Training

Your data becomes your model.

Production-ready training infrastructure for teams of any size: point a run at your data, get a measurably better model, and own the weights outright. No GPU wrangling, no ML team required.

One command to a better model.

A training run reads the outcomes your agents already produced, fine-tunes an open base, proves itself on your evals, and deploys. Under an hour, end to end.

Where the training data comes from
allocate train
$ allocate train dispatch-model --data outcomes-q2
dataset 41,208 outcomes · inside your boundary
base deepseek-v4 (MIT, yours to keep)
training ████████████████████ 38 min
evals 94.1% on your suite +24% vs v2
deployed dispatch-model/v3 · live in 4s

Owned, measured, reversible.

The discipline that makes a trained model an asset instead of a liability.

You own the weights

Anything trained on your data is your property: stored in your scope, exportable in standard format, any time. Fire us and keep your model.

Adapters, not rebuilds

Your model deploys as a hot-loaded adapter on a shared base. Live in seconds, served at base-model price.

Evals before traffic

Every candidate runs your regression suite before it can serve. Improvements are measured, never vibes.

Rollback, always

Every version stays deployable. If v4 disappoints, v3 is one click back, with nothing lost.

Start from a proven base.

Frontier-class open weights, licensed for fine-tuning, served on the same gateway as everything else.

Explore the full library
Open weightFine-tunable

DeepSeek V4

Open weight · 256K context · 290 ms

Frontier-class, fully yours to train.

$0.09 / M tokensDetails
Open weightFine-tunable

Llama 4 70B

Open weight · 256K context · 260 ms

The proven base for private models.

$0.07 / M tokensDetails
Open weightFine-tunable

Qwen 3.5

Open weight · 512K context · 270 ms

Strong multilingual, great value.

$0.08 / M tokensDetails

It starts with one dataset.

Book a build session: we scope a live agent, connect one environment, and forecast your usage, in under an hour.