RunPod

Overview

Runpod is a purpose-built AI Developer Cloud that provides developers, researchers, and AI teams with instant access to high-performance GPU infrastructure. Designed to eliminate the complexity of procuring and operating hardware, Runpod delivers a full stack of compute options—from serverless inference endpoints to persistent GPU pods and multi-node clusters—all billed by the millisecond with no long-term contracts or minimum commitments. The platform is built for the complete AI lifecycle, enabling users to move from experimentation to production rapidly and cost-effectively.

At the core of Runpod are three complementary infrastructure products. Serverless provides autoscaling GPU endpoints that scale to zero when idle, with sub-200ms cold starts powered by FlashBoot and a built-in job queue distributed across 31 global regions. Pods offer persistent GPU instances for development and stateful workloads, available as Reserved (guaranteed capacity) or Spot (interruptible, lower cost) options. Clusters deliver multi-GPU distributed compute for large-scale training and batch inference, supporting over 200 simultaneous GPUs connected via InfiniBand. All products share a unified GPU catalog that includes H100 80GB HBM3, H100 NVL, A100, L40S, and other leading accelerators, accessible on demand.

Runpod also extends beyond raw compute with supporting infrastructure tailored for AI workloads. Network volumes allow teams to share memory, model weights, and data across workers, while the Runpod Hub facilitates discovery and deployment of pre-configured AI resources. For production-grade requirements, Runpod offers a 99.99% uptime SLA, enterprise-grade compliance certifications (including SOC 2, ISO 27001, and HIPAA, depending on region), and Secure Cloud for network-isolated environments. A Runpod skills package enables coding agents such as Claude Code and Cursor to deploy and manage Runpod resources directly, streamlining agentic development workflows.

The platform is engineered to support the full spectrum of AI use cases: low-latency inference for LLMs and diffusion models, fine-tuning of open-source and proprietary architectures, persistent hosting for AI agents, and compute-heavy batch jobs such as rendering or simulations. By combining elastic scaling, millisecond-level billing, and a global footprint, Runpod helps teams reduce infrastructure overhead, accelerate time-to-market, and scale AI workloads without the operational burden of managing physical GPU clusters.

Core capabilities

Serverless GPU Endpoints

Autoscaling inference endpoints with sub-200ms cold starts powered by FlashBoot, scaling to zero when idle to minimize cost.

Pods (Reserved & Spot)

Persistent GPU instances for development and stateful workloads, with guaranteed reserve capacity or lower-cost interruptible spot instances.

Multi-Node Clusters

Distributed multi-GPU computing for large-scale training and batch inference, supporting 200+ simultaneous GPUs with InfiniBand connectivity.

Millisecond Billing

Pay only for actual usage, billed by the millisecond, with no contracts or minimum commitments.

Global Infrastructure

31 global regions with a comprehensive GPU catalog including H100 80GB HBM3, H100 NVL, A100, and L40S.

Network Volumes

Shared storage for distributing model weights, memory, and data across workers in distributed workloads.

Runpod Hub

A catalog for discovering and deploying pre-built AI models, templates, and resources quickly.

Secure Cloud

Network-isolated environments for workloads with stricter compliance and security requirements.

Enterprise Compliance & SLA

99.99% uptime SLA, with SOC 2, ISO 27001, and HIPAA certifications available depending on data center location.

AI Agent Skills Package

Enables coding agents like Claude Code and Cursor to directly deploy and manage Runpod resources.

Use cases

Low-latency production inference for LLMs, vision models, and diffusion models via Serverless endpoints

Fine-tuning open-source models such as Llama or Mistral using persistent GPU pods

Distributed training of large models on multi-node clusters with InfiniBand networking

Hosting AI agents with persistent memory and state using always-on pods and network volumes

Running batch inference or compute-heavy jobs like rendering, simulation, or data processing

Rapid prototyping and iterative development in interactive Jupyter or VS Code environments

Compliance-sensitive AI workloads using network-isolated Secure Cloud environments