Why Cost Optimization Matters

Frontier models like OpenAI and Anthropic are not cheap. Input tokens are billed on every request. Vrbose compresses prompts upstream so you send fewer tokens to your provider - saving costs, improving accuracy, and speeding up responses.

The facts on input tokens

  • A 'token' is usually considered 4 characters, which is 3/4 of a word on average.
  • OpenAI, Anthropic, and other AI services charge in part based on input tokens: the amount of text in characters that you ship to their API.
  • Reducing the size of the text you send to their API (your 'prompt') is the best lever you have for controlling your costs.
  • Compressing a prompt without losing context is was difficult, but now you have vrbose.ai .

Why optimize input costs

  • Input pricing scales with prompt size—verbose agents, RAG context, and chat history compound quickly.
  • Compression lowers the billable footprint per request, so unit economics stay predictable as traffic grows.
  • Teams often see up to 50% fewer input tokens on representative prompts—savings vary by content, but the lever is consistent.

How Vrbose fits your stack

Call POST /detokenate with your original query, receive a compressed q, and pass that to your LLM provider. One integration point, low latency, and usage metadata on every response.

Free accounts get 20 successful API queries per day to validate savings. Paid accounts unlock unlimited queries at $0.01 per successful call, with daily billing only when usage accumulated usage reaches $10. See pricing

Where savings compound

Agent loops, support bots, and RAG pipelines send repetitive or bloated context on every turn. Compressing once per hop keeps downstream models focused and your token meter lower. Explore use cases

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