Baseten expands into model lifecycle management

This shifts Baseten from being a fast place to run a model into being a control point for which model gets used, how it is tuned, and how it reaches production. Model APIs let a team swap in open models through an OpenAI compatible endpoint, while Training lets that same team fine tune across multiple nodes and then promote the result straight into a live inference endpoint. That moves Baseten upstream from serving tokens to managing more of the model lifecycle.

• Model APIs change Baseten from infrastructure vendor to model distributor. Instead of bringing a model and configuring GPUs, a developer can call managed open models on shared infrastructure that Baseten operates, with the same client shape used for OpenAI style chat completions.
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• Training closes the loop between experimentation and production. The product is built for multi node fine tuning jobs, then hands the tuned model off to Baseten inference, which is the valuable workflow link because the same platform now owns both model improvement and serving reliability.
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• This also puts Baseten more directly against Together AI and similar full stack model platforms. Together already sells fine tuning and inference through one API, so Baseten adding model supply and training is less a side feature and more a move to defend wallet share as customers consolidate vendors.
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The next step is deeper ownership of model operations, with more customers starting on a hosted open model, tuning it for a specific task, and keeping it on the same platform for production traffic. If that workflow sticks, Baseten captures more revenue per customer and becomes harder to replace than an inference only provider.