gives you token-level visibility, per-team allocation, and automated optimization — reconciled against what the providers actually invoice you.
Why AI spend needs its own FinOps practice
AI cost behaves nothing like EC2. Providers bill input, output, and cache tokens at different rates; a single agent run can fan out into dozens of hidden sub-calls; and model pricing changes without warning. Treating the AI bill as one opaque number hides the waste. FinOps for AI itemizes every token by model, feature, and team so the spend becomes legible and controllable.
See every token, priced correctly
GetFinOps captures usage per model and prices it against a maintained rate catalog — including cache-creation and cache-read tokens that Anthropic reports separately from input. A nightly reconciler compares our figure to the provider’s own Admin-API total and flags drift, so your AI cost of goods is accurate enough to price a product on.
Cut AI spend automatically
The same agent that remediates cloud waste surfaces AI-specific savings: enable prompt caching to cut repeat-context input cost, route simple calls to a cheaper model, move non-urgent jobs to the Batch API, and release idle Bedrock provisioned throughput. Each is a one-click, reviewable action — not another dashboard chart.
How GetFinOps delivers FinOps for AI
Token-level cost by provider, model, and team across Anthropic, OpenAI, Bedrock and Vertex.
Cache-aware pricing reconciled nightly against the provider’s own invoice.
A plain-English daily brief that explains where the AI bill moved and why.
Query and act on your AI spend from Claude Desktop, Cursor, or Claude Code.
FinOps for AI — frequently asked questions
What is FinOps for AI?
FinOps for AI is the practice of bringing cloud financial management — visibility, allocation, forecasting, and optimization — to spend on AI and large language models. It treats tokens, models, and agents as first-class cost dimensions rather than folding them into a single opaque invoice.
How is AI spend different from cloud spend?
AI providers bill input, output, and cache tokens at different rates, pricing changes frequently, and one agent request can trigger many hidden sub-calls. That makes AI spend harder to attribute and forecast than steady-state infrastructure, so it needs token-level metering and reconciliation against the provider invoice.
Which AI providers does GetFinOps support?
GetFinOps tracks LLM and inference spend across Anthropic, OpenAI, Amazon Bedrock and Google Vertex, priced per model against a maintained rate catalog and reconciled against provider billing data.
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See FinOps for AI on your own cloud
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