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Cloud platform to run open-source AI apps like ComfyUI and Stable Diffusion and train LoRAs on rented GPUs, billed hourly.
Single API and playground for 1000+ AI models (chat, image, video, audio) with pay-as-you-go billing.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
Enterprise unified API gateway giving one integration point to 100+ LLMs like Claude, GPT, and Gemini with reliability guarantees.
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
Free trial available
Free trial available
No public pricing
No public pricing
Free trial available
- ✦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
- ✦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
- ✦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
- ✦Unified API for 100+ AI models
- ✦Intelligent request routing across models
- ✦AI Model Insurance for quality/reliability guarantees
- ✦Enterprise-focused LLM access layer
- ✦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
- →Running ComfyUI/Stable Diffusion without a local GPU
- →Training custom LoRA models
- →Face swapping and voice conversion
- →Generating images, video, and audio at scale
- →Integrating many AI models via one API
- →Prototyping and scaling AI apps
- →Cost-controlled multi-model access
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively
- →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
- →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