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Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
Cloud platform to run open-source AI apps like ComfyUI and Stable Diffusion and train LoRAs on rented GPUs, billed hourly.
Free trial available
No public pricing
No public pricing
Free trial available
- ✦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
- ✦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
- ✦Single API for 500+ image, video and audio models
- ✦Pay-per-generation billing with no subscription
- ✦No charge on failed tasks
- ✦Workflows, agents and studio tools
- ✦MCP and CLI integrations, white-label option
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- ✦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
- →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
- →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
- →Building apps on top of many generative models via one API
- →Generating images, video and audio at scale
- →Cutting model API costs versus direct providers
- →Deploying white-label AI generation studios
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively
- →Running ComfyUI/Stable Diffusion without a local GPU
- →Training custom LoRA models
- →Face swapping and voice conversion
- →Generating images, video, and audio at scale