Domain models trained on your data, inside your network, delivered as weights you own outright. Fine-tuning where that is enough. Continued pretraining where it is not. Pretraining from scratch when your data has no usable base model.
One week scoping engagement first · full builds $15k to $120k · trained in your environment or ours
Studio already runs inference on your own hardware. No prompts leave, no schemas leave, no sample rows leave. That solves the sovereignty problem.
It does not solve the quality problem. The checkpoint you point Studio at is a general open-weights model trained on public web text. It has never seen your product taxonomy, your regulatory vocabulary, your customers' languages, or your internal document formats. You get privacy and mediocrity in the same package.
Foundry closes that. Same perimeter, same GPUs, a model that actually knows your domain.
A custom model is the right answer less often than the market suggests. These are the cases where it holds up, and the case where it does not.
Domain jargon, regulatory language, internal taxonomies, and document formats that public web text never covered. Accuracy on your task, not on a benchmark.
A general frontier API on a repeated narrow task is the most expensive way to solve it. A small model trained on that one task runs at a fraction of the cost, on hardware you already own.
Training data is the most sensitive data you have. If a hosted fine-tuning API is closed to you for the same reasons hosted inference is, this is the only remaining path.
Most teams asking for a custom model need retrieval. We will tell you that in four days and charge you for the four days, not the four months. The projects that survive that filter are the ones worth doing.
There are three rungs. Picking the right one is most of the value we add, and it usually saves you money.
Instruction tuning or LoRA on an open-weights base. Delivered as safetensors and GGUF, deployable on your existing vLLM or Ollama setup.
Corpus construction from your own data, continued pretraining on an open base, then post-training. Delivers most of the value of a from-scratch build at a fraction of the cost. This is the right answer for the large majority of teams who arrive asking for rung 3.
Tokenizer design, corpus pipeline, architecture selection, pretraining, post-training, eval harness, technical report.
The clock starts at data access, not at signature. The training run is rarely the long pole: a 9 billion parameter model over 122 billion tokens is days of wall clock on the right pod, so we compress by widening the pod and passing the compute through at cost, not by cutting evaluation. Corpus readiness is what actually moves the date.
Every engagement starts with a paid audit that can conclude you should not build a model at all.
One week, priced separately. We audit your data, assess tokenizer fit, estimate a token budget, and recommend a rung. You get a written report whether or not you continue. Roughly half of these conclude that retrieval or a fine-tune is enough, and we say so.
Your training data is built as a versioned pipeline with contracts enforced at compile time: PII redaction verified, class balance checked, language mix asserted. Lineage is emitted, so you can answer which source rows produced which model version.
Runs in your environment, your cloud account, or an India-resident GPU provider, whichever your compliance boundary requires. Evaluation is against a held-out set built from your ground truth, not public benchmarks.
Weights you own outright, in safetensors and GGUF. Eval harness and training configuration handed over. Deployable behind Studio on your own vLLM or Ollama, or standalone.
Billing is by milestone: 30% on corpus delivery, 30% at the mid-training checkpoint with evaluation, 40% on final delivery. Compute is passed through at cost plus 15% and billed separately.
Most vendors offering custom models are running someone else's fine-tuning API behind a project plan. We pretrained a 9 billion parameter mixture-of-experts model from scratch: 122 billion tokens, Google TPU v6e Trillium, MaxText and JAX. Tokenizer, corpus pipeline, architecture, training loop, evaluation.
We published the full technical report, including the benchmarks where it underperforms and the decisions we would make differently. That is the level of detail you should demand from anyone you let near your training data.
A model engineer reads every one of these and replies in writing, usually within two working days. No sequence, no nurture track.
Prefer email? Write to contact@thinkingdbx.com. If your question is really "which rung do I need", a scoping call answers it faster.
Bring the task, the data you have, and the constraint you are working under. We will tell you which rung you need, including when the answer is none of them.
Or email contact@thinkingdbx.com.
safetensors and GGUF, owned outright, no licence to renew.
Built on your ground truth, so you can re-run it on every future version.
Versioned, contract enforced, with lineage from source rows to model version.
The exact configs and report, so another team could reproduce the run.