Executives · AI economics

Pay for what you use. See where it went.

Apinizer's AI Gateway routes calls to the cheapest model that satisfies the SLA, caches answers semantically, enforces per-team budgets, and attributes every token to the consumer that asked for it.

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AI cost control — For executives use case overview from Apinizer. — For executives · AI cost control

The problem

AI bills don't show up in the procurement cycle. They show up in the close.

Engineers point a service at a frontier model. Token meters spin. The bill is fine in week one, alarming in month one, and a board-level conversation by quarter end. By then attribution is impossible — twelve teams share one API key, and nobody knows which prompt cost what. Apinizer puts every AI call through a gateway that meters, attributes, caches, and routes — before the spend happens.

40-70%
spend cut
semantic cache + smart routing
100%
calls attributed
per team / project / consumer
Real-time
budget alarms

Capabilities

What Apinizer does here

Semantic caching

Equivalent prompts hit cache, not the model. 'Summarize this contract' on the same contract serves from cache; the underlying model is never called twice for the same answer.

Smart model routing

Route simple intents to small / cheap models, complex intents to frontier models. The gateway picks per call, not per service.

Per-team budgets

Hard caps and soft alarms per project, team, or consumer. When a budget is reached, the gateway throttles or fails over — no surprise.

Per-call attribution

Every token tagged with consumer, project, and prompt fingerprint. Cost shows up in the dashboard the day it was spent.

Token economics

Prompt tokens, completion tokens, cache hits, fallbacks — broken out by model, endpoint, and consumer. Finance gets the same view as engineering.

Quotas with policy

Power users get more; weekend batch jobs get less. Quotas are a policy you write once and the gateway enforces on every call.

Use cases

In production, this looks like…

Banking
Istanbul bank cuts AI spend 58% on customer-service summaries
Semantic cache absorbs 71% of duplicate prompts. Routing sends short intents to a smaller model; only escalations reach the frontier provider.
−58% monthly
Manufacturing
Stuttgart OEM caps each engineering team's monthly AI budget
Per-team budgets at €X. Alarms fire at 80%; throttle at 100%. The CFO sees burn rates instead of surprise invoices.
E-commerce
Amsterdam marketplace attributes AI cost back to product squads
Every token is tagged with the squad that asked for it. Cost showbacks appear in the existing FinOps dashboard.
Insurance
Paris insurer routes claim summarization to a regional model
EU-hosted model handles 92% of summaries; frontier provider used only for adversarial or multi-language cases. Latency improves and spend drops together.
−42% spend, +18% latency
Telecom
Madrid carrier shifts batch enrichment to off-peak smaller models
Time-of-day policy: night batch on the cheap tier, daytime live traffic on the premium tier. Cost-to-serve drops without an SLA change.
Public sector
Prague ministry budgets AI per department in advance
Department quotas published. Departments see real-time burn; over-runs require a written request, not a surprise email.
Government
Riyadh agency uses a national-language model for 80% of calls
Routing prefers the national Arabic model first; falls over to international providers only on miss. Sovereignty and cost align.
Retail
Milan retailer cuts product-description AI cost 64%
Semantic cache deduplicates near-identical SKU prompts. Routing handles long-form on a frontier model; short-form on a 7B open model.
−64% spend