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.
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 with truss watch, which live-patches a development deployment, and deploy with truss push (published deployments are now the default). 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.
Frequently asked questions
- What is Baseten?
- Baseten is an inference platform for deploying ML and LLM models into production on autoscaling GPU infrastructure. You package a model with the open-source Truss framework, run truss push, and Baseten handles containerization, scaling, and a live API endpoint.
- Is Baseten free?
- It's freemium: the entry Basic plan is pay-as-you-go with no monthly fee, and new accounts come with credits to experiment with deployments for free. After that, Baseten bills by usage on GPU compute, metered per minute a replica is running across hardware tiers (T4 through B200). Deployments can scale to zero to avoid charges while idle. The Truss packaging framework is separately open-source under MIT.
- How do I deploy a model on Baseten?
- Package your model's serving logic with Truss, iterate with truss watch, which live-patches a development deployment, then run truss push to deploy; published deployments are now the default. Baseten provisions a GPU-backed endpoint with configurable autoscaling, and a GitHub Action can automate deploys from your repo.
Filed under
inference · gpu · deployment · autoscaling · truss · mlops
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