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Runpod logo
Runpod
✓ verifiedPaid

Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.

2.3M visits/mo
MimicPC logo
MimicPC
✓ verifiedFreemium

Cloud platform to run open-source AI apps like ComfyUI and Stable Diffusion and train LoRAs on rented GPUs, billed hourly.

299K visits/mo
ZenMux logo
ZenMux
✓ verifiedPaid

Enterprise unified API gateway giving one integration point to 100+ LLMs like Claude, GPT, and Gemini with reliability guarantees.

435K visits/mo11K saves
Pricing
Pods A40 48GB: $0.44/hr
Pods RTX 4090 24GB: $0.69/hr
Pods A100 SXM 80GB: $1.49/hr
Pods H100 SXM 80GB: $2.99/hr
Pods H200 141GB: $4.39/hr
Pods B300 288GB: $7.39/hr
Essential: $13.95/mo (+$12 credit)
Advanced: $26.95/mo (+$25 credit)
GPU hardware: from $0.29/hr

Free trial available

No public pricing

Core features
  • On-demand GPU pods across 30+ GPU types and 31 regions
  • Serverless GPU endpoints with sub-200ms cold starts
  • Zero idle cost billing for inference workloads
  • Multi-node clusters for distributed training
  • Persistent network storage for full pipelines
  • Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
  • Pre-installed open-source AI apps (ComfyUI, SD, Fooocus)
  • LoRA and custom model training
  • Image, video, audio, and LLM workflows
  • Hourly GPU rental across several tiers
  • Private storage and shareable workflows
  • No-deployment, browser-based access
  • Unified API for 100+ AI models
  • Intelligent request routing across models
  • AI Model Insurance for quality/reliability guarantees
  • Enterprise-focused LLM access layer
Use cases
  • Renting GPUs for model training and fine-tuning
  • Deploying low-latency real-time inference APIs
  • Running AI agents that need to scale instantly
  • Processing compute-heavy batch or distributed workloads
  • Running ComfyUI/Stable Diffusion without a local GPU
  • Training custom LoRA models
  • Face swapping and voice conversion
  • Generating images, video, and audio at scale
  • Building applications that need failover across multiple LLM providers
  • Consolidating billing/access to many AI models under one API
  • Enterprises requiring guaranteed model output reliability
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