cirra

How it works

The method behind every measured saving.

Cirra does by hand what the future platform will automate: sample your workload, build quality evals, run configuration experiments, and personally track when your stack goes stale.

01your real traffic

Profile

We sample production requests, measure the current serving stack, and establish a cost-and-quality baseline — model choice, quantization, batching, caching, hardware, and API versus self-hosted economics.

workload sampling · serving telemetry · spend attribution

02quality that matters

Eval

From a slice of your real traffic we construct an evaluation set that reflects what users actually ask — so every candidate change is judged on your workload, not a public leaderboard.

production-derived evals · task-level scoring · regression gates

03candidate alternatives

Benchmark

Provider-side options — prompt caching, model routing, cheaper hosted models — are tested alongside self-hosted candidates, quantized variants, and hardware choices. Same evals. Same latency bar. Winner is whichever path wins on your traffic.

provider opts · vLLM · TensorRT-LLM · quantization · hardware

04cost impact first

Rank

Opportunities are ranked by verified savings potential and implementation risk. You get a written report: baseline, ranked findings, and concrete guidance — not a slide deck of vague advice.

impact-ranked opportunities · implementation notes · trade-offs

05we do the work

Implement

We tune the serving stack or run a full API-to-self-hosted migration: prove the alternative on your traffic with a shadow deployment, then shift load gradually with automatic fallback to the prior path.

shadow deploy · gradual cutover · automatic API fallback

06your team owns it

Handoff

Every engagement ends with runbooks, monitoring hooks, and training — so your team can operate the stack without us sitting in the critical path.

runbooks · monitors · operator training

07fractional inference eng

Maintain

The optimal setup drifts as models and hardware move. On retainer we re-benchmark quarterly, evaluate new releases against your workload, re-tune as traffic evolves, and stay on call.

quarterly re-benchmarks · new-model evals · on-call tuning

The unit of trust

Every sprint is a contract on your numbers.

The deliverable names the baseline stack, the eval protocol, each opportunity with estimated cost impact and quality delta, and clear implementation guidance. If we cannot identify meaningful savings, you owe nothing for the sprint.

  • ✓Recommendations ranked by verified cost impact
  • ✓Quality deltas measured on your production-derived evals
  • ✓Implementation notes ready for your team — or for us
sprint-report.yamlranked by impact
1# Optimization Sprint — findings
2client: acme-inference
3baseline:
4 stack: api/gpt-class · uncached
5 quality: 94.2
6 relative_cost: 100
7recommendations:
8 - id: provider-cache-and-route
9 path: stay_on_api
10 impact: -38% cost
11 quality_delta: +0.3
12 - id: cheaper-hosted-model
13 path: stay_on_api
14 impact: -22% cost
15 - id: shadow-self-host
16 path: migrate
17 impact: -48% cost
18winner: provider-cache-and-route
19status: ready_for_implementation

How the work runs

Manual today. Compounding toward the platform.

Client-specific validation stays on your infrastructure. Reusable benchmarking compounds into the library that makes each next engagement sharper.

Workload sampling

Your traffic

We profile real production requests — not synthetic prompts — so every recommendation reflects how your users actually use the model.

Eval construction

From production

A quality harness built from your traffic sample. Candidates pass or fail against the tasks that matter to your product.

Config experiments

Rented GPUs

Reusable benchmarking on short-lived GPU capacity. Client-specific validation runs on your infrastructure and your bill.

Shadow deployment

Your cloud

OSS or tuned alternatives prove themselves on live traffic in parallel before any cutover — with automatic fallback armed.

Serving tuning

Your stack

vLLM / TensorRT-LLM, quantization, batching, caching, and hardware choice — adjusted until cost and quality meet the bar.

Ecosystem tracking

Ongoing

New model releases and hardware options are watched against your workload so the stack does not silently go stale.

Engagement ladder

Start with a sprint. Grow into ongoing ownership.

Three service tiers today — and a quiet roadmap toward the software platform those engagements are building.

1

Optimization Sprint

Baseline, evals, ranked savings, written implementation guidance

Wedge engagement
2

Implementation / Migration

We tune or migrate the stack — shadow deploy, cutover, handoff

Execute the plan
3

Retainer

Fractional inference engineer — re-benchmark, re-tune, on call

Keep it optimal
4

Scripted workflow

Repeat analyses accelerate as the benchmark library compounds

ahead
5

Optimization platform

The software destination — same method, productized

ahead

The optimization sprint

One to two weeks from kickoff to a ranked savings report.

Days 1–301

Scope & access

Align on the serving path under review, quality bar, and success criteria. Get read access to traffic samples, metrics, and current stack docs.

Week 102

Baseline & evals

Establish cost and quality baselines. Build an eval set from production traffic and lock the measurement protocol.

Week 1–203

Benchmark & rank

Run candidate models, quantized variants, serving configs, and hosting options. Deliver a written report ranked by cost impact.

Next04

Decide

Choose implementation, a performance-tied migration, or a retainer — or stop with a clear map of what to do yourself.

Talk to us about a sprint

Production LLM serving · quality bar agreed up front · meaningful savings identified — or you owe nothing

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