Service Cost Optimization

Your AI bill is 3–5× bigger than it needs to be.

It happens quietly: the pilot used the biggest model for everything because quality mattered and volume didn't. Then volume arrived, nobody revisited the defaults, and now finance is asking why the AI line tripled. Almost every LLM deployment I audit is paying frontier prices for work a model a tenth the cost handles identically. I find that waste and remove it — measured, without moving the quality bar.

60–80% Typical spend reduction

0 pts Quality drop allowed (evals prove it)

2–4 wks Audit to savings

LLM spend balloons for boring reasons: one premium model set as the default for every task, prompts that grew to thousands of tokens as engineers appended instructions and never pruned, context windows stuffed with entire documents when three paragraphs were relevant, no caching on traffic that is 40% repeats, and retry logic that pays full price to fail twice. None of this is exotic. All of it compounds.

The fix is measurement plus routing. First instrument everything — cost per task, per feature, per customer — because you can't cut what you can't see. Then route: the cheapest model that clears the eval bar for each task type, with the frontier model reserved for the work that genuinely needs it. Add prompt compression, caching, and batching, and the bill drops 60–80% while your eval scores hold flat. The evals are the guardrail — every optimization ships only if quality holds.

Where the savings hide

Model routing — classification, extraction, and formatting moved to small models; reasoning kept on frontier ones. Usually the single biggest line.
Prompt and context diet — system prompts pruned, retrieval tightened, and documents trimmed to relevant passages. Tokens are the meter; stop leaving it running.
Caching and batching — repeated questions served from cache, non-urgent work batched at off-peak rates, retries made intelligent instead of expensive.

The cheapest token is the one you don't send. The second cheapest is the one you send to the right-sized model.

Four dials that control the bill.

Every change gated by evals — savings that cost quality aren’t savings.

01 / Visibility

Meter everything first

Cost per task, per feature, per customer — instrumented before anything changes, so savings are provable and permanent.

02 / Routing

Right-size every task

Each task type benchmarked across model tiers, then routed to the cheapest model that clears the quality bar.

03 / Efficiency

Shrink the tokens

Prompt pruning, context trimming, caching, and batching — the compounding percentages that halve bills.

04 / Governance

Keep it optimized

Budgets, alerts, and monthly reviews so the bill stays optimized as usage grows — instead of quietly re-bloating.

Model routingPrompt cachingToken auditBatchingUnit economicsEval-gated

Questions, answered.

The ones every buyer asks first.

How much can LLM costs realistically be reduced?

Deployments still running on pilot-era defaults typically see 60–80% reduction — routing alone often accounts for half of that. Already-optimized systems see less, and I will tell you within the first week of the audit which kind you are. The number is measured against your own eval suite, not asserted.

Will cutting costs make the AI worse?

Not if evals gate every change — that is the discipline. Each optimization ships only if quality scores hold. In practice some tasks improve on smaller models (less over-reasoning on simple jobs), and the frontier budget gets concentrated where it actually moves outcomes.

Does this work if we are on Azure OpenAI or Bedrock?

Yes — routing, caching, prompt compression, and batching all work inside cloud platforms, and committed-spend agreements often make right-sizing more valuable, not less. Cross-provider routing adds another lever where your compliance posture allows it.

What does the engagement look like?

Two to four weeks: week one instruments and baselines, weeks two and three implement routing and efficiency changes behind eval gates, week four hands over dashboards, budgets, and alerts. Fixed price, and the target is that the engagement pays for itself within a quarter from savings alone.

Same AI. Smaller invoice.

Send me last month's AI bill and a description of what it bought. I'll tell you in one call whether there's 20% or 70% hiding in it.

Let's talk

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