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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

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
Vast ai logo
Vast ai
✓ verifiedPaid

GPU rental marketplace with per-second billing across thousands of GPUs, aimed at AI training, inference, and fine-tuning workloads.

1.4M visits/mo
Replicate AI logo
Replicate AI
✓ verifiedPaid

Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.

1.3M visits/mo17K saves
AI/ML API logo
AI/ML API
✓ verifiedPaid

Single API and playground for 1000+ AI models (chat, image, video, audio) with pay-as-you-go billing.

223K visits/mo5.7K saves
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
Pricing
Essential: $13.95/mo (+$12 credit)
Advanced: $26.95/mo (+$25 credit)
GPU hardware: from $0.29/hr

Free trial available

No public pricing

CPU (Small): $0.000025/sec ($0.09/hr)
Nvidia A100 80GB: $0.0014/sec ($5.04/hr)
Nvidia H100: $0.001525/sec ($5.49/hr)

Free trial available

Pay As You Go: $20 top-up (pay per use, all models)

Free trial available

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
Core features
  • 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
  • On-demand GPU cloud with per-second billing
  • Interruptible instances at discounted rates for batch/fault-tolerant jobs
  • Reserved capacity with 1, 3, or 6-month terms for steady workloads
  • Serverless deployment with autoscale-to-zero for inference endpoints
  • Dedicated multi-node clusters with InfiniBand for large-scale training
  • Python SDK and CLI plus REST API for programmatic provisioning
  • Access to 68+ GPU types across 40+ data centers
  • Pre-configured templates for popular open-source models
  • One-line API calls to run community and proprietary AI models
  • Support for image, video, speech, and LLM generation models
  • Fine-tuning and custom model deployment via Cog
  • Per-second usage billing on shared or dedicated hardware
  • Automatic scaling for high-traffic private models
  • Thousands of community-published models with production APIs
  • One API for 1000+ models
  • OpenAI/Anthropic-compatible endpoints
  • Chat, image, video, audio and embedding models
  • AI playground/sandbox
  • Pay-as-you-go billing across models
  • Enterprise dedicated infrastructure option
  • 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
Use cases
  • Running ComfyUI/Stable Diffusion without a local GPU
  • Training custom LoRA models
  • Face swapping and voice conversion
  • Generating images, video, and audio at scale
  • ML engineers training or fine-tuning models on rented GPUs
  • Startups running inference at scale without owning hardware
  • Developers needing quick, low-cost access to specific GPU types
  • Teams building AI agents that autonomously provision compute
  • Developers embedding image/video/speech generation into an app via API
  • Teams deploying and scaling their own fine-tuned models
  • Builders comparing outputs from multiple AI models in one playground
  • Companies avoiding GPU infrastructure management for ML inference
  • Integrating many AI models via one API
  • Prototyping and scaling AI apps
  • Cost-controlled multi-model access
  • 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
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