KeryWe find the cost and speed gains across your entire AI stack, and ship them.
Read-only access to start.
You pay for every word you send the AI.
Each time your app asks an AI model something, it also sends a pile of background text. Most of it goes unused, and all of it is billed. Here is what one request really looks like.
Sorting a support ticket, on the most expensive model
Example requestJust 0.3% of this request is the actual question.
- 35%
Standing instructions
The same instructions, sent every time
- 23%
List of tools
40 tools listed. This job uses 3.
- 27%
Background documents
12 documents attached. 2 were useful.
- 15%
Past conversation
The whole chat, resent on every message
- 0.3%
The actual question
The one part your user typed
17,760 tokens, the unit AI providers bill by
Money also leaks outside the request
- Overnight jobs paying instant-response prices
- AI agents retrying the same failed step over and over
- Test environments running on your most expensive model
Shorter requests and smaller models also respond faster, so your product speeds up as the bill comes down.
Cheaper only counts if quality held.
Nothing ships on a hunch. Every change is tested on your real traffic first.
Replay
We replay your real traffic through the cheaper option, in your cloud, on your API keys.
Compare
Answers are scored against your current output on the checks that matter for that route.
Ship
If quality held, you get a pull request with the before/after evidence. If not, it never reaches you.
Built to be trusted
- Read-only access to start
- Never sits in your request path
- Replays run in your cloud, on your API keys
Trim ticket tagging and move it to a small model
Sample data- model: "frontier"- tools: allTools- topK: 12+ model: "small"+ tools: taggingTools+ topK: 2| Check | Before | After |
|---|---|---|
| Input tokens | 17,760 | 7,910 |
| Quality eval | 96.1% | 96.0% |
| p95 latency | 2.4s | 0.8s |
How it runs
Free audit, 48 hours
Connect read-only access. You get your top savings opportunities with dollar estimates. No code changes required.
Cost cut, 2 weeks
We test the fixes on your real traffic and deliver each one as a pull request.
Savings dashboard
See spend by feature, team and customer, savings against your baseline, and alerts when costs creep back.
Ongoing optimization
New models and prices tested automatically, quarterly check-ins, and migration help when a model is retired.
See your savings potential
Enter your monthly LLM API spend for a rough estimate.
Estimated savings
$5,000–$10,000
($60k–$120k/year)
Estimate only. Actual savings depend on your workload. The free audit gives you real numbers.
Get my real number
Find out what you could save
30 minutes. We'll look at your setup and tell you where the money is.
Book a callOr email hello@kery.dev