LLM Cost & Performance Optimization
Most teams running production AI pay 3–10x what they should. We audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
- Outcome
- 30–60% LLM cost reduction in 4 weeks, documented
- Timeline
- 4–6 weeks
- Pricing
- $15–40k, or outcome-priced (25% of first-year savings)
- Buyer
- CTO, VP Eng, Head of Infra
The problem
You shipped AI features and the bill kept climbing. Nobody can say which prompts, models, or call patterns are driving spend — so every proposed cut is a guess, and latency complaints pile up on top.
What we do
- Instrument every LLM call: tokens, latency, cost, and quality, per route.
- Right-size models per task and add cost-aware routing with quality guards.
- Add prompt + response caching, request batching, and graceful fallback chains.
- Wire cost and latency dashboards so the savings stay visible after we leave.
What you get
01A documented 30–60% cost reduction with before/after numbers
02Cost-aware routing in production (fast-litellm or your stack)
03A live cost + latency observability dashboard
04A runbook so your team can keep tuning without us
Built on our open source
fast-litellm — Rust acceleration for LiteLLM — faster connection pooling, rate limiting, and memory-intensive workloads.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.