Open-weight models on your infrastructure, without an ML team
Most enterprises that decide on open-weight models make the same first move: pick a provider so the weights and the data stay inside their own account. That move solves hosting. It leaves everything hard exactly where it was.
The usual path
The decision to go open weight usually starts with a constraint: the data cannot leave the account. So the team picks a cloud or a neocloud, Together, Fireworks, Thinking Machines, or their own GPUs, and stands up a model inside their VPC. Weights hosted, endpoint live, compliance satisfied.
That only solves the easiest section of the problem while leaving the heavy lifting up to your team. The provider will store the weights, run a fine-tuning job on the dataset you hand it, and serve what comes out. That is the part of the problem with a price list.
It is also the easy part.
The heavy lifting is still yours
After the weights are hosted, every decision that determines whether the model is any good is still sitting with your team.
| The provider does | You still have to |
|---|---|
| Store the weights | Decide which base model is worth training on, for this task |
| Run the fine-tuning job on your dataset | Turn raw traces and documents into a dataset worth training on: dedupe, split, check for leakage, know what a good example looks like |
| Serve the result | Build the environment that grades it: your runtime, your checks, a frozen holdout |
| Report training loss | Judge the checkpoint on task behavior, against the base model, and decide whether it ships |
| Keep the endpoint up | Notice when production drifts, pull the failures back into the data, and retrain |
The right-hand column is a research team's job. Most enterprises that choose this path do not have one, and the ones that do have it working on something else. So the project stalls at a hosted base model that nobody has made better, and the frontier API quietly stays in production.
What Ashr does on your infrastructure
Ashr takes the right-hand column. The agents run inside your account, against your data, on the GPUs you already have or the provider you already chose. Nothing moves.
- Judgment. Which base is worth training, which method the task calls for, which checkpoint ships. Recommended by the agent from scores in your environment, decided by you.
- Data curation. Your traces and documents become a qualified dataset: deduplicated, split so nothing straddles train and eval, checked for leakage and coverage, built from tested functions rather than a script written on the spot.
- Evaluation. Your runtime in a container, your checks as the grader, a holdout frozen before training. A candidate ships only if it beats the base model on your task.
- The loop after launch. Serving is watched. Failures come back as new cases, and retraining runs on hooks you set.
Your provider keeps doing what it does well: hosting the weights and running the jobs. Ashr does the part that was never on its price list.
What stays yours
The weights, the data, the account, the endpoint, the decision to promote. Every run leaves a receipt your auditors can read: what data, what code, what it cost, what it scored, who promoted it. Spend caps are set by you and cannot be raised by the agents.
Most enterprises start with one task and one model, on data they already have and a provider they already pay. We forward-deploy an engineer with the agents until that model clears the bar you set.
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