Baseten runs AI models as production inference endpoints. You deploy open-source, custom, or fine-tuned models onto infrastructure the company operates, and it handles serving, scaling, and the API in front of them.
Alongside dedicated deployments, it offers pre-optimized Model APIs for testing a workload or evaluating a new model without configuring a deployment first, and a training path that uses the Loops SDK so a model trained there deploys onto the same stack. Model providers can also distribute and monetize their own models on the platform.
Deployments run in Baseten's cloud or in your own virtual private clouds, across regions and cloud providers, with single-tenant clusters for workload isolation and a hybrid option that adds on-demand capacity from Baseten Cloud. The platform documents 99.99% uptime and cross-cloud high availability for its managed option.
Features
- Dedicated inference: serve open-source, custom, and fine-tuned models on infrastructure built for high-scale workloads
- Model APIs: pre-optimized endpoints for prototyping a product or evaluating a new model without setting up a deployment
- Training: train models with frontier reinforcement learning through the Loops SDK and deploy them onto the same inference stack
- Runtime optimization: custom kernels, recent decoding techniques, and caching built into the Baseten Inference Stack
- Cross-cloud scale: workloads spread across regions and cloud providers, in Baseten's cloud or in yours
- Deployment isolation: single-tenant clusters and self-hosted deployments, with optional on-demand flex capacity on Baseten Cloud
- Model lab distribution: model labs publish and monetize a model on Baseten infrastructure
- Forward deployed engineers: hands-on support for building, optimizing, and scaling a deployment
