Token Optimization
Spend less per task.Keep the same quality.
PressW tunes model selection, prompts, caching, and batching across your Claude API and Claude Code workloads, so every task runs on the right model with the fewest tokens it needs.
Cut Token Costs Without Cutting Corners.
Our approach
PressW's optimization practice starts with measurement, not guesswork. We find the workflows driving spend before changing a single prompt or model setting.
Model routing, prompt design, and caching are tuned together as one system, with evals confirming that output quality holds after every change.
Every change is measured against a baseline and documented. The result is a lower cost per task your users never notice, except in faster responses.
MODEL ROUTING
OptimizedDocument triage
HaikuMemo drafting
SonnetComplex analysis
OpusPrompt caching
EnabledOvernight jobs
Batch APIOPTIMIZATION LEVERS
6 of 8 applied
Every change validated by evals
OPTIMIZATION REPORT · SEP 2026
Cost per task down, quality held
-34%
Cost per task
72%
Cache hit rate
0
Quality regressions
Classification workflow moved to a smaller model after eval pass
Nightly summaries shifted to the Batch API
Match Every Workload to the Right Model.
Optimization services
We find where tokens are wasted, fix it without lowering output quality, and keep costs in line as usage grows.
Spend Audit & Baseline
We review API, Claude Code, and seat usage to find the workflows driving cost. You get a baseline cost per task and a ranked list of the highest-value fixes to make first.
Model Selection & Routing
Not every task needs the largest model. We route simple work like classification and extraction to faster, cheaper models and save the most capable ones for complex reasoning.
Prompt & Context Engineering
Bloated system prompts and oversized context add cost to every call. We tighten instructions, trim context, and cap outputs so each request carries only what the task needs.
Prompt Caching & Batch Processing
Repeated instructions and reference documents get cached, and work that can wait runs through the Batch API. Both cut cost on high-volume workloads without changing what users see.
Quality Evals & Regression Testing
Every optimization is tested against gold-standard examples and grading criteria before it ships. If quality drops, the change doesn't go live, so savings never cost accuracy.
Ongoing Cost Governance
We monitor spend against targets, re-test routing when Anthropic releases new models, and report savings and next steps in a monthly optimization report.
Lower Cost Per Task, Release After Release.
Our process & impact
A repeatable optimization cycle finds savings fast, protects output quality with evals, and keeps cost per task falling as new models and pricing arrive.
01
Measured before anything changes
We baseline cost, latency, and quality for every major workflow first, so each optimization has a clear before and after instead of a rough estimate.
02
Tuned without trading off quality
Model routing, caching, prompt rewrites, and batching are applied one workflow at a time, and each change has to pass the same evals as the original before it ships.
03
Re-tested as models evolve
New Claude models often change the math on cost and quality. We re-run evals on each release, adjust routing where it helps, and report savings every month.
Dive Deeper
Learn more about reducing Claude token costs without losing output quality from the PressW team.
Pay for the TokensYou Actually Need.
Right-size every model, prompt, and workload so Claude costs less per task as your usage grows.
Model routing · Prompt caching · Batch API