AI teams · Cost engineering

Tokens are a unit of work. Treat them like one.

Apinizer's AI Gateway counts prompt tokens, completion tokens, cache savings, and fallbacks per call. Every number rolls up by consumer, intent, model, and time — the numerator and denominator of every cost decision.

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Token economics — For AI teams use case overview from Apinizer. — For AI teams · Token economics

The problem

If you can't attribute a token, you can't optimize a token.

Vendor invoices give you a monthly total. The total tells you nothing about which feature, which team, or which prompt drove the spend. Engineers can't optimize what they can't see. Apinizer captures every token at the gateway: prompt and completion, cached and fallback, tagged to consumer, intent, and model — in the same Elasticsearch the rest of the platform uses.

Capabilities

What Apinizer does here

Per-call token accounting

Prompt tokens, completion tokens, total tokens, cost — captured per call. No invoice-driven retrofits at month-end.

Multi-axis attribution

Consumer, project, intent, model, region, env — every call tagged. Aggregation is a saved query, not a finance project.

Budgets and quotas

Hard and soft caps per consumer, project, or intent. Burst allowance for spikes; throttle when the cap hits.

Cost-per-intent dashboards

What does 'summarize this' actually cost? Sum prompt + completion tokens across a million calls; rank by intent, by team, by model.

Cache and routing savings

See savings explicitly — how much would have been spent without the cache, without smart routing. Justify the platform with the numbers it generates.

Audit-grade evidence

Token accounting joins the audit ledger. Finance, engineering, and audit see one truth.

Use cases

In production, this looks like…

Banking
Istanbul bank attributes 100% of AI cost back to product squads
Every squad sees its own AI burn in the existing FinOps dashboard. Cost showbacks land monthly; the central platform reclaims its bonus.
100% attributed
Insurance
Frankfurt insurer ranks intents by cost-per-intent
'Summarize claim' costs €0.04 per call; 'analyze adverse signal' costs €1.20. Cheap intents on the small model; expensive intents capped and reviewed.
Public sector
Paris ministry budgets AI per directorate quarterly
Each directorate has a quarterly cap. Real-time burn dashboards remove all surprise from the close.
Retail
Madrid retailer quantifies cache savings in the same view as spend
Spend €X; cache saved €Y. Marketing presents the platform's ROI to finance with one chart.
Savings visible monthly
Media
Milan publisher shifts to smaller models for short-form prompts
Cost-per-intent showed short-form prompts were 90% of volume and 30% of cost. Routing shifted them to a 7B model; cost per call dropped 70%.
Telecom
Amsterdam carrier alarms on burn-rate anomalies
Anomaly detector watches token burn vs. baseline. A prompt loop fired at 3am triggered alarm; on-call killed it in 9 minutes.
Healthcare
Prague hospital ties AI spend to clinical workflows
Every token attributed to the workflow that triggered it. Workflow owners see cost-per-encounter and optimize their prompts directly.
Energy
Baku utility caps SCADA-agent token burn per shift
Shift-level quotas. A faulty agent loop would have run up €4k overnight; throttle stopped it at €200; on-call rolled back the deploy.
€4k → €200 saved