Platform overview
If you are asking what does baseten do, the short answer is that Baseten provides infrastructure for deploying and serving AI models. It helps teams turn a model into an application-facing inference service, rather than requiring them to build the serving layer entirely themselves.
Baseten is an AI infrastructure platform focused on putting models to work in applications. Its central job is model serving: receiving requests, running a model, and returning results.
PurposeBaseten helps teams deploy AI models as services that applications can call.
The definition is a starting point. These guides look at where to access the platform, how an inference workflow is approached, and what to assess before sending data.
Model serving connects an existing model to the software that needs its predictions or generated output. The exact setup depends on the model and application.
A team chooses the model it wants to run and determines its input and output requirements. A text model, for example, needs a different request shape from an image model.
The model is made available through inference infrastructure. That infrastructure handles the operational work of accepting requests and running the deployed model.
Application code sends inputs to the endpoint and uses the returned output. The team still needs to test response quality, handle errors, and monitor whether the result meets its needs.
Baseten can provide a serving layer, but serving a model is not the same as choosing, evaluating, or governing it. These are the practical requirements around an inference project.
A defined task and a model suitable for that task — Infrastructure does not establish whether a model gives useful or reliable answers.
Inputs and expected outputs your application can handle — The application must know what to send and how to use or reject a response.
A plan for testing quality, failures, and data handling — Deployment does not replace evaluation or a review of sensitive information.
Custom application features around the modeloptional — A user interface or business workflow may be needed, but it is separate from inference serving.
Baseten is most relevant to teams building software that calls AI models, rather than people looking for a finished chat or image-editing app.
Application work
Step 1
A developer may need a backend service that accepts an application request, calls a model, and returns a result to the user. Baseten addresses the model-serving part of that path. The surrounding interface, error messages, and decision about when to call the model remain application work.
Model operations
Step 2
These teams may have a model they want other services to use. Baseten gives them infrastructure to consider for deployment and inference, while they retain responsibility for selecting the model, checking its behavior, and deciding whether it meets their organization's requirements. That division matters when moving from a promising experiment to a dependable application.
Knowing what Baseten does can help you separate model serving from the model and application built around it. If you are exploring AI tools more broadly, the link below opens Synexa, a separate service; it is not a Baseten deployment console.
Baseten provides infrastructure for deploying and serving AI models. In practical terms, it helps teams make a model available for inference requests from their applications.
Model serving and application development are different jobs. Baseten addresses the infrastructure used to run a model, while the team building an application decides how users interact with it and how its output is handled.
No. Deployment makes inference possible, but the model's output still needs to be tested against the intended task. Teams must evaluate quality, failure cases, and any requirements for handling data.
An application sends an inference request containing input appropriate to the deployed model. The model produces an output that the application can then process or show to a user; the specific input and output depend on the model.