You built the core.
We built the afterburner.
Most of what your product sends to frontier models is the same few jobs, thousands of times a day. Afterburner finds those calls in your code, trains a small model for each one, and proves the swap in a live A/B. You pay 10% of what you save, and nothing until it's proven.
Measured on a bank's support inbox: 93% cheaper, within 3.4 points of the frontier. Then it held itself back until it could prove that. Read why that's the point →
Your bill is sorted by model. Your costs come from lines of code.
Provider dashboards show tokens per model. They can't tell you that one triage prompt is a quarter of the bill, or that it only ever answers one of five words.
Agent loops and hard reasoning should stay on the frontier. The repeated jobs around them, like classifying, extracting, routing and summarizing, are where a small trained model does the same work for a fraction of the cost.
Same task, a sixth of the cost.
Both bars are to the same scale. Most of what's left is the 10% of calls that still go to the frontier.
Modeled · 800 tokens in, 50 out, $3 / $15 per million tokens
Five stages. One loop.
Every miss becomes training data, so each model starts from the last one's mistakes.
Point your SDK's base URL at our proxy. It runs in your VPC, logs every call, fails open, and ranks your call sites by what they cost.
OPENAI_BASE_URL=https://proxy.internal/v1The same proxy that logs each call later decides who answers it. If our side fails, calls go straight to the frontier.
We ran it on a bank's support inbox. Then we tried to break it.
2,084 real customer messages to a bank, routed by an LLM into 12 look-alike intents. Afterburner traced the routing call to line 21 of the bank's code, trained a 135M model on an 8-core M2 CPU, and put it in front of half the customers.
It cut cost per successful task by 93%, answered faster, and on messages it had never seen came within 3.4 points of the frontier, inside the agreed margin. But 962 messages weren't enough to prove that with 95% confidence, so the system held it back before any ramp. Nothing ships on a maybe. That is the product working.
53-minute run in 80 seconds · recorded from the live dashboard
Nothing ramps without a signed proof report.
The proxy splits real traffic between your frontier model and ours, by user, so each person sees one arm. Success is your downstream outcome, such as a ticket that stayed in the queue it was routed to, not our own grade.
Before the test starts, you agree how much worse our arm may be and still count as no worse: two points in a typical pilot. Our arm's cost includes every attempt that fell back to the frontier. If the test can't show we're within the margin, nothing changes.
| Arm | Tasks | Success | Cost / success | p95 |
|---|---|---|---|---|
| Frontier | 1,320 | 95.9% | $0.00330 | 812 ms |
| Afterburner | 1,306 | 96.2% | $0.00049 | 240 ms |
What a pass looks like.
The interval sits near zero, and the one-sided bound (▲) is right of the two-point margin. The owner signs, and traffic ramps.
Sample numbers · modeled
What “not proven yet” looks like.
In the bank case study our model was 93% cheaper and 3.4 points behind on average, inside the five-point margin. With only 962 unseen messages the interval stayed too wide to rule out more, so nothing ramped until more traffic proves it.
Measured · bank's support inbox
You pay 10% of what you save.
Read-only billing access and a repo scan. You get spend estimated per call site, and a saving estimate for each one.
A pilot on your biggest call site that can move: train, shadow, A/B. It ends with a proof report your team signs, or with nothing changed and nothing owed.
Each month: what the call site would cost today on your current model, minus what it costs now, net of every fallback to the frontier. You keep the other 90%.
No saving, no fee. The saving is measured against the frontier arm of the A/B, then against a 5% holdback that stays on your current model. Example: a call site that cost $10,000 a month now costs $1,500. You save $8,500 and pay $850.
Your weights. Each adapter is trained only on your traffic, serves only you, and can be exported if you leave.
Your data. The proxy runs in your VPC. Prompt bodies can stay in your own storage bucket.
What the first quarter looks like.
Nothing changes during shadow. Cost drops at the A/B and again at the ramp, while the success rate stays at the frontier's line.
Illustrative trajectory
Cheaper tokens don't make this obsolete.
Price falls at the bottom.
Tokens and GPUs get cheaper every year. Our fee is a share of your saving, so it shrinks with your costs, and you always keep 90%.
Value builds at the top.
Every correction your team makes becomes a labelled edge case. Those checks and fixes keep adding up, whatever the token price.
You keep what it learns.
The adapters, datasets and verdict history are yours, and leave with you.
What buyers ask us first.
Find out which of your calls can burn cleaner.
Thirty days of billing data and a read-only scan of your repo are enough to estimate spend per call site and size the saving. It takes about a day and needs no code change.
- ^Spend estimated per call site
- ^Which calls can move, and which stay on the frontier
- ^A saving estimate for each one