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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.

Qoder logo
Qoder
✓ verifiedFreemium

Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.

2.7M visits/mo32K saves
Supernova.io logo
Supernova.io
✓ verifiedFreemium

Design-system platform that packages tokens, code components, and rules into scoped context for AI coding agents.

93K visits/mo
Cody logo
Cody
✓ verifiedPaid

Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.

245K visits/mo
Pricing

No public pricing

Free trial available

Pro: $35/mo per full seat (up to 15 seats, billed monthly)
Enterprise: starting at $16K (includes AI feature credits, scales with team size)
Core features
  • 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
  • Scoped MCP context distribution to multiple AI coding tools
  • Design token and component API management
  • Collaborative documentation with analytics
  • Figma and Storybook data source integration
  • Feedback loop for improving AI context quality
  • Skill and exporter management for agent capabilities
  • Codebase-aware developer chat
  • AI code completions and inline edits
  • Customizable and shareable prompts
  • Automatic bug identification and debugging help
  • Context filters to exclude sensitive repos
  • Integrates with major code hosts and IDEs
Use cases
  • Autonomous feature development in large codebases
  • Terminal-based AI pair programming
  • Cross-department task automation for legal, finance, HR
  • Onboarding developers to unfamiliar codebases
  • Product teams giving AI coding agents accurate design-system context
  • Design system managers publishing a single source of truth
  • Engineering teams reducing token usage by scoping agent context per team
  • Engineers asking questions about an unfamiliar large codebase
  • Teams standardizing common coding tasks with shared prompts
  • Developers debugging errors faster with AI-assisted context
  • Enterprises running large-scale code migrations
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