Claude optimization services that cut API spend, not quality.
Claude optimization — or Claude optimisation, in UK spelling — means tuning a production Claude integration so it costs less and responds faster without losing output quality. Four levers do most of the work: prompt caching (58-72% cost reduction measured across our production builds), model-tier routing (Haiku at $1 per million input tokens vs Opus at $5), context trimming, and the Batch API (50% off any workload that can wait up to an hour).
For teams who already have Claude in production and a bill growing faster than usage. We do not replace your build — we tune it. The caching data behind the 58-72% figure is documented in our prompt caching in production writeup.
Stack review for a $30M company picking between OpenAI, Claude, and Bedrock. 1-hour call, 3-page memo, decision made.
The economics, in one table
Claude pricing has built-in discounts most teams never use. Optimization is mostly about using them.
| Lever | Mechanic | What it means for the bill |
|---|---|---|
| Prompt caching | Cached input tokens bill at ~10% of base input rate | 58-72% total bill reduction measured across our builds |
| Model-tier routing | Haiku $1/$5 · Sonnet $3/$15 · Opus $5/$25 per 1M tokens (input/output) | Route simple requests down-tier; escalate on confidence |
| Batch API | Async processing at 50% of standard prices | Most batches complete within 1 hour |
| Context trimming | Fewer input tokens on every request | Lower spend and often better accuracy |
Cost, latency, and time-to-ship data from five production AI builds is in what 5 AI builds taught me. The platforms themselves are in our case studies.
The levers we actually pull
Named because the names are how you tell whether the engineer touching your Claude bill knows what they are doing.
Prompt caching on Claude
Lift stable system prompts, tool definitions, and few-shot examples into the cache prefix. Cached input tokens bill at ~10% of the base rate. Measured across our production builds: 58-72% off the Claude bill. The single biggest cut on most bills.
Model-tier routing
Cheap model first — Haiku at $1/M input tokens — with confidence-routing to Sonnet ($3/M) or Opus ($5/M) only when the task needs it. Half the bill, same output quality, proven by evals before it ships.
Context trimming
Most requests carry tokens the model never uses: bloated histories, redundant retrieval chunks, verbose tool results. Trimming the context window cuts input spend on every single request and often improves accuracy.
Batch API
Anything that can wait up to an hour — nightly enrichment, bulk classification, report generation — runs at 50% of standard prices through the Batch API. A routing change, not a rebuild.
Structured output
Stop parsing free-text JSON. Schema-constrained output plus Zod validation cuts retries to zero, drops token spend, and removes the ‘LLM returned malformed JSON’ class of bug.
Eval harness
Golden-set evals checked into git. Every model swap, prompt change, or cache restructure runs against the set first. You ship on numbers, not vibes.
When to call
A few hours of tuning now is cheaper than a quarter of runaway spend.
Existing Claude build, runaway costs
Your Claude bill is climbing faster than usage. We audit the prompt, the cache strategy, the model tiers, and the retrieval pipeline. Prompt caching alone delivered 58-72% reductions across our production builds.
Latency complaints
Users say it is slow. We check whether it actually is, or whether the UX is hiding the work. Streaming, model swaps, parallel tool calls, async tasks — whichever lever moves the meter without breaking the output.
Output quality regressions
Outputs got worse after a model swap, a prompt edit, or a vendor update. We bring the eval harness, find the regression, and ship the fix with numbers attached.
Vendor migration
Moving from OpenAI to Claude, Claude to Bedrock, or off a wrapper SDK to direct API. We do the migration with parity evals so you ship the change and the numbers in the same week.
Need a new system built rather than an existing one tuned? That is our AI agent ecosystem service. For stack decisions before any code exists, start with AI consulting.
Pricing
Build / Scale / Consult. Same tiers across every Wolrix service — full details on the pricing page. Every engagement: 100% Satisfaction or your money back.
Audit of an existing Claude build. Prompt-cache review, model-routing review, eval-harness review. Written summary in 48 hours.
Implement the audit. Caching, tiering, structured output, eval harness checked into git. Cost cut + quality held, with numbers.
Full optimization sweep. Tiering, retrieval rebuild, vendor migration, observability stack. For builds at 6-7 figure annual AI spend.
Claude optimization, answered
The questions teams ask before sending us a bill.
What do Claude optimization services actually include?
An audit of your prompts, cache strategy, model routing, and retrieval pipeline, followed by implementation: prompt caching, model tiering, structured output, context trimming, and an eval harness checked into git. You get before-and-after numbers on cost, latency, and output quality, not a slide deck.
How much can prompt caching save on a Claude bill?
Cached input tokens bill at roughly 10% of the base input rate. Across our production builds, prompt caching alone cut Claude spend by 58-72%. It is usually the single biggest lever on any Claude bill because system prompts, tool definitions, and few-shot examples repeat on every request.
Is Claude optimisation different from Claude optimization?
No. Optimisation is the British spelling, optimization the American one. Same service either way: we work with teams across the US, UK, and EU from Belgrade, on US and UK hours.
When should requests route to Haiku instead of Sonnet or Opus?
Classification, extraction, routing, and short summaries usually hold quality on Haiku at $1 per million input tokens versus $5 on Opus. The pattern that works: cheap model first, with confidence-based escalation to Sonnet or Opus only when needed. An eval harness proves the quality holds before the swap ships.
Does the Batch API really cut Claude costs by 50%?
Yes. The Batch API processes requests asynchronously at 50% of standard prices. Most batches complete within an hour. Any workload that does not need a real-time answer, such as nightly enrichment, bulk classification, or report generation, should run through it.
How much does Claude optimization cost?
Advisory runs $100-$200/hr with no minimum: audit plus written summary in 48 hours. Implementing the audit is a fixed-scope build at $10K-$25K over 2-4 weeks. Every engagement carries our 100% Satisfaction or your money back guarantee.
Send the bill. We will find the lever.
A few hours of advisory now is cheaper than another quarter of runaway model spend. NDA before any details.