LLM Cost Optimization

Cut your LLM API spend by up to 80% without changing your workflow or sacrificing output quality.

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You're paying for tokens you don't need

The Problem

Most LLM API costs come from context repetition, not creative work. In a 30-turn coding session, 60-80% of tokens are stale file reads, build logs, and git diffs being resent on every turn. Add verbose prompts and frontier models handling simple tasks, and your bill grows faster than your codebase.

The Solution

Tokonomy is a reverse proxy that optimizes every request before it reaches the API. Observation masking strips stale context. Smart routing sends simple tasks to cheaper models. Prompt compression removes verbosity. All three stack, compounding savings across every request.

Before and After

Without Tokonomy
Monthly API spend: $247 Avg tokens per session: ~45,000 Requests on frontier model: 100% Stale context resent: ~60% of tokens
With Tokonomy
Monthly API spend: $31 Avg tokens per session: ~12,000 Requests on frontier model: ~28% Stale context resent: 0%
Token savings: 87%

How It Works

1

Your tool sends a request to the Tokonomy proxy URL instead of the provider

2

The proxy masks stale tool outputs, compresses the prompt, and classifies the task

3

Simple tasks route to cheaper models automatically. Complex tasks stay on your frontier model

4

The optimized request forwards to the provider. Your tool gets the response unchanged

Frequently Asked Questions

Does optimization affect output quality?
No. Masking removes context the model won't reference again. Routing only downgrades tasks where cheaper models produce identical results. Compression preserves all semantic content.
How long does setup take?
About 5 minutes. Swap one URL in your tool's settings. No SDK, no code changes, no dependencies.
What providers are supported?
Claude (Anthropic), ChatGPT (OpenAI), and Gemini (Google). The proxy handles format conversion transparently.
Is my data stored?
No. Prompts are processed in memory and never persisted. Only usage metadata (model, token counts, costs) is retained.
Can I control how aggressive the optimization is?
Yes. Compression has profiles from Low (12%) to High (72%). Routing and masking are separate toggles. You can enable any combination.

Related Tools

Token Management

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Prompt Compression

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Model Tiering

Learn more →

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Tokonomy — LLM cost optimization proxy for Claude, ChatGPT, and Gemini

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