Custom models from your production traffic
Keep the quality.Drop the bill.
OS3 Compute trains a smaller model for your product to improve on quality, cost and speed.
Backed by Y Combinator- Support replies
- Ticket triage
- Document extraction
- Summaries
- Classification
- Search
- Drafting
- Agent tool calls
Why
Why teams move off frontier APIs.
01
Cost
Most of what your product asks a model is routine work. You pay frontier prices for all of it.
02
Fit
Frontier models are general purpose. On your domain, quality can slip between versions and requests can get refused.
03
Control
If your product runs on a rented model, so can every competitor's. A model trained on your traffic is yours alone.
What you get
A model trained for your product.
Lower cost
The same quality bar at a lower price, so expensive features can go to every user.
Faster answers
Smaller models answer sooner, so features can work while people type.
Better at your work
Trained on your domain, your team's judgement and the mistakes your current model keeps making.
Yours to improve
It keeps learning from new traffic and your team's corrections. You decide when each version ships.
Training and serving
- 3.4×
- more held-out tasks completed after reinforcement learning
- 1,073
- tokens a second on one request, about 5× public APIs for the same model
- 14×
- the stock engine's speed with 128 customer models on one GPU
- 1B+
- tokens served a day