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Baseten

Freemium

Deploy AI models in production with high-performance inference across any cloud.

Baseten is a training and inference platform that turns open-source, custom, and fine-tuned AI models into production API endpoints. It combines the Baseten Inference Stack — optimized engines, autoscaling, and multi-cloud capacity management — with the Truss packaging framework, pre-optimized Model APIs, and a managed training platform. Teams can deploy models via config-only files or custom Python code, orchestrate multi-step pipelines with Chains, and run workloads on Baseten Cloud or inside their own VPCs. It is built for engineering and ML teams that need reliable, low-latency inference for large language models, image generators, speech models, embeddings, and compound AI systems.

model servingLLM inferenceGPU inferenceAI deploymentBaseten Inference StackOpenAI-compatible APITrussautoscalingAutomated DeploymentAutomated Workflow ExecutionTask ExecutionData MonitoringSystem MonitoringeditingmonitoringWorkflow AutomationCode AssistantAI Agents

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What is it

A training and inference platform that packages, serves, and scales AI models through the Baseten Inference Stack, with options for managed cloud, single-tenant clusters, or self-hosted deployments.

What it can do

Deploy custom models with Truss, call pre-optimized frontier models through OpenAI-compatible Model APIs, build multi-step pipelines with Chains, train and fine-tune models with Loops or Training Jobs, and autoscale workloads across multiple clouds and regions.

Who is it for

ML engineers, AI engineering teams, and product builders in startups and enterprises that need production-grade inference for LLMs, image, audio, video, and embedding workloads.

Key Features

Baseten Inference Stack — Optimized Model Runtime

The platform compiles models with engines such as TensorRT-LLM, BIS-LLM for mixture-of-experts, and BEI for embeddings, and bakes in custom kernels, advanced decoding, and caching to reduce latency and increase throughput.

Model APIs — Pre-optimized Frontier Models

On-demand endpoints for models like DeepSeek V4, Kimi K2.6, GLM 5.1, and GPT-OSS 120B are OpenAI-compatible, support structured outputs and tool use, and run on the latest-generation GPUs.

Dedicated Deployments — Custom Model Serving

Package any open-source, fine-tuned, or proprietary model with Truss and deploy it on dedicated GPU instances, with control over hardware, autoscaling limits, concurrency targets, and scale-to-zero behavior.

Baseten Chains — Compound AI Workflows

Orchestrate multi-step pipelines such as RAG, image generation plus upscaling, or safety filtering, where each step runs on its own hardware with its own dependencies and scales independently.

Use Cases

Serve Production LLM Chatbots

Deploy large language models with autoscaling and low-latency runtimes so customer-facing assistants remain responsive during traffic spikes without over-provisioning GPUs.

Run Image Generation Workflows

Serve custom diffusion models or ComfyUI workflows on inference-optimized GPUs, then chain upscaling and safety filtering steps for end-to-end image pipelines.

Power Voice Agents and Text-to-Speech

Use real-time audio streaming and state-of-the-art text-to-speech endpoints to drive AI phone calls, voice agents, translation, and live captioning with sub-second time-to-first-byte.

Deploy Embedding Pipelines for Search and RAG

Run high-throughput embedding and reranking models with Baseten Embeddings Inference, then feed vectors into retrieval and generation steps via Chains.