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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

Gemini Code Assist logo
Gemini Code Assist
✓ verifiedFreemium

Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.

559K visits/mo

Thin 'Lingbot-map' agent listing on github.com with zero traffic; too thin to tell.

5.2K saves
Devv.AI logo
Devv.AI
✓ verifiedPaid

AI search engine for developers with code repo integration.

52K visits/mo4.2K saves
Gitmore logo
Gitmore
✓ verifiedFreemium

Turns Git commits and PRs into AI-summarized daily or weekly reports delivered to Slack or email, no source access.

7.6K visits/mo
4.7K visits/mo
Pricing

No public pricing

No public pricing

No public pricing

No public pricing

Free trial available

No public pricing

Core features
  • AI code completion and suggestions
  • Natural-language code generation
  • In-IDE chat assistance
  • AI code review
  • IDE integrations (VS Code, JetBrains, etc.)
  • GitHub integration
  • 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)
  • GitHub Mode for repository search
  • Web Mode for web-based information retrieval
  • Chat Mode for direct AI interaction
  • Model selection (GPT, Claude, Gemini)
  • Student discount program
  • AI-summarized commit and PR reports
  • Daily and weekly scheduled digests
  • Slack and email delivery
  • One-click OAuth or webhook setup
  • GitHub, GitLab and Bitbucket support
  • Templates for standups and reports
  • Zero-ETL data integration
  • Federated Query
  • Streaming Ingestion
  • Instant Replication with CDC
  • API to SQL conversion
  • NoSQL to SQL conversion
  • SQL to API conversion
  • Self-service Integration
  • Generate SQL with AI
Use cases
  • 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
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Writing API reference documentation
  • Brainstorming SEO strategies
  • Enhancing code functionality
  • Gaining insights into open-source projects
  • Resolving complex code issues
  • Keep stakeholders updated on what shipped
  • Replace manual status updates and standups
  • Give teams visibility into Git activity
  • Query data directly from its source in real-time.
  • Process data wherever it is, blending data from different sources.
  • Ingest streaming data from Kafka, Segment, etc., into Peaka BI Table.
  • Replace nightly batch ingestion with real-time data access.
  • Treat every data source like a relational database by converting APIs to tables.
  • Use SQL to query NoSQL databases.
  • Query consolidated data and expose it with APIs.
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