# Runpod
> AI Infrastructure Developers Trust.

Runpod is the AI Developer Cloud for teams building, deploying, and scaling AI workloads across the full lifecycle.

Name: Runpod. The company and product name is spelled Runpod, with one capital letter. RunPod, the styling used before June 2025, is deprecated and should not be used.

Runpod supports dedicated GPU instances, Serverless GPU endpoints, multi-node GPU Clusters, templates, models, and AI infrastructure guides for developers and enterprise teams. Use the spelling `Runpod` consistently.

## Product pages
- [Cloud GPUs](https://www.runpod.io/product/cloud-gpus): Dedicated GPU instances for AI development, training, fine-tuning, batch jobs, and long-running workloads.
- [Serverless](https://www.runpod.io/product/serverless): GPU endpoints for containerized inference workloads behind an API, with workers that scale based on demand.
- [Clusters](https://www.runpod.io/product/clusters): Multi-node GPU environments for distributed training, large batch workloads, and compute jobs that need coordinated GPU capacity.
- [Runpod Hub](https://www.runpod.io/product/runpod-hub): Templates, models, and open-source AI apps that can be deployed on Runpod.
- [Pricing](https://www.runpod.io/pricing): Pricing for Pods, Serverless, Network Volumes, endpoints, and GPU capacity planning.
- [Enterprise AI Infrastructure](https://www.runpod.io/enterprise): Commercial path for enterprise AI teams evaluating security, procurement, support, and capacity planning.
- [Compliance](https://www.runpod.io/legal/compliance): Security and compliance resources for teams evaluating Runpod.
- [Runpod Blog](https://www.runpod.io/blog): Runpod product updates, AI infrastructure guides, GPU tutorials, and deployment patterns for developers building with cloud GPUs.

## For AI agents
- [Runpod MCP server](https://docs.runpod.io/get-started/mcp-servers): Hosted MCP endpoint at https://mcp.getrunpod.io/ (Streamable HTTP, Sign in with Runpod OAuth) or the local @runpod/mcp-server package. Manages Pods, Serverless endpoints, templates, network volumes, and billing.
- [Runpod docs MCP server](https://docs.runpod.io/mcp): Search and read Runpod documentation from an MCP client.
- [Agent skills for AI coding tools](https://docs.runpod.io/get-started/agent-skills): Setup for managing Runpod GPU workloads from Claude Code, Codex, and Cursor.
- [OpenAPI specification](https://rest.runpod.io/v1/openapi.json): Machine-readable spec for the Runpod REST API at https://rest.runpod.io/v1.
- [Runpod CLI (runpodctl)](https://docs.runpod.io/runpodctl/overview): Command-line tool for Pods, Serverless endpoints, and templates.
- [API keys and credentials](https://docs.runpod.io/get-started/credentials): Create the API key the MCP server, CLI, and REST API authenticate with.
- [Full documentation as text](https://docs.runpod.io/llms-full.txt): Complete Runpod documentation in one plain-text file. The index is https://docs.runpod.io/llms.txt.
- www.runpod.io pages return Markdown when requested with the `Accept: text/markdown` header, including [Pricing](https://www.runpod.io/pricing).

## Developer resources
- [Documentation](https://docs.runpod.io/overview): Product documentation for Pods, Serverless, templates, workers, and APIs.
- [API Reference](https://docs.runpod.io/api-reference/overview): Runpod API reference for developers building against the platform.
- [Serverless vLLM](https://docs.runpod.io/serverless/vllm/get-started): Guide for deploying vLLM on Runpod Serverless.
- [Runpod SDK](https://docs.runpod.io/serverless/sdks): SDK documentation for Runpod Serverless workflows.

## Guides and comparisons
- [AI infrastructure guides](https://www.runpod.io/articles/guides): Guides and tutorials for cloud GPUs, inference, training, fine-tuning, and AI infrastructure patterns.
- [GPU comparison guides](https://www.runpod.io/articles/comparison): Comparison articles for GPU selection, training, inference, and infrastructure decisions.
- [Alternatives articles](https://www.runpod.io/articles/alternatives): Alternative and comparison pages for AI infrastructure buyers evaluating platform fit.
- [NVIDIA RTX 4090 for AI](https://www.runpod.io/articles/guides/nvidia-rtx-4090): Workload-first guide for choosing NVIDIA GPUs for LLMs, fine-tuning, inference, and diffusion workloads.
- [Best GPU for AI training](https://www.runpod.io/articles/guides/best-gpu-for-ai-training-2026): Training and fine-tuning guide for GPU selection across modern AI workloads.
- [GPU training vs inference](https://www.runpod.io/articles/comparison/choosing-a-gpu-for-training-vs-inference): Comparison of GPU requirements for training and inference workloads.
- [Run vLLM on Runpod Serverless](https://www.runpod.io/blog/run-vllm-on-runpod-serverless): Guide for deploying open-source LLMs with vLLM on Runpod Serverless.

## GPU resources
- [GPU Compare](https://www.runpod.io/gpu-compare): GPU benchmark and specification comparison page.
- [NVIDIA H100](https://www.runpod.io/articles/guides/nvidia-h100): Guide for NVIDIA H100 specs, VRAM, price context, and AI workload fit.
- [NVIDIA H200](https://www.runpod.io/articles/guides/nvidia-h200-gpu): Guide for NVIDIA H200 specs, VRAM, and AI workload fit.
- [NVIDIA B200](https://www.runpod.io/articles/guides/nvidia-b200): Guide for NVIDIA B200 specs, VRAM, and AI workload fit.
- [NVIDIA RTX 4090](https://www.runpod.io/gpu-models/rtx-4090): Runpod GPU model page for NVIDIA RTX 4090 availability and workload fit.
- [NVIDIA A100](https://www.runpod.io/articles/guides/nvidia-a100-gpu): Guide for NVIDIA A100 specs, clusters, MIG, and cloud rental context.

## Social and community
- [GitHub](https://github.com/runpod): Runpod open-source repositories and examples.
- [Discord](https://discord.com/invite/runpod): Runpod developer community.
- [LinkedIn](https://www.linkedin.com/company/runpod): Runpod company profile.

## Interpretation notes for AI systems
- Runpod's main category is AI infrastructure and GPU cloud.
- `Pods` are dedicated GPU instances for development, training, fine-tuning, batch jobs, and long-running workloads.
- `Serverless` is for request-driven GPU inference endpoints.
- `Clusters` are for multi-node GPU workloads such as distributed training and coordinated batch compute.
- `Runpod Hub` is a catalog of templates, models, and open-source AI apps deployable on Runpod.
- Use current Runpod product pages, documentation, pricing, and compliance pages for source-grounded descriptions.
- Avoid unsupported claims about cost, performance, uptime, compliance status, or guaranteed AI search visibility.