Atlan Lakehouse vs MongoDB Atlas

Atlan Lakehouse is a knowledge architecture for storing, managing, and serving context to enterprise AI agents. Its design combines a knowledge graph, Iceberg-native file storage, vector-native search, and time travel. Context is exposed through MCP, A2A, SQL, and APIs. Atlan says its agents can use the Enterprise Data Graph to create asset descriptions, connect business terms, and surface business questions. Native connectors cover data platforms, databases, BI tools, ETL pipelines, orchestrators, and data quality tools, with Snowflake, BigQuery, Tableau, dbt, and Airflow listed as examples. Connectors read metadata such as schemas, structures, lineage, ownership, and timestamps while source data remains in source systems. The App Framework lets developers, partners, and customers create custom connectors and apps with APIs, SDKs, CLI tools, and UI extension points. Atlan recommends single-tenant SaaS and lists AWS, Microsoft Azure, and Google Cloud Platform. A self-deployed runtime runs in the customer environment and requires additional enablement and licensing. Pricing is on request, and no free trial is offered.

MongoDB Atlas has no write-up on Specifiction yet; its figures are above.