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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.

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

5.2K
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✓ verifiedFreemium

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

👁 775K/mo
Lovable
✓ verifiedFreemium

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

👁 35M/mo69K
Coder
✓ verifiedFreemium

Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.

👁 208K/mo41
OLMo 2
✓ verifiedFree

Ai2's family of fully open language models with weights, code, and training data released, built for transparent LLM research and building.

Pricing

No public pricing

No public pricing

No public pricing

Community: $0 (open-source, self-hosted, unlimited workspaces)

Free trial available

No public pricing

Core features
  • 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)
  • 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
  • Self-hosted workspaces with desktop and web IDEs
  • Coder Agents run coding agents on isolated infrastructure
  • AI Governance gateway for LLM usage control
  • SSO (OpenID Connect) and role/group sync
  • Audit logging and resource quotas
  • Multi-organization access controls
  • High availability and workspace proxies
  • Fully open weights, code, and training data
  • Base, Think (reasoning), and Instruct variants
  • 7B and 32B model sizes
  • Open model flow across all training stages
  • Open-source training/eval tools (OlmoCore, OLMES)
  • OlmoTrace to trace outputs to training data
Use cases
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • 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
  • Standardize developer environments
  • Run AI coding agents securely on-prem
  • Enforce governance and compliance
  • Cut VDI costs
  • Speed up developer onboarding
  • Researching language-model training and behavior
  • Building and fine-tuning open models
  • Machine-unlearning and clinical-NLP research
  • Deploying transparent open LLMs
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