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✓ verifiedFreemium
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
👁 775K/mo
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Cody
✓ verifiedPaid
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
👁 245K/mo
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Nscale
✓ verifiedPaid
Full-stack AI cloud offering GPU compute, inference, fine-tuning and sovereign data centers for large-scale AI and HPC workloads.
Pricing
No public pricing
No public pricing
Enterprise: starting at $16K (includes AI feature credits, scales with team size)
No public pricing
Core features
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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)
- ✦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
- ✦On-demand GPU and CPU compute
- ✦Autoscaling inference endpoints
- ✦Serverless fine-tuning pipelines
- ✦Managed Kubernetes and Slurm
- ✦AI-optimized storage and RDMA networking
- ✦Sovereign, sustainable data centers
- ✦Fleet operations and observability
Use cases
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →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
- →Large-scale model training and fine-tuning
- →Deploying inference at scale
- →Running HPC and GPU workloads
- →Sovereign or compliant AI infrastructure
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