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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.

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✓ verifiedFreemium

Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.

775K visits/mo
Lovable logo
Lovable
✓ verifiedFreemium

AI app builder that turns chat prompts into working web apps and sites, with credit-based build and deploy.

35M visits/mo69K saves
Databricks logo
Databricks
✓ verified

Mosaic AI on Databricks, a leading enterprise data-and-AI platform.

8.9K saves

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

5.2K saves
DDN logo
DDN
✓ verifiedPaid

Data-intelligence and storage platform powering large-scale AI and HPC, aimed at maximizing GPU utilization.

Pricing

No public pricing

No public pricing

No public pricing

No public pricing

No public pricing

Core features
  • Open-source AI code assistant
  • Customizable autocomplete
  • In-editor AI chat
  • Community-built coding agent
  • 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
  • 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)
  • AI-native data intelligence platform
  • High-performance storage appliances (AI400X series)
  • EXAScaler and Infinia software
  • Multi-tenant secure data isolation
  • Real-time encryption for data sovereignty
  • Integrations with NVIDIA AI infrastructure
Use cases
  • Get AI code completions while coding
  • Ask questions about code in the editor
  • Build on an open-source coding-agent foundation
  • Build web apps without coding
  • Prototype product ideas quickly
  • Create landing pages and sites
  • Ship internal tools
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Feeding data to large GPU training clusters
  • Building AI factories and sovereign-AI platforms
  • Powering HPC and supercomputing storage
  • Accelerating research in finance, life sciences and automotive
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