toolspool

Compare tools

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

⇄ Comparison dimension — pick the market you're actually shopping in

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
CometAPI logo
CometAPI
✓ verifiedPaid

Unified API to 500+ AI models (OpenAI, Anthropic, Google, etc.) with OpenAI-compatible calls priced ~20% below official rates.

363K visits/mo4.8K saves
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
Nebius logo
Nebius
✓ verifiedPaid

AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.

678K visits/mo133K saves
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, min top-up $10
GPT-5.6: $4 / 1M tokens
Claude Sonnet 5: $1.6 / 1M tokens

Free trial available

Essential: $13.95/mo (+$12 credit)
Advanced: $26.95/mo (+$25 credit)
GPU hardware: from $0.29/hr

Free trial available

No public pricing

NVIDIA H100: $3.85/GPU-hour on-demand ($2.15 preemptible)
NVIDIA H200: $4.50/GPU-hour on-demand
NVIDIA B200: $7.15/GPU-hour on-demand
Shared filesystem storage: $0.08/GiB per month
Core features
  • 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 key for 500+ models
  • OpenAI-compatible API
  • Pay-as-you-go credits (~20% below list)
  • Multimodal: text, image, video, audio
  • Usage analytics and budget alerts
  • Integrations (Claude Code, n8n, Zapier, etc.)
  • 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
  • NVIDIA GPU instances (H100, H200, B200, GB200)
  • On-demand and preemptible GPU pricing
  • High-performance and object storage
  • Managed Kubernetes and Slurm (Soperator)
  • Serverless and managed inference (Token Factory)
  • MLOps tooling and 24/7 expert support
  • Commitment discounts up to 35%
Use cases
  • 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
  • Consolidating multi-provider AI billing
  • Switching models without re-integration
  • Powering apps and automation pipelines
  • Benchmarking models in one playground
  • 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
  • Train large AI/ML models on GPU clusters
  • Run scalable inference workloads
  • Store and manage large training datasets
  • Run Slurm/Kubernetes AI pipelines
Visit
More in Model Hosting Inference