- Create, monitor, and manage Pods for persistent workloads.
- Deploy and scale Serverless endpoints for AI inference.
- Configure network volumes for data persistence.
- Integrate Runpod’s GPU computing power into your existing applications and CI/CD pipelines.
Available resources
The Runpod API provides complete access to Runpod’s core resources:- Pods: Create and manage persistent GPU instances for development, training, and long-running workloads. Control Pod lifecycles, configure hardware specifications, and manage SSH access programmatically.
- Serverless endpoints: Deploy and scale containerized applications for AI inference and batch processing. Configure autoscaling parameters, manage worker pools, and monitor job execution in real-time.
- Network volumes: Create persistent storage that can be attached to multiple resources. Manage data persistence across Pod restarts and share datasets between different compute instances.
- Templates: Save and reuse Pod and endpoint configurations to standardize deployments across projects and teams.
- Container registry authentication: Securely connect to private Docker registries to deploy custom containers and models.
- Billing and usage: Access detailed billing information and resource usage metrics to optimize costs and monitor spending across projects.