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Native macOS app that unifies 300+ AI models in one private workspace with agents, MCP tools, and one-time licensing.
Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.
Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
Free trial available
No public pricing
No public pricing
- ✦Switch across 300+ hosted and local AI models
- ✦Native macOS app with global shortcut and screenshot-to-answer
- ✦Reusable agents, projects, and forked chats
- ✦Multimodal analysis of PDFs, images, and code
- ✦MCP tools and code execution
- ✦Local chat storage with encryptable API keys
- ✦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
- ✦Hosted inference for many open models
- ✦Simple REST/OpenAI-compatible API
- ✦Pay-per-token or per-time billing
- ✦On-demand GPU rental
- ✦Broad catalog (Llama, DeepSeek, Qwen, Flux, etc.)
- ✦DeepStart and DeepCluster tooling
- ✦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
- ✦Serverless GPU compute defined in Python
- ✦Sub-second container cold starts
- ✦Autoscale 0 to 1000+ GPUs
- ✦Inference, training and batch workloads
- ✦Secure sandboxes for untrusted code
- ✦Built-in logging and observability
- →Using multiple AI providers in one place
- →Explaining or fixing on-screen content instantly
- →Building reusable task-specific agents
- →Analyzing documents and screenshots privately
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale
- →Serving open-source models via API
- →Building AI apps cost-efficiently
- →Renting GPUs for inference or training
- →Scaling inference up and down on demand
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes