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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
Developer tool that deploys Docker Compose apps (with LLMs and databases) into your own AWS, GCP or Azure account via one command.
No public pricing
No public pricing
Free trial available
Free trial available
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦Asset-based pipeline orchestration
- ✦Built-in lineage and data-quality checks
- ✦Data catalog with asset metadata
- ✦Native dbt, Snowflake and Fivetran integrations
- ✦Branch deployments and hybrid deployment
- ✦Open-source core plus managed Dagster+ cloud
- ✦One-command deploy from Docker Compose
- ✦Deploys into your own or a customer's cloud account
- ✦Native managed LLM access (Bedrock/Vertex/Azure AI)
- ✦Managed Postgres, MongoDB and Redis
- ✦Auto-configured IAM, VPC, TLS and load balancing
- ✦Open-source CLI and cloud providers
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →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
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms
- →Shipping AI agents and web apps to production
- →Deploying the same app across many customer clouds
- →Agencies deploying into client cloud accounts
- →Avoiding hand-written Terraform or Kubernetes