LLM Cost & Latency Optimization
Cut token spend and response time with measurement, routing, caching and context engineering, never by quietly degrading quality.
Sounds familiar?
- 01The monthly model bill is growing faster than usage.
- 02P95 latency makes the product feel slow, and nobody knows which component to blame.
- 03Every request sends the same 40k tokens of context “just in case.”
- 04A cheaper model was tried once, quality dropped, and the idea was abandoned.
What it costs to leave it
- Unit economics make growth unaffordable or uninvestable.
- Latency budgets are spent on the wrong components.
- Quality silently erodes when cost cuts are made without evaluation.
What the engagement covers.
Measurement first: per-request cost/latency attribution across the pipeline.
Context engineering: trimming, structuring and caching what you send.
Semantic and deterministic caching where behavior allows it.
Model routing: right-sizing models per task, with quality gates.
Streaming and interaction redesign to cut perceived latency.
How the engagement runs.
Attribute cost and latency per stage of every request.
Find the top spend/latency drivers and their quality constraints.
Routing, caching, context and interaction changes, gated by evaluation.
Confirm savings and quality on production traffic, then hand over.
Deliverables
Cost and latency attribution report
Optimization backlog ranked by expected impact
Routing/caching design with documented quality constraints
Before/after measurements on agreed evaluation sets
Ongoing budget and SLO recommendations
A good fit when
- Products with meaningful per-request volume or expensive context patterns.
- Teams preparing for scale where unit economics matter.
- Platforms with latency-sensitive user experiences.
Probably not, when
- Zero-traffic prototypes; there is nothing meaningful to optimize yet.
- Teams seeking savings without any willingness to change behavior.
Common questions.
Do you only work with one model provider?
No. Model choice is an engineering decision per task. We have no vendor incentive, and routing across providers is often part of the answer.
How much can you save?
That depends entirely on your workload. We do not quote invented percentages; we measure your pipeline first and then bound what each change can achieve.
Will optimization degrade quality?
Not silently. Every optimization is gated by the same evaluation used for releases, so tradeoffs are explicit and deliberate.
Can you work with our existing gateway or proxy?
Yes. Attribution and routing usually integrate with the gateway you already run rather than replacing it.