Tokeen

LLM cost intelligence

See where your LLM bill really goes. Then get it back.

Most AI products pay for the same tokens again and again: prompt caches that never hit, tool results replayed on every turn, fan-out calls that could be batched. Tokeen shows you the exact dollars, from your invoice, before you change a line of code.

Get a free auditHow it works

How it works

  1. 01

    Import your invoice

    Drop your OpenAI, Azure or Anthropic usage export. Ten minutes, zero integration. Your real cache-hit rate is already on the bill — most teams have never read it.

  2. 02

    Run in shadow mode

    One line wraps your SDK client. Tokeen records usage, timings and rolling prefix hashes for a few days. No prompt content ever leaves your servers.

  3. 03

    Get a score card in dollars

    Each lever — stable prefix, batching, context eviction, output budget, model size — priced per month and labelled deterministic, estimated or speculative.

  4. 04

    Keep the savings

    The Tokeen SDK reorders prefixes for caching, compacts JSON without loss, batches fan-out and evicts stale tool results — and alerts you when a deploy breaks your hit rate.

What the numbers say

From our own benchmarks and audits. Every figure on this site comes from a real invoice, a real trace or a reproducible run.

10 %
cache-hit rate on a B2B SaaS invoice we audited — the provider caches for free, the prompt just kept changing.
70 %
of an AI agent's bill is cache reads of the same context, turn after turn. Context size × calls, not compression, is the lever.
≈ 0 %
saved by neural prompt compression once caching is on: shorter input, longer output. We measured it on 300 calls.
3.7 %
of cache misses is all it takes for a 30 % compression to cost more than it saves.

Spending more than $2k a month on LLM calls?

Send us your last invoice. You get a score card in dollars within a week, free, whether or not you ever use the SDK.

Request the free audit