Look like infrastructure, sell confidence
On the surface Baseten is inference infrastructure — a place to run models on GPUs. The public positioning is more precise: it sells the move from a fragile prototype to a production deployment an engineering team can actually trust under load.
That reframing matters because it changes who the buyer is. The purchase decision isn't "cheapest GPU" — it's "the deployment that won't page us at 2am." Confidence, not compute, is the product.
Meter the workload, not the seat
Baseten's commercial unit is the workload: tokens served, GPU minutes, model runs. This is the correct meter for a product whose value scales with usage — a seat-based price would misalign cost from the thing being bought.
For any product where the value is work performed rather than people logged in, the seat is the wrong unit. Metering the workload keeps price tracking value as an account grows.
Expand through reliability and controls
Once a workload runs in production, expansion comes from the surrounding needs: autoscaling, observability, enterprise controls, and the reliability guarantees a larger team requires. Each is a reason to consolidate more of the inference stack on one layer.
The lesson
Sell the outcome the buyer actually fears losing — a model that breaks in production — and meter the workload that outcome depends on. The infrastructure framing is the wrapper; engineering confidence is the sale.