Datastrato Iceberg Lakehouse Platform vs Oracle Autonomous AI Lakehouse
Datastrato Iceberg Lakehouse Platform
datastrato.aiOracle Autonomous AI Lakehouse
oracle.comSide by side
| Figure | Datastrato Iceberg Lakehouse Platform | Oracle Autonomous AI Lakehouse |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | Yes |
| Open source | Yes | — |
| Web | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 5 |
| Spec score · Data Lakehouse Platforms | 8.4 (#4) | 7.8 (#9) |
| Founded | LinkedIn lists Datastrato's founding year as 2023. | 1977 |
| Purpose | Datastrato is a federated metadata catalog built on Apache Gravitino and Iceberg REST Catalog for connecting data sources and governing access across clients. | Oracle describes Autonomous AI Lakehouse as a pay-per-use platform for running AI on data with open-source lake technologies and enterprise data warehouse capabilities. |
Plans
Datastrato Iceberg Lakehouse Platform
- Datastrato EnterprisePriced on request
Oracle Autonomous AI Lakehouse
- Always Free Autonomous AI LakehouseFree
- Bring Your Own LicensePriced on request
- Oracle Autonomous AI Lakehouse on Dedicated InfrastructurePriced on request
- Oracle Autonomous AI Lakehouse on Exadata Cloud@CustomerPriced on request
- Oracle Autonomous AI Lakehouse ServerlessPriced on request
In their own words
Datastrato Iceberg Lakehouse Platform
Datastrato Iceberg Lakehouse Platform is a federated metadata catalog for connecting data sources and governing access across clients. Built on Apache Gravitino and Iceberg REST Catalog, it provides a standards-compliant REST catalog with metadata caching and table maintenance for production lakehouses. It can register Iceberg, Hive, Glue, relational databases, Kafka, filesets, and models in place without copying metadata. Spark, Flink, Trino, and Dremio are named Iceberg catalog clients, and the same namespace resolves across BigQuery. Governance includes catalog-level role-based access, row filtering, column masking, tag-based policies, and auditing across engines and agents. S3, GCS, and ADLS are supported with short-lived scoped credentials per principal. Agents can use an MCP metadata interface and GVFS under the same access controls. Per-table compaction, snapshot expiry, and orphan cleanup jobs can be sent to Airflow, Kubernetes, EMR, or Databricks. Enterprise is installed with a Helm chart in Kubernetes and activated by license key. Pricing is on request; the commercial distribution includes support with a defined response commitment and maintained release line.
Oracle Autonomous AI Lakehouse
Oracle Autonomous AI Lakehouse is a pay-per-use platform for working with data through open lake technologies and enterprise data warehouse capabilities. It can query Apache Iceberg tables across clouds without moving the data, and is available on OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer. It supports structured, semi-structured, and unstructured data, with catalog integrations including OCI Data Catalog, AWS Glue, and Apache Iceberg catalogs in Databricks and Snowflake. Data Studio provides drag-and-drop workflows for integrating data from more than 100 application, cloud service, and database sources, and enables bidirectional sharing with Power BI and Tableau through Delta Sharing. Its AI and analytics tools include machine learning, graph and spatial analytics, and AI Vector Search for semantic search and retrieval-augmented generation. Select AI can use models from providers including Cohere, OpenAI, Google, Anthropic, and AWS. Oracle says automated management covers provisioning, security, tuning, and scaling. The Always Free offer includes two database instances, subject to capacity limits; a separate trial provides US$300 in cloud credits for up to 30 days.