Most developers are familiar with the hidden costs of traditional databases that tightly couple compute and storage together. This architecture often pushes teams to create custom infrastructure for managing developer workflows, instead of focusing on building. More importantly, it creates a dangerous resource conflict. Because every query competes for the same fixed CPU and memory resources, a single query can affect all live operations.
These constraints slow teams down and make it risky to work against live data. As applications become more automated and systems act on data in real time, this kind of shared, fragile infrastructure becomes an even bigger liability.
To remove this architectural bottleneck, we created the lakebase category, a new architecture for operational databases that separates compute from storage. Today, we are pleased to announce that Databricks Lakebase, the first implementation of this category, is Generally Available on AWS.
For decades, the architectural ‘tax’ of keeping operational and analytical data separate has slowed enterprise innovation. By decoupling the storage layer and integrating directly with the data lake, Lakebase is positioned as a new class of operational database that treats infrastructure as a flexible, on-demand service. For companies building advanced AI capabilities, this means the database is less likely to be a manual bottleneck. It becomes a tool that agents can spin up and manage more independently to keep pace with the speed of AI development. This reflects a broader shift toward architectures that reduce data movement and duplication and bring operational, analytical, and AI workloads closer together.— Devin Pratt, Research Director at IDC
Ship faster with serverless managed Postgres
Lakebase’s general availability delivers a fully managed, serverless Postgres service with the uptime and predictable performance needed for production applications. By separating compute from storage, it automates configuration and resource management tasks that typically slow development. Its new architecture automatically scales to handle heavy queries, keeps apps responsive under load, and supports instant data branching so teams can safely test and develop without risking production. Since its launch in June 2025, adoption has grown at more than twice the rate of Databricks’ data warehousing product, with thousands of companies running production workloads directly on their operational data.
Key capabilities available today include:
- Serverless autoscaling and scale to zero: Compute resources dynamically adjust to match traffic spikes and shut off completely when idle to eliminate wasted costs.
- Instant database branching: Create isolated, zero-copy clones of production data in seconds for risk-free testing and development.
- Point-in-time recovery (PITR): Protect against accidental deletions or bugs with millisecond-level restoration.
- Unified governance: Manage access control and auditing through Unity Catalog for a single security model across your entire data estate.
- Sync tables: Keep your operational data and historical lakehouse context in sync without maintaining fragile pipelines.
Get started with Databricks Lakebase today
Building AI agents and apps with Lakebase
With Lakebase, operational workloads run directly on the Databricks Platform. Applications share the same governance, security, and data foundation already trusted for analytics and AI. There is no siloed database to manage, no separate access controls to maintain, and no data movement to keep in sync.
This shared foundation enables common application patterns such as:
- Real-time feature serving for machine learning with low-latency access to fresh data.
- Persistent memory for AI agents that remains consistent with the lakehouse.
- Embedded analytics that combine operational data with historical insights.
Hafnia used Lakebase to move beyond a classic BI stack and static reports toward real-time business applications for fleet, commercial, and finance workflows. By using Lakebase as the transactional engine for their internal operations portal, they reduced the time to deliver production-ready apps and dashboards from 2 months to just 5 days.
Other companies like Warner Music Group have enjoyed development efficiency thanks to Lakebase.
Lakebase gives us a unified foundation where analytics and operational workloads work together in real time. By moving insights directly into production systems, we can respond faster, innovate with confidence, and ship new capabilities without compromising reliability. That speed and integration are critical as we scale experiences for our customers. — Mike Jones, Director of Software Engineering, Warner Music Group
Enterprise governance and performance
Lakebase’s GA release adds production-grade features for reliability, performance, and governance.
- Unified governance with Unity Catalog: Applications inherit consistent access control, auditing, and compliance across the Databricks Platform.
- Trusted foundation for AI: Governed, auditable operational data ensures autonomous AI systems act on reliable, compliant information.
- Automated backups and point-in-time recovery enable teams to restore database state to a specific millisecond within a configurable retention window, protecting against application bugs or accidental deletions.
- Increased storage capacity supports up to 8TB per instance, enabling larger application workloads.
- Postgres 17 support brings the latest Postgres improvements and extensions, including pgvector for AI-driven search, while continuing to support Postgres 16.
Together, these capabilities make Lakebase suitable for mission-critical systems with demanding reliability and performance requirements.
Modernize legacy systems with Lakebase
Many organizations run critical applications on aging databases and desktop tools that are difficult to evolve. Replacing them often requires rebuilding entire stacks or running parallel systems for years.
Lakebase provides a simpler, modernization path by consolidating application logic, analytics, and governance on a single platform.
easyJet used Lakebase and Databricks Apps to replace a decade-old desktop application and one of Europe’s largest legacy SQL Server environments. They consolidated more than 100 Git repositories into 2 and reduced development cycles from 9 months to 4.
We had outgrown our legacy systems, but with the Databricks Data Intelligence Platform–especially Lakebase and Databricks Apps–we’ve built a revenue management app that’s faster, simpler, and far more reliable. What once took months of effort is now delivered in a fraction of the time, creating the foundation for commercial decision-making and empowering our teams to analyse, decide, and act like a data-driven modern airline retailer. — Dennis Michon, Head of Data Product, easyJet
Try it out today
With General Availability, Lakebase establishes a new foundation for operational systems in an AI-native world. Starting today, Lakebase is available for production workloads in select AWS regions and in beta on Azure in select regions. We are rapidly expanding GA to Azure in the next few months and Google Cloud later this year. Compliance certifications, including SOC2 and HIPAA, are also on the roadmap for early this year.
Get up and running with Lakebase in minutes. Create your first project, connect to your database, and explore key features. To get started, check out our technical documentation and our getting started guide.
Lakebase GA arrives with validated integrations from partners who have worked closely with Databricks during development and have validated Lakebase in real production environments. Read more about our GA Partner Ecosystem in our companion blog here.
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