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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
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.
AI app builder that turns chat prompts into working web apps and sites, with credit-based build and deploy.
Low-code integration platform for connecting thousands of APIs into workflows and AI agents, including an MCP tool server.
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
- ✦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
- ✦Visual and code-based workflow builder
- ✦Prebuilt AI agent builder and deployment
- ✦Managed authentication across thousands of apps
- ✦MCP server exposing integrations as agent tools
- ✦Scheduled and event-triggered workflows
- ✦Connect SDK for embedding integrations into other products
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →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
- →Build web apps without coding
- →Prototype product ideas quickly
- →Create landing pages and sites
- →Ship internal tools
- →Building AI agents that call external APIs and tools
- →Automating cross-app workflows such as Slack, Gmail, or Sheets notifications
- →Embedding third-party integrations into a SaaS product
- →Prototyping event-driven automations without heavy infrastructure