Your AI bill tripled.
You changed nothing.
Not a bug. Not waste. Just the way AI billing works by default — and the reason your AI costs are eating into your margins. You know exactly what's happening.
Why your AI bill grows when you change nothing — and what to do about it.
Why bills spike
- Every request re-processes your full context from scratch — then discards it.
- Prompt caching exists. Restructuring every prompt to use it correctly is expensive engineering work most teams defer.
- More scale means faster cost growth — LLM spend doesn't get cheaper as you grow.
- Most teams treat the LLM as a single unit. It isn't. Smaller models can generate, larger models can verify — but only if the architecture is built to support it.
/ req
Same 48,000-token context. Sent. Processed. Discarded. Sent again — every call.
14:02:11 POST /v1/chat ctx=12,480 tok $0.031 14:02:14 POST /v1/chat ctx=12,480 tok $0.031 14:02:18 POST /v1/chat ctx=12,480 tok $0.031 14:02:22 POST /v1/chat ctx=12,480 tok $0.031 14:02:25 POST /v1/chat ctx=12,480 tok $0.031 14:02:29 POST /v1/chat ctx=12,480 tok $0.031 14:02:32 POST /v1/chat ctx=12,480 tok $0.031 14:02:36 POST /v1/chat ctx=12,480 tok $0.031 # same context. every. single. call.
Meet TokenTune
- Sits between your app and your LLM provider — a transparent proxy, one environment variable.
- Runs a lightweight coordinator that decides what actually needs your expensive model's attention — and what doesn't. Intent extracted locally before anything hits a paid API.
- Responses validated against a confidence threshold before being committed to cache — so only high-quality answers get reused.
- No code changes required. Every dollar saved is verified against your actual provider invoices.
/ req
Same 48,000-token context — prefix cached, only ~16,800 tokens billed per call.
# before OPENAI_BASE_URL=https://api.openai.com/v1 OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxxxxx # after OPENAI_BASE_URL=https://proxy.tokentune.dev/v1 OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxxxxx TOKENTUNE_API_KEY=tt_live_xxxxxxxxxxxxxxxxxxxx OPENAI_FALLBACK_URL=https://api.openai.com/v1 # auto-used if proxy latency exceeds 200ms
The LLM is not the whole stack.
A webpage needs a frontend, a backend, and a database. A well-architected AI system needs more than just a model. Most teams bolt the LLM directly to the application and wonder why costs spiral. TokenTune is the missing middleware.
Four layers between your app and the model — designed to never let a wasted token through.
Why token costs don't scale like normal software.
Illustrative — based on observed API pricing behavior.
"Token costs accelerate nonlinearly as models grow more complex and usage scales."
saved on your entire API spend — without rewriting anything.
Based on observed cache hit rates across comparable workloads. Actual savings vary.
You only pay when we save you money.
No seats. No monthly minimums. We take 20% of verified savings — nothing else. Confirmed against your real provider invoices. If your bill doesn't drop, you pay nothing.
# Your monthly bill baseline_spend = $10,000 actual_spend = $5,200 # Verified savings savings = $4,800 # TokenTune fee (20%) your_fee = $960 # Net gain: $3,840
You're in good company.
You're not the only one staring at that invoice. But you might be the first on your team to do something about it.
- OpenAI & Anthropic supported at launch
- Integration in under
30 minutes - No code changes to your application logic
- Compatible with LiteLLM, Kong, Braintrust, LangSmith
We onboard a limited cohort spending $2K+/mo on LLM APIs.