Dedicated GPU Pods
Launch a GPU environment with control over the container, storage, GPU type, and runtime. Runpod lists more than 30 GPU SKUs and deployment across 31 regions.
Runpod
Freemium
Cloud GPU infrastructure for AI development, training, inference, and batch workloads.
Runpodとは？
Runpod is an AI developer cloud that combines dedicated GPU instances, autoscaling serverless inference, and multi-node GPU clusters in one platform. Teams can use GPU Pods to control containers, storage, GPU type, and runtime; Serverless to expose inference workloads through autoscaling endpoints; and Clusters for coordinated multi-node jobs. The platform supports workflows from model experimentation and fine-tuning through deployment and production scaling, without moving between separate Runpod accounts.
Runpodでできること
Autoscaling Serverless endpoints
Deploy a handler as an inference endpoint and scale workers in response to demand, including scaling down to zero when no work is running.
Multi-node Clusters
Provision GPU clusters for distributed workloads, attach shared storage, and use Slurm to manage jobs across nodes.
Per-second compute billing
Pods and on-demand Clusters are billed by the second; Serverless workers are billed according to usage. Workload type and storage choices affect total cost.
Container-based workflows
Bring Docker containers or use Runpod templates for inference, training, and research workloads across major AI frameworks.
Operational monitoring
Serverless includes real-time logs, monitoring, and metrics, while Runpod manages task queuing and worker distribution.
利用シーン
“Develop and fine-tune models”
Use a GPU Pod to prepare data, run training or fine-tuning jobs, and retain direct control over the software environment.
“Serve variable-traffic inference”
Deploy model handlers to Serverless endpoints when request volume changes over time and worker capacity should adapt to demand.
“Run distributed training”
Use a multi-node Cluster with high-speed inter-node networking and Slurm when a training job needs coordinated GPUs across machines.
“Process large batch workloads”
Provision on-demand Clusters for compute-intensive batches, attach shared storage, and terminate the cluster when the work is complete.
よくある質問
How do Pods, Serverless, and Clusters differ?
Pods are dedicated GPU instances suited to development and long-running jobs. Serverless provides usage-based, autoscaling inference workers. Clusters connect multiple nodes for distributed or batch workloads.
How are GPU Pods and on-demand Clusters billed?
The pricing information describes per-second billing for Pods and on-demand Clusters. Serverless worker charges are based on usage; storage and deployment choices can also affect total cost.
Can I run my own containers and job scheduler?
Yes. Clusters support user-provided Docker workloads and Slurm commands such as sinfo, sbatch, and srun. Runpod says Clusters are not compatible with Kubernetes.
How large can an on-demand Cluster be?
The cluster page says users can access two nodes on demand, with up to 16 GPUs. Larger clusters up to eight nodes and 64 GPUs require a spend-limit increase.
Can I reserve capacity for larger deployments?
Runpod offers reserved Clusters with dedicated capacity, custom configurations, support and onboarding, an uptime SLA, and discounted rates for longer commitments. Contact sales for availability and terms.
クイック情報
- Product category
- AI developer cloud and GPU infrastructure
- Compute options
- GPU Pods, Serverless, and multi-node Clusters
- GPU availability
- 30+ GPU SKUs across 31 regions
- Cluster networking
- 1,600–3,200 Gbps inter-node bandwidth, depending on configuration
- Billing models
- Per-second billing for Pods and on-demand Clusters; usage-based Serverless workers
- Storage
- Persistent and shared storage options; pricing page lists storage starting at $0.05/GB/month
Runpod のトラフィック分析
トラフィックデータは参考情報としてご利用ください。
- ドメイン評価
- 77
Runpodの代替品
Hyperbolic
hyperbolic.xyz
Hyperbolic is an open-access GPU and AI cloud for deploying on-demand H100, H200, B200, and other GPU capacity. It supports experimentation, training, fine-tuning, inference, and production workloads through on-demand instances, reserved clusters, and Private Cloud infrastructure.
CoreWeave
www.coreweave.com
CoreWeave is an AI-focused cloud platform that combines GPU infrastructure, storage, networking, orchestration, and operational tooling for training and serving AI workloads. It supports teams moving from model experiments to production systems, including reinforcement-learning and agent-development workflows.
Hyperstack
www.hyperstack.cloud
Hyperstack is a cloud GPU platform for running AI and machine learning workloads, including training, inference, data analytics, and model development. It also provides AI Studio, virtual machines, and managed Kubernetes for deploying and operating GPU-backed workloads.
Lambda
lambda.ai
Lambda provides cloud GPU compute for AI training, fine-tuning, inference, and prototyping. Teams can launch on-demand GPU instances, use production-ready 1-Click Clusters, or discuss reserved and single-tenant infrastructure for larger workloads.
Nebius AI Cloud
nebius.com
Nebius AI Cloud is a purpose-built cloud platform for developing, training, and serving AI workloads. It combines GPU and CPU compute, managed Kubernetes, MLOps tooling, and managed or serverless inference for teams scaling from experiments to production.
SambaNova
sambanova.ai
SambaNova is an AI inference platform for developers, enterprises, and infrastructure operators running large open-source models and agentic workloads. It combines cloud services with deployable hardware and software for cloud, on-premises, hybrid, and air-gapped environments.