The systems guide to production token optimization
The New Stack Boris Chabeda
Enterprise AI support and CI agent systems (Concierge and Pathfinder) saw rapidly growing latency and costs as autoregressive history re-billing compounded across multi-step runs. The article’s baseline pricing example is $3 per 1M input tokens and $15 per 1M output tokens. It proposes fixes including dynamic context injection/RAG, prompt compression (LLMLingua-2), strict JSON/schema enforcement, output token bounding, caching with up to 90% discounted reads, semantic caching, context compaction, and model cascading to keep token growth bounded.
Why it matters
When enterprise AI applications scale, they inevitably hit a wall. For many engineering teams, this wall is initially diagnosed as The post The systems guide to production token optimization appeared first on The New Stack.