Baseten

Baseten is a model deployment and inference infrastructure platform built for teams shipping custom machine learning models—including open-weight LLMs, diffusion models, embedding models, and fine-tuned transformers—into production without owning GPU fleets. At its core is Truss, an open-source Python framework for packaging models with their dependencies, pre/post-processing code, and hardware requirements into a portable, reproducible artifact that runs identically in local dev and on Baseten's cloud. The platform layers optimized inference engines (custom TensorRT-LLM, vLLM, and proprietary runtime optimizations) on top of multi-cloud GPU capacity spanning A10G, L4, A100, and H100 instances, handling autoscaling (including scale-to-zero), request batching, and streaming token output for real-time generation workloads. Beyond single-model serving, Baseten offers Baseten Chains, a framework for composing multi-step, multi-model inference pipelines (e.g., retrieval + reranking + generation) with independent autoscaling per component, reducing latency and cost versus monolithic deployments. The platform exposes REST APIs, a CLI, and a Python SDK for model push, versioning, and traffic management, and integrates with existing MLOps workflows via webhooks and OpenTelemetry-based observability for latency, GPU utilization, and error tracking. Target users are ML engineers and infrastructure teams at AI-native startups and enterprises who need production-grade inference performance and reliability without building and maintaining their own Kubernetes-based GPU orchestration stack, but want more control than a fully black-box model API provides.