Baseten is an inference and training platform that lets you:

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Deploy dedicated models with full control

• Package any model for production: Define dependencies, hardware, and custom code without needing to learn Docker. Build with your preferred frameworks (e.g. PyTorch, transformers, diffusers), inference engines (e.g. TensorRT-LLM, SGLang, vLLM), and serving tools (e.g. Triton) as well as any package installable via pip or apt.
• Build complex AI systems: Orchestrate multi-step workflows with Chains, combining models, business logic, and external APIs.
• Deploy with confidence: Autoscale models, manage environments, and roll out updates with zero-downtime deployments.
• Run high-performance inference: Serve synchronous, asynchronous, and streaming predictions with low-latency execution controls.
• Monitor and optimize in production: Monitor performance, debug issues, and export metrics with built-in observability tooling.

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Start fast with model APIs

• Try model APIs: Model APIs provide a fast path to production with reliable, high-performance inference. Use OpenAI-compatible endpoints to integrate models like Llama, DeepSeek, and Qwen, with built-in support for structured outputs and tool calling.

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Pre-train and fine-tune models

• Run training jobs on scalable infrastructure: Launch containerized training jobs with configurable environments, compute (CPU/GPU), and resource scaling. Supports any training framework via a framework-agnostic API.
• Manage artifacts and streamline workflows: Track experiments, organize training runs, and handle large artifacts like checkpoints and logs. Seamlessly transition from training to deployment within the Baseten ecosystem.