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Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
GPU rental marketplace with per-second billing across thousands of GPUs, aimed at AI training, inference, and fine-tuning workloads.
Unified API to 500+ AI models (OpenAI, Anthropic, Google, etc.) with OpenAI-compatible calls priced ~20% below official rates.
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.
No public pricing
No public pricing
Free trial available
Free trial available
- ✦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
- ✦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 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.)
- ✦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
- ✦Serverless per-token inference with OpenAI/Anthropic-compatible APIs
- ✦On-demand dedicated and reserved GPU deployments
- ✦Fine-tuning and reinforcement-learning training pipelines
- ✦Large library of open LLM, vision, image and audio models
- ✦Optimized inference engine for throughput and latency
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively
- →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
- →Consolidating multi-provider AI billing
- →Switching models without re-integration
- →Powering apps and automation pipelines
- →Benchmarking models in one playground
- →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
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale