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AI app builder that turns chat prompts into working web apps and sites, with credit-based build and deploy.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.
No public pricing
No public pricing
No public pricing
Free trial available
Free trial available
No public pricing
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Chat-to-app and website generation
- ✦Real-time prototype building
- ✦One-click deploy and hosting
- ✦Templates to start projects
- ✦Credit-based building with shared workspaces
- ✦You own your code and data
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦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
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Build web apps without coding
- →Prototype product ideas quickly
- →Create landing pages and sites
- →Ship internal tools
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →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
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts