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The web interface for managing your compute resources, account, teams, and billing.
A pay-as-you-go compute solution designed for dynamic autoscaling in production AI/ML apps.
A dedicated GPU or CPU instance for containerized AI/ML workloads, such as training models, running inference, or other compute-intensive tasks.
An AI model API hosted by Runpod that you can access directly without deploying your own infrastructure.
A managed compute cluster with high-speed networking for multi-node distributed workloads like training large AI models.
Persistent storage that exists independently of your other compute resources and can be attached to multiple Pods or Serverless endpoints to share data between machines.
A storage interface compatible with Amazon S3 for uploading, downloading, and managing files in your network volumes.
A repository for discovering, deploying, and sharing preconfigured AI projects optimized for Runpod.
Container
A Docker-based environment that packages your code, dependencies, and runtime into a portable unit that runs consistently across machines.
Data center
Physical facilities where Runpod’s GPU and CPU hardware is located. Your choice of data center can affect latency, available GPU types, and pricing.
Machine
The physical server hardware within a data center that hosts your workloads. Each machine contains CPUs, GPUs, memory, and storage. 
