AI cost runs through the tools your teams use and the apps you ship — seats, tokens, compute and data. FinOpsly ties every stream to one owner, one budget, one policy.
AI cost governance means attributing, budgeting and controlling AI spend before the invoice — across the tools your teams use and the applications you run in production.
Copilot, Cursor, ChatGPT Enterprise and Claude are billed as per-seat licenses plus per-user tokens. FinOpsly resolves each seat and its token draw to a user, team and line of business, flags seats idle past 30 days, and sets a per-user budget that alerts the owner on overage.
See the Workforce AI use caseOne production request spends on three meters at once: tokens (GPT-4o, Claude, Bedrock), compute (GPU-seconds on EKS or SageMaker), and data (Pinecone, Snowflake, Databricks reads). FinOpsly joins all three into one cost-to-serve, keyed to app, customer and end user.
See the Application AI use caseCopilot here, Cursor there, ChatGPT Enterprise and Claude on the side. FinOpsly pulls every AI seat and API token one engineer expenses into a single per-user view, ties it to their team, and sets a monthly budget that pings the owner the moment usage runs over.
It won't switch the tool off — these are third-party SaaS, not your app. It makes every AI dollar visible, owned and budgeted, so nothing sprawls unseen.
Acme Corp is your customer; jdoe is a user there. FinOpsly joins the tokens, compute and data that one user burns over a month into a single cost-to-serve, then rolls it up to the customer. No shared bills, no guessing which account is underwater.
Trimming, batching and caching all help. The biggest AI savings come down to one call: which model runs the workload. FinOpsly prices that workload across every model — frontier, lighter, open-weight — side by side. You see what each one costs and pick the fit. FinOpsly sizes the cost; you own the fit.
The same workload can run $8,000 or $48,000 a month depending on the model behind it. FinOpsly runs the numbers across frontier, lighter and open-weight models — GPT-4o, Claude, GLM, Kimi and more — so your developers model the trade-off and choose. FinOpsly sizes the cost. You own the fit.
Below the model call, FinOpsly surfaces the signals behind every dollar — cache hit ratio, cost per call by model, tokens and GPU utilization — so your team knows exactly where to act.
See what every call costs, split by model and workload. The routine work quietly running on a premium model shows up fast.
See your cache hit ratio and how much repeat traffic re-pays full price.
See which latency-tolerant calls could move to batch, and what the shift is worth.
See input and output tokens per call, and where oversized context inflates every request.
See utilization on self-hosted and inference GPUs, by workload and environment.
See volume and throughput per workload, and the breakeven for committed capacity.
One view. One policy. One owner.