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 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.
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?
- No — Baseten is a commercial platform billed 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 locally with truss watch live-reload, then run truss push --publish to deploy. Baseten provisions a GPU-backed endpoint with configurable autoscaling, and a GitHub Action can automate deploys from your repo.
Related
- Deploying LLMs to Production: A Reliability & Cost ChecklistTake an LLM feature from prototype to production: API vs self-host, provider fallback, retries, caching, observability, eval gates, and safe rollout.
- Self-Host vs API: When Does Running Your Own LLM Actually Pay Off?The real economics of self-hosting an LLM vs. calling a hosted API — GPU utilization, privacy, latency, and the hidden ops costs that decide the crossover.
- Cold Start OptimizerCut cold-start latency for serverless functions and slow-booting apps by measuring the init breakdown, then attacking the dominant phase — artifact size, eager imports, eager connections, or under-provisioned memory — instead of reflexively buying provisioned concurrency. Use when serverless p99 spikes on the first request, when a function times out during init, or when scale-to-zero is hurting user-facing latency.
- LLM Cost and Latency Engineering: Caching, Right-Sizing, and p95 BudgetsA practical playbook for cutting LLM cost and tail latency — caching, model right-sizing, prompt trimming, and enforced p95 budgets — without losing quality.