# Baseten

> Production inference platform for ML and LLM models — autoscaling GPU deployments, scale-to-zero, and packaging via the open-source Truss framework.

Baseten runs ML and LLM models in production: package serving logic with the open-source Truss framework, push it, and get an autoscaling GPU endpoint. It handles containerization, multi-region/multi-cloud replicas, scale-to-zero, and per-deployment observability — built for serving your own and open-source models at production scale.

Website: https://www.baseten.co

Baseten is an **inference platform for putting your own ML and LLM models into production**. Rather than calling a hosted model API, you bring a model — custom, fine-tuned, or open-source — package its serving logic, and Baseten runs it on GPU infrastructure as an autoscaling endpoint. It is built around productionizing models you control, with the operational concerns (containerization, scaling, observability, high availability) handled for you.

Packaging happens through **Truss**, Baseten's open-source (MIT) framework. You define the model server and its dependencies in Python, iterate locally with `truss watch` live-reload, and deploy with `truss push --publish`. Truss serves models from frameworks like vLLM, SGLang, TensorRT-LLM, transformers, diffusers, PyTorch, and TensorFlow, and **Truss Chains** compose multi-step, multi-model workflows.

Dedicated deployments **autoscale on traffic** — configurable min/max replicas, concurrency targets, and **scale-to-zero** so idle deployments cost nothing. Baseten supports multi-region and multi-cloud replicas for high availability, plus per-deployment dashboards covering request volume, latency, GPU utilization, and logs. Alongside dedicated deployments, it offers pre-optimized Model APIs for quick evaluation and training that lands directly on inference-ready infrastructure.

Billing is **usage-based GPU compute**, metered per minute a replica runs across hardware tiers (from T4 up to B200), making cost track actual serving time rather than a flat seat fee.

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_Source: https://agentscamp.com/tools/baseten — Tool on AgentsCamp._