AllocateOwn your model from day one,
before your data becomes someone else’s moat.
Build on frontier APIs today and graduate to your own fine-tuned open weights as your data compounds, without changing a line of product code.
Start on frontier, own the route
Route by name from day one; when your traffic justifies it, swap in your own model with zero deploy.
Fine-tune when the data arrives
Every task your product completes becomes training signal for a model your competitors can’t rent.
Spend that scales with you
Metered tokens, hard caps, and no idle cost while you find product-market fit.
The startup program: platform credits, hands-on onboarding, and the full platform from your first customer.
Models teams route here.
Start on frontier, fine-tune the open ones on your own data.
Common questions
Why not just call a lab’s API directly?
You can, and at first the result looks the same. The difference appears later: routes mean you can swap models without rewrites, metering means you see unit economics per feature, and captured outcomes mean you can eventually train a model competitors cannot rent.
When should a startup fine-tune?
When you have a few thousand real resolved cases and a workflow where accuracy is the product. Before that, route to the right frontier or open model per task and let the data accumulate.
What does it cost before we have traffic?
Nothing while idle: billing is metered per token. The startup program adds platform credits to get you live.
Does building here lock us in?
The wire is OpenAI-compatible and your fine-tuned weights are contractually yours and exportable. The lock-in question inverts: the asset you accumulate is a model you own, not a dependency.