Inference Optimization
Cut inference cost.
Keep the quality.
Cirra is a hands-on inference optimization consultancy. We measure, redesign, and maintain your LLM serving infrastructure — typically cutting inference costs 30–50% without sacrificing quality.
Your traffic · your evals · measured savings · quality held
Relative cost
100%
Quality score
94
p95 latency
180ms
The problem
Your inference stack is optimized for last month's ecosystem.
Models shrink, quantization improves, serving engines advance, and hardware economics flip — monthly. Most teams pick a stack once, then watch cost drift while quality gates stay informal. Nobody owns the continuous question: is this still the cheapest way to serve this workload at this quality?
The chain Cirra measures end-to-end
Services
Show up. Measure. Fix. Keep it fixed.
Today the product is the engagement — expert hands on your serving stack, not a dashboard you configure alone.
Wedge
Optimization Sprint
We benchmark your current stack against your real production traffic, test candidate alternatives, and deliver a written report — baseline, savings ranked by impact, and implementation guidance.
- ▸ Production-derived eval set
- ▸ Models, serving configs, hardware compared
- ▸ Meaningful savings — or you owe nothing
Execute
Implementation / Migration
We execute the recommendations: tune your serving stack, or prove an OSS alternative via shadow deployment and gradually shift traffic with automatic fallback.
- ▸ vLLM / TensorRT-LLM tuning
- ▸ API-to-self-hosted cutover
- ▸ Runbooks, monitoring, team training
Ongoing
Retainer
A fractional inference engineer on call. We re-benchmark quarterly, evaluate new model releases against your workload, and re-tune as traffic and the ecosystem move.
- ▸ Quarterly re-benchmarks
- ▸ New-model evaluation
- ▸ On-call tuning as load evolves
30–0%
typical inference cost reduction
0 wk
fixed-scope sprint turnaround
0
quality sacrificed for savings
1
expert owning the full chain
Evidence, not anecdotes
Not a slide deck. A measured engagement on your traffic.
Every recommendation is proven against an eval set built from your production requests. Candidate stacks are benchmarked side by side. Migrations run in shadow before traffic moves. If a sprint cannot identify meaningful savings, you owe nothing.
Example engagement outcome
A sprint against production traffic found a quantized self-hosted path that cut relative inference cost by 42% while holding the quality score within 0.3 points of baseline.
Shadow deployment validated the stack for 9 days before gradual cutover. Automatic API fallback stayed armed. Handoff included runbooks and on-call monitoring.
→ retainer kept the stack current through three model releases
Who we work with
One expert connection from tokens to the invoice.
ML Platform
Know whether the model, the serving stack, or the hardware is burning the budget.
Infra & SRE
Get a measured migration path — shadow first, cutover with fallback, never a leap of faith.
Applied AI
Cut inference spend without watching quality silently degrade on your real traffic.
FinOps & Leadership
Turn GPU and API invoices into a ranked, defensible list of savings you can act on.
Prove it on your traffic.
A fixed-scope optimization sprint on your production workload. Ranked savings, quality held constant — and if we cannot find meaningful reduction, you owe nothing.