Anyscale

Serverless GPUs. Run training, inference, batch jobs from Python.

Define cloud workloads in Python, deploy with one command — GPU access on demand, fast cold starts, fair-share pricing. The default 'I need to fine-tune a model from a Jupyter cell' platform.

Inference cloud for serving any AI model in production.

Production inference platform offering both pre-optimized Model APIs (Llama, DeepSeek, and more, billed per token) and dedicated GPU/CPU deployments for custom models, billed per minute with no charge for idle time. Custom models are packaged with its open-source Truss format and autoscale, including scale-to-zero. Aimed at low-latency, high-throughput serving.

GPU cloud for AI — on-demand instances and serverless inference.

Runpod is an AI developer cloud for renting GPUs on demand or running auto-scaling serverless inference endpoints. Serverless workers bill by the millisecond, scale to zero when idle, and advertise sub-200ms cold starts; on-demand Pods and multi-node Clusters cover training and long-running jobs. A Community Cloud tier offers cheaper, peer-sourced GPUs alongside the vendor-operated Secure Cloud.

Run AI and batch jobs on any cloud or Kubernetes, from one interface.

An open-source framework for running, managing, and scaling AI and batch workloads across Kubernetes, Slurm, and 20+ cloud providers through a single unified interface. It abstracts away per-provider setup, optimizes for cost and GPU availability, and automatically fails over between regions and clouds when capacity is scarce. You run it yourself against your own infrastructure — the software is free and Apache-2.0 licensed; you pay only your own cloud bills.