Atlan Lakehouse vs Firebolt

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.

Firebolt is an open-source analytical database for engineers, with Postgres-compatible SQL and options for self-managed or managed deployment. It can run as a single binary on a laptop or scale to a cluster of hundreds of nodes in a customer’s cloud. Firebolt is designed for sub-second query latency and high-concurrency analytics, and supports Apache Iceberg and DuckLake using open storage standards. Deployment choices include a single binary, Helm chart, Kubernetes Operator, and managed service. Firebolt Core is free forever for self-hosted use, with no usage limits; it cannot be used to build a hosted SaaS that competes with Firebolt’s managed service. Managed compute is billed per second, with scale-to-zero, automatic start and stop, and autoscaling. Object storage is pass-through at $0.0264 / GB·mo, and the managed service includes $200 free credits. AI capabilities include query and embedding functions, vector search, an MCP Server, and a preview agent. Self-managed availability includes AWS and GCP, with Azure in preview; the managed service is currently AWS-only.