Lightning AI
AIAll-in-one platform to prototype, train, scale, and serve ML models from the browser with zero setup, from the creators of PyTorch Lightning.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
Lightning AI
AI
All-in-one platform to prototype, train, scale, and serve ML models from the browser with zero setup, from the creators of PyTorch Lightning.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About Lightning AI
Lightning AI is a unified platform for AI development that enables teams to prototype, train, deploy, and serve models with minimal setup. It integrates the PyTorch Lightning ecosystem (Fabric, TorchMetrics, LitServe, LitModels, Lightning Thunder) to provide browser-based collaboration, autoscaling cloud deployment, and production-grade serving for models, agents, and retrieval-augmented pipelines. The platform emphasizes developer productivity (zero local setup, reusable templates), scalable inference (multi-GPU batching, streaming, multi-model support), and flexible hosting (self-host or deploy to Lightning AI cloud with built-in autoscaling and access controls).
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Key Features
Browser-based Development: Zero-setup web studio for coding, prototyping, and collaborative experiments directly from the browser, reducing onboarding friction for teams.
Integrated Training Stack: First-class integration with PyTorch Lightning and Lightning Fabric to run experiments, leverage built-in training features, and accelerate model development workflows.
LitServe Inference Engine: Deploy any model type (vision, audio, text) or full AI systems (agents, RAG, pipelines) with batching, multi-GPU support, streaming outputs, and custom logic without YAML or heavy MLOps.
Model Hosting and Checkpoints: LitModels capability to save, load, host, and share model checkpoints with enterprise-grade access controls and options to host on Lightning or self-managed cloud.
Autoscaling Cloud Deployment: One-command deployments to Lightning AI cloud with autoscaling, security controls, and high-availability SLAs (99.995% uptime when deployed via platform).
LLM Router & Agent Framework: Tools and libraries to route calls to LLM APIs, unified billing, retries/fallbacks, logging, and a minimal agent framework for building LLM-based applications.
Dataset & Optimization Tools: Utilities such as litData for transforming and optimizing datasets at scale and Lightning Thunder compiler for performance/memory optimizations during training and inference.
Self-Host Flexibility: Option to self-host all components for full control or use Lightning's managed cloud for faster time-to-production with built-in monitoring and security.
Browser-based collaborative development with zero setup
End-to-end tooling: prototype, train, optimize, host, and serve models
LitServe: flexible inference engine for agents, RAG, pipelines, multi-model serving, streaming and batching
LitModels: save, load, host, and share model checkpoints with enterprise-grade access controls
LitData: dataset transformation and optimization at scale
PyTorch Lightning / Lightning Fabric integration for structured training and low-level control
Lightning-thunder: PyTorch compiler optimizations for performance, memory, and parallelism
One-click cloud deployment and CLI (e.g., lightning deploy server.py --cloud) with autoscaling and managed uptime
Support for self-hosting or managed hosting, multi-GPU, custom logic, and advanced routing (LLM router, retries, fallback, logging)
Open-source components under Apache-2.0 and active GitHub ecosystem
Use Cases
Collaborative Prototyping: Rapidly prototype model ideas and iterate with teammates in a browser workspace without local environment setup.
Training Large Models: Run scalable training experiments using PyTorch Lightning/Fabric with built-in optimizations and support for multi-GPU or distributed setups.
Production Inference for Agents and RAG: Deploy multi-model agents, chatbots, or retrieval-augmented generation pipelines with LitServe’s batching, streaming, and custom logic features.
Model Hosting and Sharing: Save, host, and share model checkpoints with access controls for team collaboration or enterprise governance using LitModels.
Cloud Deployment with Autoscaling: Deploy model servers to Lightning AI cloud with autoscaling and high uptime guarantees for production traffic.
Self-Hosted Enterprise Deployments: Run the full stack on private infrastructure for customers needing full control over data, security, and compliance.
Rapid prototyping and collaborative model development in the browser without environment setup
Training and fine-tuning models using structured PyTorch Lightning workflows
Deploying inference services, agents, chatbots, and RAG pipelines with multi-model and streaming support
Hosting and sharing model checkpoints with access controls and integration into training workflows
Transforming and optimizing datasets for faster training at scale
Applying compiler-level optimizations for faster training and inference on multi-GPU setups
Self-hosting ML systems on customer infrastructure or using Lightning AI managed cloud for autoscaling production
