Inference Cost Management

Cut your token bill.
Sharper answers.

Enrich every prompt with the right business context up front. Models do less searching, fewer retries, fewer hallucinations. A live dashboard shows what you saved.

How it works.

  1. 01Butter intercepts every prompt before it reaches the LLM, via MCP.
  2. 02It enriches the prompt with the relevant business context, pre-computed at ingestion time.
  3. 03Models answer correctly on the first pass. The dashboard shows exactly what you saved, in tokens and dollars.

Butter vs. prompt caching and context compression.

Caching / compressionButter
What it solvesReuses or shrinks tokensReplaces wasted context-hunting with the right context, up front
Effect on accuracyCompression can lose fidelityAnswer quality goes up, not down
VisibilityPer-call savings, no per-team viewLive dashboard by team, user, query type, and model
GovernanceTactical, per integrationCentralized budgets, alerts, and audit per team

Questions.

How does Butter actually save token cost?

Most LLM cost comes from models searching for context they don't have, retrying on wrong answers, and generating hallucinated paths the user has to correct. Butter enriches every prompt with the right business context up front, so the model answers correctly the first time. Fewer retries, fewer wasted tokens, fewer corrections.

What does the inference cost dashboard show?

Spend by team, by user, by model, and by query type. Tokens saved through context enrichment. Trends weekly and monthly. Budget alerts. A per-query audit of exactly what context was delivered and what it cost.

Will this work with any LLM?

Yes. Butter runs as an MCP server, model-agnostic. Use Claude, GPT, Gemini, or open-source models. The dashboard tracks cost across all of them in one view.

What kind of token cost reduction should we expect?

Early design partners are seeing 30 to 50 percent reduction in token spend on internal AI workflows. Variance depends on how context-heavy your queries are. We measure savings per workflow during onboarding so you have a baseline before expanding.

Can I cap LLM spend per team?

Yes. Set per-team budgets and the dashboard alerts before they're hit. Hard caps are configurable per workflow.

How is this different from prompt caching?

Prompt caching reuses tokens from a previous identical prompt. It helps when prompts repeat. It does not help when the model is searching for context it never had. Butter solves the underlying problem: the model gets the right context up front, so it does less searching in the first place.

See where your LLM dollars actually go.

We are rolling out access by team. Tell us which workflows are eating your token budget. We'll reach out within a few business days.

Request access