LLM Cost & Performance Optimization
Last verified: June 2026· engagement
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.
How it fits together
What you get
Built on our open source
fast-litellm — Rust acceleration for LiteLLM — faster connection pooling, rate limiting, and memory-intensive workloads.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
Guides for this work
How to reduce LLM API costs
Most production LLM spend is avoidable: it comes from sending the wrong model too many tokens, too many times. Cutting it is an engineering problem — routing, caching, batching, and context control — not a matter of waiting for prices to drop.
LLM costWhy are LLMs so expensive?
LLMs are expensive because you pay per token on every call, output usually costs more than input, and agents re-read a growing context on every step — so cost compounds far faster than the work appears to grow.
LLM costHow to reduce LLM latency
LLM latency is two separate problems — the wait for the first token and the time to stream the rest — and each has its own set of fixes.
Hiring & partnersHow to hire an LLM cost optimization consultant
Hire an LLM cost consultant when your bill is large enough to matter but your team is too busy to instrument, route, and eval their way out of it themselves — and only hire one who leaves you owning the result.
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.