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Online database-design tool with sample schemas and an AI generator to explore, modify or build database structures visually.
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
Continuously analyzes MySQL, MariaDB, and PostgreSQL workloads to recommend and safely apply configuration and query fixes.
Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
No public pricing
No public pricing
No public pricing
Free trial available
Free trial available
Free trial available
- ✦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)
- ✦Library of sample database designs
- ✦Visual database designer / diagram tool
- ✦AI database generator
- ✦Modify and optimize existing schemas
- ✦SQL script export
- ✦Dialect converters (MySQL/PostgreSQL/MSSQL)
- ✦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
- ✦Workload-based configuration tuning
- ✦SQL query analytics and optimization suggestions
- ✦Schema optimization (duplicate/unused index detection)
- ✦24/7 automated health and security monitoring
- ✦One-command agent installation
- ✦Human approval required before applying changes
- ✦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
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Finding a starting schema for a project
- →Designing a database visually
- →Generating a schema with AI
- →Converting between SQL dialects
- →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
- →Database teams reducing manual tuning workload
- →Hosting providers optimizing customer databases at scale
- →Engineering teams without a dedicated DBA fixing performance issues
- →AWS RDS users tuning managed database instances
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms