Stop paying LLMs to read irrelevant context.
Most of what your app sends to the model is noise: extra search results, raw tool output, old messages. Contextwaise keeps only what the answer needs, in milliseconds, so you pay for fewer tokens. Add it with one API call, or one line in your agent.
| Agent | Connected through | Optimizations | Avg sent | Reduction |
|---|---|---|---|---|
| support-bot | API call | 48,210 | 5.3K | -71% |
| shop-assistant | Contextwaise SDK | 60,517 | 533 | -83% |
| research-agent | API call | 31,904 | 9.8K | -56% |
Your model isn't expensive. Your context is bloated.
RAG returns extra chunks. Agents dump raw tool output into every step, then replay it on every turn after. Web search brings back whole pages. The model reads all of it, and you pay for it, again and again.
72% of what an agent's model reads is tool output.
Logs, test runs and API responses come back raw, and every later step reads them again. That is where an agent's bill grows, and where Contextwaise cuts.
Our demo agent, five questions on GPT-6.1 Sol: everything the model read over the run.
Other ways to cut the bill, and the catch.
Send your model only what the answer needs.
Contextwaise sits between your app and your model. It scores every piece of context against the question, removes what doesn't help and logs why. Same model, same prompt, fewer tokens.
- 1Your app gathers context
Search results, documents, tool output, chat history. Keep everything you use today.
- 2Contextwaise keeps what matters
Duplicates, noise and anything off-topic are removed, in well under a second.
- 3Your model reads less
Same model, same prompt, fewer tokens. You see what was removed and why.
Watch what gets removed.
A customer asks a support bot about a double charge. Search returns 13 pieces of context. Only 3 answer the question.
Measured on our example request, 510 ms end to end.
- Why do I see two charges for the same invoice?89 tok
- Two charges for one invoice: when a card payment times out…70 tok
- Refunds for duplicate charges are issued within 24 hours74 tok
- Cancelling a subscription68 tok
- Support hours55 tok
- 2023 pricing (archived)53 tok
- Customizing invoices with your logo56 tok
- Single sign-on for Business plans56 tok
- Disputing a charge with your bank68 tok
- Accepted payment methods46 tok
- Downloading invoices as PDF45 tok
- Changelog, August40 tok
- Billing API: invoice inv_8842, two payments181 tok
Agents too.
A coding agent is asked why the tests fail. It runs the test suite, and the tool sends back everything: a progress bar, 336 passing tests and 2 failures. Only the failures help.
- Trimmed the moment the tool returns, before the model reads it.
- Agents reread every earlier result on each step, so the savings repeat on every step after.
- Nothing is lost: the full output stays with your agent, and the model can ask for any part back.
Measured on our demo agent.
Try the full demoLess to read. Less to pay.
See it liveMeasured on our example request and demo agent. Your numbers depend on how much noise your context carries.
510 ms to cut a 13-item RAG request by 71%.
The cut happens before your model is called, in milliseconds, and it pays for itself on that same request. The free local cleanup takes a few ms; relevance scoring adds a few hundred.
One run each through the live engine, on our example requests and demo agent.
Any context you send, not only RAG.
Tool results are trimmed once and stay small on every later step, so long runs stop getting more expensive.
Retrieve more results, then pay your expensive model for only the evidence that matters.
Test runs, logs and API responses lose their noise first: passing tests, repeated lines, empty fields.
Whole pages become the few passages that answer the question.
Old messages that no longer matter stop being resent on every turn.
Works with what you already use.
One API call, or one line in your agent. No new model, no migration, and you keep calling your provider directly.
OpenAI
Anthropic
Gemini
Mistral
DeepSeek
LlamaSafe by default.
If Contextwaise can't answer, your original context goes through, as it does today.
After a cut, a check asks whether what was kept was enough, and cuts less next time if it wasn't.
See what was removed from each request, and why: duplicate, off-topic or over budget.
What would you save?
An agent that sends 1B input tokens a month to Claude Sonnet 5.5 pays about $1,108 for them today. With Contextwaise, the model reads about 412M and the bill is $414, plan included.
Calculate yoursGo deeper.
All guides
LLM Cost Optimization: 8 Levers That Cut 50–90%
LLM cost optimization in 8 levers with 2026 prices: cut input context, prompt caching (90% off), batching (50% off), routing and output caps. Worked examples.
Anthropic Prompt Caching (Claude): What It Costs
Anthropic prompt caching bills a repeated prefix at 10% of the input price or less. How cache_control works, when it pays off, and what silently breaks it.
Context Rot: Why Long Prompts Make LLMs Worse
Context rot is the drop in LLM accuracy as input grows, long before the context window is full. What research shows, how to measure it and how to prevent it.
off your first year on a yearly plan.
Choose yearly billing on Pro or Scale by December 31, 2026 and your first year costs 30% less than paying monthly: $126 instead of $180 for Pro. Applied at checkout, no code needed.
Start freeQuestions
The short ones are here.
Will it change my answers?
It only removes context the answer doesn't need: duplicates, off-topic results, and noise like passing tests or empty fields. After a cut, a check asks whether what was kept was enough, and the next requests cut less if it wasn't.
What if Contextwaise is down?
Your app keeps working. If we can't answer, you send your original context to your model, as you do today. Agents keep running with the free local cleanup only.
Do I have to change my model or provider?
No. You keep calling OpenAI, Anthropic, Gemini or any model directly. Contextwaise only decides which context reaches it, and never sits between you and your provider's bill.
What does it cost?
Free for your first 1M context tokens, then plans from $15 a month. The calculator on the pricing page shows what you'd save on your model and volume.
Is my data stored?
Not in full. For your request log we keep the first 500 characters of the question and a short preview of each context item. With an API key, the agent SDK sends tool results still over budget after cleanup, in sections, to be scored; they are not stored, and the dashboard keeps only tool names and sizes.
Cut your LLM costs
without cutting quality.
One layer between retrieval and inference removes the context your model never needed, in milliseconds. Fewer input tokens, lower cost, a prompt cache that keeps hitting, and every removal logged with its reason.