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Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
AI coding assistant for editors and IDEs that explains, refactors, documents, and generates code across 56 languages.
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
No public pricing
No public pricing
No public pricing
Free trial available
Free trial available
No public pricing
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Bug detection and fix suggestions
- ✦Code and CSS framework conversion
- ✦Unit test and documentation generation
- ✦Regex, SQL query, and CI/CD pipeline generation
- ✦Code explanation and style checking
- ✦Editor extensions for VS Code, Sublime, JetBrains, Visual Studio
- ✦Public dataset repository
- ✦Machine-learning competitions with prizes
- ✦Browser-based notebooks with free GPU/TPU
- ✦Micro-courses on data science topics
- ✦Community forums and shared code
- →Speeding up coding with AI completions
- →Generating code from plain-language prompts
- →Getting in-editor help and explanations
- →Reviewing pull requests with AI
- →Understanding unfamiliar codebases
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Generating unit tests for existing functions
- →Refactoring legacy code to modern practices
- →Producing inline documentation automatically
- →Learning new programming languages or concepts via AI explanations
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks