Your best model from last month?
You probably can't reproduce it.
And when someone asks what changed… you don't have an answer.
One command. A complete record of what actually happened. Works with any training script — no code changes, no framework.
Let your agents roar.
Capture what ran. Store the record. Control what runs next.
Three components give you a complete record of how your models are actually built — and what gets to run next. Use any piece on its own.
If it ran, roar saw it.
A CLI that captures lineage at runtime — data, code, environment, artifacts. No code changes. No loggers. No frameworks.
Read about roar →Every model has a recipe.
A content-addressable registry of every artifact and job. Resolve any artifact's hash back to the code, data, and environment that made it.
Read about GLaaS →Approve before compute.
Training requests as pull requests. Nothing runs until someone — or a policy — says go.
Why TReqs →One command.
No instrumentation.
Lineage captured at runtime.
No code changes. No frameworks. No loggers. No infrastructure lock-in.
Just roar run before your existing command.
A runtime observer records what actually happened — not what a logger remembered to log. If it ran, roar saw it.
- env & deps
- data & S3 objects
- config & args
- git SHA & diff
- GPU / CUDA state
- model artifacts
Every artifact points back
to how it was made.
A content-addressable registry of every job your team has run.
Point at any model hash. GLaaS walks the provenance graph back to the exact code, data, config, and environment that produced it — or walk forward from a dataset to every model it touched.
Your models and data never leave your infrastructure. GLaaS records how they were made, not what they are.
Decide what gets to run — and why — before the money moves.
Right now, anyone — or any agent — can burn $1,000 on a run. And you find out after.
TReqs turns that into a training request before it runs.
- file a request: config + data + compute budget
- team or policy reviews it before it starts
- roar captures what actually ran
- the request resolves with a real artifact
- Without TReqs: runs happen. You investigate later.
- With TReqs: runs are reviewed with context before they start.
Pull requests for training runs. Control plane for ML compute.
Free for individuals. Paid when you're a team.
Anyone who only needs to read results, browse lineage, or track costs is a free reader seat — including finance and leadership. Paid seats are for people who run or approve.
Individual use. The CLI and hosted public lineage.
- roar CLI, unlimited runs
- hosted GLaaS · public lineage
- 1 writer seat · 1-year retention
- 5 projects · BYO node
Small teams with 1–3 ML engineers.
- everything in Free
- private lineage · unlimited retention
- up to 3 writer seats
- unlimited reader seats (free)
- cloud capture · multi-node
Shared lineage, unlimited readers, TReqs coordination.
- everything in Free
- private lineage · unlimited retention
- full TReqs approvals & policies
- unlimited reader seats (free)
- Slack · API & cost attribution
When procurement, security, or scale call.
- everything in Team
- SSO, SAML, audit logs
- fully customizable policies
- dedicated support & SLA
Try it on your last training job.
Thirty seconds to install. No account needed. No code changes.