Play 14
Cost-Optimized AI Gateway
Medium🔧 Skeleton
APIM-based AI gateway with semantic caching, token budgets, and load balancing.
Route AI requests through APIM with semantic caching (Redis stores embeddings of recent queries — similar questions get cached responses). Token budgets per tenant prevent runaway costs. Multi-region load balancing with fallback chains ensures availability. Built-in analytics track cost per team.
Architecture Pattern
Semantic caching, token metering, load balancing, FinOps
Azure Services
API ManagementRedis CacheAzure OpenAI (multi-region)
DevKit (.github Agentic OS)
- agent.md — FinOps guardian persona
- instructions.md — caching strategy
- plugins/ — cache handler, budget enforcer, load balancer
TuneKit (AI Config)
- config/gateway.json — caching rules, token budgets, fallback chains
- config/routing.json — load balancing, model selection
- config/pricing.json — cost limits per tenant
Tuning Parameters
Token budgets per tenantCache TTL and similarity thresholdFallback chainsRegion routing rulesModel selection per tier
Estimated Cost
Dev/Test
$80–200/mo
Production
$1K–5K/mo