Solution · AI Operations
What does it cost to run your AI in production? The bill you didn't see coming — and the function that keeps it under control, month after month
You budget for building AI once. You pay to run it every month, and that bill grows on its own: what was cents in the demo becomes thousands a month, split across five vendors nobody can break down. Controlling the cost of running AI isn't squeezing spend one afternoon — it's a function that runs: measure, attribute, budget, optimize and govern, so the AI line stops being a month-end surprise.
The problem
The cost of running AI isn't the build invoice. It's the one that lands every month, grows on its own and nobody can break down.
- The AI bill is a line that climbs every month and nobody can split it: which use case, which model, which team eats each dollar is a mystery until someone asks and there's no answer.
- Spend spiked the moment you went from pilot to real volume: what cost cents in the demo is thousands a month in production — money nobody budgeted, because in the pilot you couldn't see it.
- There's no cap and no alarm: a badly designed prompt, an agent loop or a traffic spike can multiply a full day's spend with nothing tripping until the invoice arrives.
- Every vendor bills differently — tokens, calls, GPU-hours, vector storage — and there's no unified picture: the real cost of running AI lives split across five invoices that reconcile with no budget.
Cost of staying the same
Left uncontrolled, AI cost grows faster than its value — and the problem isn't just overpaying: it's not knowing which of your use cases pays for itself and which is bleeding you. The CFO finds out after the fact, on an invoice that can't be undone, and the default reaction is the worst one: freezing the rollout out of fear of the bill. Then the profitable use case stops right next to the one that isn't, because nobody has the numbers to tell them apart. And since models and prices change every quarter, a cost snapshot from three months ago is already stale: what paid off yesterday may not today, and the other way round.
The solution
We set up and run the AI cost-control function — AI FinOps: measure, attribute, budget, optimize and govern — so running AI stops being a surprise invoice and becomes a governed line
- 1We instrument spend: every model call, every token, every GPU-hour and every vector query is measured and tagged by use case, team and model. The bill stops being a black box and becomes a table you can read.
- 2We attribute and budget: we split real cost by use case and put a budget on it, with alerts and caps that trip BEFORE a loop or a spike eats the month — not when the invoice lands and there's no way back.
- 3We optimize continuously: we route each task to the cheapest model that solves it, cache what repeats, batch what isn't urgent and trim the prompt that overpays — always against the quality you already measure, never blind.
- 4We govern change: when the vendor raises prices, ships a new model or volume shifts, we recompute and adjust. Control isn't a one-month snapshot — it's a function that keeps running when the ground moves underneath.
What changes
What you stop losing
AI spend stops being a black box: every dollar is attributed to a use case, a team and a model, so you know which AI pays for itself and which is bleeding before you decide where to scale.
Mechanism
Bill shocks stop happening: budget, alerts and caps stop an agent loop or a traffic spike BEFORE they eat the month, instead of finding out once it's already billed.
Mechanism
Scaling stops being scary: with cost per use case in plain view, you grow what pays and cut what doesn't, instead of freezing all AI out of caution.
Mechanism
What we measure: cost per use case and per 1,000 responses, share of spend attributed vs. unassigned, variance against budget per month, and savings freed by routing and caching with no loss of quality.
What we measure
Spec sheet
- Work it removes
- letting the cost of running AI in production grow with no control, no attribution and no cap, only to be discovered at month-end on an invoice that can't be undone
- Typical setup
- 2–4 weeks
- Input
- real spend scattered across your model providers', cloud, GPU and vector-store invoices — untagged, unattributed and unbudgeted
- Output
- AI cost measured and attributed by use case and model, with budgets, alerts and caps running, and continuous optimization that lowers spend without touching the quality you measure
- Works with
- OpenAIAnthropicAzure OpenAIAmazon BedrockGoogle Vertex AI
- Can connect to
- Your cloud and inference infrastructureYour vector store and storageYour billing and FinOps tooling
- What we measure
- cost per use case and per 1,000 responsesshare of spend attributed vs. unassignedvariance against budget per monthsavings freed by routing and caching with no loss of quality
- Good fit for
- companies already running AI in production with a bill that grows every month, who want to measure, attribute and budget it without stalling the use cases that do pay off
- Not a fit for
- anyone still in a pilot with no real spend volume, or looking to cut cost by dropping quality instead of governing spend
Frequently asked questions
Your provider shows you ITS invoice: the tokens you spent with them. The real cost of running your AI lives split across several vendors — two or three models, the cloud, the GPUs, the vector store — and none of them has the full picture or can break it down by use case. What we build is the missing layer: it unifies everyone's spend, attributes it to what generates it, and puts budgets and caps on it. The provider dashboard is one piece; the function is the whole.
No. We always optimize against the quality you already measure, never blind. Routing a simple task to a cheaper model, caching what repeats or batching what isn't urgent lowers cost without the output getting worse — and if the cheap model doesn't cut it in a given case, it stays on the expensive one. The goal isn't to spend less at any price: it's to stop overpaying for what doesn't need it.
With caps and alerts that act before, not reports that arrive after. Each use case carries its budget and its limit; if a loop, a badly designed prompt or a traffic spike starts running up consumption, the alert trips and the cap stops spend in the moment — not when it's already on the invoice. The month-end shock turns into a heads-up in time.
The ones you already use. We work over OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock, Google Vertex AI and your cloud and inference infrastructure, unifying everyone's spend into a single picture. We don't ask you to switch provider or model: we give you cost control over the stack you already run in production.
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You’ve pinned the problem. We ship the fix and leave it measured.