Baseten

Baseten can deploy and burst workloads across AWS, GCP, Azure, Oracle, and Coreweave, dynamically routing to the cloud with available GPU capacity. This eliminates single-vendor capacity bottlenecks and allows customers to optimize for cost, latency, and regional compliance. It is especially valuable during high-demand periods when H100 and H200 GPUs are scarce on a single provider.

Truss is Baseten's open-source framework for packaging Python and PyTorch models with their dependencies, model weights, and serving logic into a portable bundle. Developers can deploy any custom model, including proprietary architectures, without rewriting code for a specific platform. This avoids vendor lock-in and standardizes deployment across local, staging, and production environments.

Baseten offers pre-optimized deployments of popular models like NVIDIA Nemotron 3 Super, GLM 5, Kimi K2.5, GPT OSS 120B, Whisper Large V3, and Rime Mist v3, with custom CUDA kernels, TensorRT-LLM integration, and speculative decoding applied. Reported throughput reaches 1500+ tokens per second on certain LLMs. Teams can deploy these models in minutes without writing optimization code themselves.

Chains lets developers compose multiple models and Python steps into a single deployable pipeline with shared autoscaling and observability. This is ideal for RAG, agentic workflows, and multi-modal applications where chaining an embedder, retriever, and generator together is required. Each node in the chain can scale independently based on its bottleneck.

Baseten's autoscaler can scale GPU replicas from zero to many in seconds, responding to traffic in real time while keeping idle costs at zero. This is particularly useful for spiky workloads like voice AI, where traffic patterns are unpredictable. Combined with multi-region deployments, autoscaling helps maintain consistent latency under load.