Azimutt
Seamless exploration for large & complex databases
Founded
2021
Starting Price
Free
Category
Developer Tools
Last updated May 21, 2026
About Azimutt
Azimutt is a next-generation ERD (Entity Relationship Diagram) tool built to explore, document, and analyze large and messy databases. It combines incremental schema visualization, an AML design language, a database linter, and path-finding to help developers, analysts, and DBAs make sense of complex relational and document databases.
Pros & Cons
Pros
- Handles very large and messy schemas where traditional ERD tools become unusable
- Built-in linter catches missing keys, type mismatches, and other schema smells automatically
- AML DSL lets you design databases as code with minimal syntax
- Native support for both relational and document databases in one tool
- Generous free tier with no signup required for basic exploration
Key Features
Scalable ERD
Incrementally visualize huge schemas by choosing exactly which tables, columns, and relations are displayed.
AML Design Language
Minimal DSL for rapidly modeling databases as code without leaving the editor.
Database Linter
Automatic analysis surfaces missing primary keys, missing foreign keys, type inconsistencies, and other schema issues.
Multi-Database Support
Native support for PostgreSQL, MySQL, SQL Server, MongoDB, Couchbase, Snowflake, BigQuery, and more.
Path Finding
Automatically discovers join paths and relationship chains between any two tables.
Documentation Layer
Annotate tables and columns, save focused layouts, and embed visual indicators directly in the diagram.
Data Exploration
Browse rows, follow relationships, and inspect data alongside the schema.
Pricing
Free
Free
- Schema and data exploration
- Preview database analysis (top 3 suggestions)
- 10 tables in AML design
- 3 databases per project
- No saveable projects
Best For
Onboarding to a large legacy database
New engineers can incrementally explore a huge schema, follow relationships, and build focused layouts that document the parts of the system relevant to their work.
Database documentation for teams
Annotate tables and columns, save layouts per domain, and share or embed diagrams so the whole team has a single source of truth for the data model.
Schema auditing and tech debt cleanup
Run Azimutt's linter against a production schema to surface missing primary keys, missing foreign keys, and inconsistent column types as a prioritized backlog.
Cross-database investigation
Connect relational and document databases in one project to trace how data flows across services during bug investigations or integration work.
