Stop shadow AI from
burning your budget
Token budgets per tenant. Semantic caching that saves 40%+.
Per-team cost analytics. Know exactly who's spending what on which models.
The cost control gap
Most companies discover their AI spending problem too late.
Shadow AI spend
Teams calling LLM APIs directly with no governance. A runaway prompt loop burns $50K over a weekend.
No cost attribution
Finance asks "how much are we spending on AI?" and nobody can answer by team or project.
Repeated prompts
40% of LLM calls are semantically identical. You're paying full price for cached answers.
How gatez solves it
AI cost control built into the gateway layer.
Per-tenant token budgets
Pre-request check in Redis. Budget alert at 80%. Hard block at 100%. No overruns.
Semantic caching
Two-tier: Redis exact match + Qdrant similarity. 599 req/s on cache-hit path. 40%+ savings proven.
Per-model cost tracking
ClickHouse logs every token. Prompt vs completion split. Cost per provider. CSV export for finance.
Multi-model routing
Route to cheapest model for simple tasks, best model for complex ones. Fallback chains when providers fail.
Budget alerts
Notification at 80% threshold. Projected exhaustion date. Per-tenant dashboards. Catch burn rate spikes before month-end.
Usage analytics
Per-tenant, per-model, per-route breakdowns. Time-series charts. Drill into spikes. CSV export for billing systems.
Real-world example
How Synthara AI routes 2k req/s with cost control.
Synthara AI — AI coding assistant
Synthara routes 2k req/s across OpenAI, Anthropic, and Gemini through gatez.
- Default model:
gpt-4o-mini(cheapest, fastest) - Complex tasks:
claude-sonnet(best for code generation) - Fallback:
gemini-flash(if OpenAI/Anthropic down)
Results after 1 month
When staging burned 3x normal tokens, the alert caught it before month-end. Token budgets per team prevent surprises.
Stop paying for repeated prompts
Deploy gatez in 5 minutes. Start tracking every token, every model, every tenant.
Free forever. Apache 2.0 license. No credit card required.