Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Project IDX by Google
✓ verifiedFree
Google's cloud-based, AI-assisted development environment, now rebranded and merged into Firebase Studio.
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Aide Dev
✓ verifiedPaid
Aide helps developers code faster with parallel agents and automated workflows.
7.6K visits/mo
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Apify
✓ verifiedFreemium
Full-stack platform for web scraping, data extraction, and automation; category leader.
4.4M visits/mo2.0K saves
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Jules by Google
✓ verifiedFreemium
Google's asynchronous AI coding agent that autonomously fixes bugs and builds features in GitHub repos, powered by Gemini.
Pricing
No public pricing
No public pricing
Standard: $49 per month
Free: $0/mo ($5 included usage)
Starter: $29/mo ($26/mo billed annually)
Scale: $199/mo ($179/mo billed annually)
Business: $999/mo ($899/mo billed annually)
Free trial available
No public pricing
Core features
- ✦Cloud-based IDE accessible from the browser
- ✦AI-assisted coding
- ✦Cross-platform app development
- ✦Preconfigured workspaces and templates
- ✦Now part of Firebase Studio
- ✦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)
- ✦Parallel Agents for faster coding
- ✦GitHub native integration
- ✦Automated PR workflow
- ✦Smart PR suggestions
- ✦Automatic code reviews
- ✦Real-time progress tracking
- ✦Web scraping
- ✦Data extraction
- ✦Browser automation
- ✦AI agents
- ✦Anti-blocking
- ✦Proxy rotation
- ✦Open-source tools (Crawlee)
- ✦Ready-made tools and code templates
- ✦Autonomous coding agent
- ✦GitHub repository integration
- ✦Runs in a cloud VM
- ✦Multi-step task planning
- ✦Opens pull requests with changes
- ✦Powered by Gemini
Use cases
- →Building apps from anywhere in the browser
- →Prototyping with AI assistance
- →Developing cross-platform applications
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Automating code reviews
- →Generating PRs automatically
- →Improving code quality through continuous improvements
- →Data for generative AI
- →Lead generation
- →Market research
- →Sentiment analysis
- →Fixing bugs asynchronously
- →Adding features to a codebase
- →Writing and updating tests
- →Automating routine development tasks
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