Nothing to setup up
Start a run from a Slack message, a commit or your own agent. Because configs are code, every launch is reviewable and repeatable.
Research at the speed of thought.
Model Foundry lets your team launch a run the moment they have the idea,
so the only thing between them and a better model is their next idea.
Never wait on one vendor
Your code and config run unchanged on NVIDIA, AMD, TPU or Trainium. If one provider is short on capacity, your run starts on the next best one.
Nothing to dig for
Every dataset, config, checkpoint and eval is linked so you can trace any model back to its data and every dataset forward to every model it trained.
Why models stallModels improve in loops.
Models improve in loops.
Most tooling stops at the checkpoint.
You train, evaluate, ship and learn from what breaks. Model Foundry runs that whole loop as one system, so nothing gets lost between turns, all while you keep full ownership of your weights and process.
Scattered artifacts
Checkpoints, datasets and configs drift apart across buckets and notebooks, so nobody can say which data made which model.
Infra failures
GPUs failures, gradient spikes and connection drops make training slower and drive up iteration speeds, especially at scale.
Traces that never come back
Production traffic is the best training signal you have, but it usually lands in a log bucket nobody reads.
The loop
From raw data to production and back again.
Each stage hands the next a versioned asset, so every turn of the loop starts where the last one ended.
Configure and review
Datasets, data blends and training configs land as a pull request, with a diff your team can review.
Train and track
Pre-train, fine-tune, distill or run RL, with every checkpoint and metric recorded.
Evaluate and promote
Evals run on every checkpoint, catching false wins and reward hacks before anything is promoted.
Deploy and learn
Serve the model behind a standard API, then turn production traces into the next training dataset.
Product tour
See the loop, one screen at a time
Everything your team touches, from the first dataset to production traffic, in one place and linked together.
Dedup, filter and blend datasets as versioned jobs, and see exactly which rows were dropped and why.
Datasets
Dedup, filter and blend datasets as versioned jobs, and see exactly which rows were dropped and why.
Experiments
Define a sandbox, an agent harness, a task source and a verifier in one place, then dry-run it against your current policy before spending a GPU-hour.
Workspaces
Start from any past run, eval or experiment with the same image, mounts and code, or fork a workspace instantly to try something new.
Training
Loss, gradient norm, learning rate and throughput, live. Spikes are caught and checked automatically, and any checkpoint deploys in one click.
Evals
A judge flags wins that look too good, a researcher confirms the hack, and the patched task re-queues the eval on its own.
Deploy
Serve your model, price it, and see usage, latency and revenue, with every change logged.
Traces
Filter production traces by judge score, redact PII, and refresh the dataset on a schedule. The next run starts here.
Built down to the silcion
Every run is faster and cheaper by default
Under every run are our own kernels, training engines and fleet, so you get the speedup of tens of thousands of hours of optimization without changing a line of code.
Beyond experiment tracking
Don’t just log what happened, know why it happened.
A tracking tool only records metrics after the fact, while Model Foundry records the graph, from every dataset shard, config diff, checkpoint and eval as a node with links in both directions.
Beyond experiment trackingSee the loop,
See the loop,
Don’t just log what
happened, know why it
happened.
A tracking tool only records metrics after the fact, while Model Foundry records the graph, from every dataset shard, config diff, checkpoint and eval as a node with links in both directions.
Trace any output back
Follow a token from a production trace to the checkpoint, run, config and dataset shard behind it.
Diff against the baseline
Every run is compared with the config and data it was forked from, not just its metrics.
Fork from any asset
Reproduce, sweep or branch from any dataset, checkpoint or run without rebuilding the pipeline around it.
Every training stage
Pre-train, align and server your own models.
Pre-training, post-training, evals and serving all share one graph, one compute pool and one set of permissions.
Pre-training
Data blends, curricula and loss-spike forensics at cluster scale, with every shard accounted for.
SFT & distillation
Post-training recipes with chat templates, data mixes and epoch checkpoints you can compare.
Reinforcement learning
Fast rollouts using our own inference engine designed for long-horizon agentic and async RL.
Quantize & speculate
FP8 and INT4 releases behind quality gates, plus spec decoders trained on your own traffic.
What should your
model get better at?
Your team sets the goal and runs the research and Model Foundry handles everything else, from the first dataset to your flagship model.