Token usage optimization
We reduce token usage in your AI calls: we shorten and structure prompts, trim excess context, cache, choose economical models for the simple — to pay less for essentially the same quality. This is a narrow, 'surgical' part of AI cost optimization. Honestly upfront: token savings are real, but these are TRADE-OFFS, not a free win: too aggressive context trimming and too short prompts can worsen answers; usage cannot be reduced 'to zero'; and you must optimize with quality control, not blindly.
Token usage optimization — overview
Token usage optimization is reducing the number of tokens (and thus cost) in AI requests and answers without unacceptable quality loss: shortening and restructuring prompts (remove filler, keep the essence), trimming/compressing the passed context to what is really needed, caching the repeated, choosing more economical models for simple tasks, controlling answer length, eliminating duplicate calls. It is a narrower, technical slice of overall cost optimization (904), focused specifically on tokens. Honestly about trade-offs, this is key: tokens directly affect quality. The context you pass the model and the prompt length are the 'fuel' for a good answer. Trimming context too aggressively or shortening the prompt means risking quality: the model may lose important information and answer worse. So token optimization is a balance of 'cheaper but not dumber', not 'the fewer tokens the better'. Honestly about 'not to zero': some tokens are always needed — we reduce usage, sometimes noticeably, but cannot zero it. Honestly about quality control: optimizing tokens blindly is dangerous — you can quietly drop answer quality for savings. So a quality measurement (eval — 899) before/after is needed. Honestly about the effect: at a large call volume token savings give a tangible bill reduction, but with deliberate trade-offs. Honestly about the link: it is part of broader AI cost optimization (904, which also has model routing, batching etc.). Honestly about access: access to prompts/calls and usage data is needed. An important boundary: this is tokens; broader cost optimization — 904; cost monitoring — LLMOps 898. Picture this: instead of 'paying for bloated prompts and excess context' — precise, economical calls without losing the essence. The base price starts from 35,000 ₽ (depends on volume and the number of scenarios).
Problems we solve
- Prompts are bloated, excess context is passed — many tokens.
- The AI bill is high due to uneconomical calls.
- Repeated content is sent to the model again.
- Answers are excessively long, tokens are wasted.
What's included in the Token usage optimization service
- Shortening and restructuring prompts (essence without filler)
- Trimming/compressing context to what is really needed
- Caching the repeated, controlling answer length
- Choosing economical models for simple tasks
- Quality measurement before/after (eval 899) to not drop it
- Honest boundaries (trade-offs; not to zero; not blind)
- A link with overall cost optimization (904)
- Handover and review with you
What you get
- Reduced token usage and AI bills
- Precise economical calls without losing the essence
- Quality control during optimization (before/after)
- Honest boundaries (a 'cheaper but not dumber' balance)
How the work goes: steps
- We analyze prompts, context, token usage; measure quality
- We optimize prompts/context/cache, verify quality
- We honestly state trade-offs and savings boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
The fewer tokens the better?
No, and that is honest: tokens are the 'fuel' for a good answer. Context and prompt length affect quality. Trimming context too aggressively or shortening the prompt means risking quality: the model loses the important and answers worse. The goal is a balance of 'cheaper but not dumber', not 'minimum tokens at any cost'. We optimize with quality control rather than cut blindly.
Can token usage be reduced to zero?
No, honestly: some tokens are always needed for the model to work. We reduce usage, sometimes noticeably (especially at a large call volume), but cannot zero it. Promising 'almost no tokens' is dishonest. It is reasonable savings by eliminating the excess, not magic free AI.
How is this different from AI cost optimization (904)?
It is a narrower, token-specific part. 904 is overall cost optimization (routing between models, batching, limits, architectural decisions), while here the focus is specifically on reducing tokens in prompts/context/answers. They are often done together. We will honestly suggest whether you need only token optimization or broader cost work.
About the provider
The «Token usage optimization» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.