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RunPod Pods

RunPod Pods are dedicated cloud GPU instances for AI development, training, inference, batch jobs, and long-running workloads.

RunPod Pods

RunPod Pods provide dedicated GPU environments with control over the GPU type, container, storage, drivers, and runtime. Users can deploy custom Docker images, run Linux GPU frameworks, and rent GPUs across 31 regions with billing by the second.

They are used by researchers, developers, and teams for model training, fine-tuning, inference, image and video generation, and AI agents. GPU compute is usage-priced, while container disks, volume disks, and network storage cost extra. Pods are separate from RunPod Serverless endpoints and multi-node Clusters.

Features

  • Deploy dedicated GPU instances in under 30 seconds
  • Choose from 30+ GPU models across 31 global regions
  • Control containers, storage, GPU type, drivers, and runtime
  • Run custom Docker images from registries
  • Use per-second GPU billing with no long-term commitment
  • Manage instances through an API, CLI, SDKs, and CI/CD
  • Attach persistent or temporary storage
  • Use spot instances at discounted rates with eviction risk

Use cases

  • Run low-latency LLM inference with H100 or L40S instances
  • Train and fine-tune models on A100 or H100 SXM GPUs
  • Generate images and video with RTX 4090 or RTX A6000 GPUs
  • Host stateful AI agents on persistent GPU instances
  • Process batch jobs and other compute-heavy workloads
  • Deploy custom containers with preferred ML frameworks

Pros

    Cons

      Latest updates

      • Terraform provider 2.0.0 (2.0.0)

        Built on REST API v2, it adds support for Instant Clusters, global volumes, CPU Pods and endpoints, FlashBoot tiers, and v2 billing.

      • Terraform provider 1.x to 2.0.0 migration (2.0.0)

        Upgrading from 1.x requires configuration and state changes; remove base_url if set and follow the migration guide.

      • New Release Terraform provider 2.0.0 (2.0.0)

        Version 2.0.0 of the Runpod Terraform provider is built on the REST API v2 and adds support for Instant Clusters, global volumes, CPU Pods and endpoints, FlashBoot…

      • Breaking Terraform provider 1.x to 2.0.0 migration (2.0.0)

        Upgrading from 1.x requires configuration and state changes. 2.0.0 makes no REST v1 calls.

      • Runpod SDK 2.0 (2.0)

        Flash is now part of the runpod Python package; define queue endpoints, load-balanced APIs, and tasks that run on their own Pod.

      Capabilities

      • Command line — “Deploy and manage directly from your terminal.” source
      • API — “Automate everything with a simple, flexible API.” source
      • Official SDKs — “CLI & SDKs.” source
      • Runs models for you — “Instant access to pre-deployed AI models via API. No infrastructure setup required.” source

      Get it

      Pricing

      Prices checked
      2026-10-25

      B300

      • Community Cloud — 6.94 USD
      • $ 8.99 /hr
      • 288 GB HBM3e
      • 251 GB RAM
      • 32 vCPUs

      H200

      • Community Cloud — 3.59 USD
      • $ 5.29 /hr
      • 141 GB VRAM
      • 276 GB RAM
      • 24 vCPUs

      B200

      • Community Cloud — 5.98 USD
      • $ 7.99 /hr
      • 180 GB VRAM
      • 283 GB RAM
      • 28 vCPUs

      RTX Pro 6000

      • Community Cloud — 1.69 USD
      • $ 2.49 /hr
      • 96 GB VRAM
      • 188 GB RAM
      • 16 vCPUs

      H100 NVL

      • Community Cloud — 2.59 USD
      • $ 3.19 /hr
      • 94 GB VRAM
      • 94 GB RAM
      • 16 vCPUs

      H100 PCIe

      • Community Cloud — 1.99 USD
      • $ 2.89 /hr
      • 80 GB VRAM
      • 188 GB RAM
      • 16 vCPUs

      H100 SXM

      • Community Cloud — 2.69 USD
      • $ 3.99 /hr
      • 80 GB VRAM
      • 125 GB RAM
      • 20 vCPUs

      A100 PCIe

      • Community Cloud — 1.19 USD
      • $ 1.79 /hr
      • 80 GB VRAM
      • 117 GB RAM
      • 8 vCPUs

      A100 SXM

      • Community Cloud — 1.39 USD
      • $ 1.79 /hr
      • 80 GB VRAM
      • 125 GB RAM
      • 16 vCPUs

      Pro 6000 MIG 48GB

      • $ 1.29 /hr
      • 48 GB VRAM
      • 62 GB RAM
      • 8 vCPUs
      Official website