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EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost
Tech Optimizer
July 29, 2026

EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost

EDB Postgres AI is a pioneering solution that integrates intelligence and data on a unified sovereign foundation, eliminating the need for ETL processes, data duplication, and separate vector stores. Independent benchmarks show that EDB Postgres AI outperforms competing platforms in speed, accuracy, and cost-effectiveness. A study by McKnight Consulting Group indicates that EDB Postgres AI excels in key performance metrics, including query latency, accuracy, cost, and data freshness, outperforming specialized vector databases and other managed Postgres platforms. EDB Postgres AI achieves median query latencies of 50 milliseconds at a scale of 50 million vectors, making it 80x faster than Databricks, 21x faster than MongoDB Atlas, and up to 2x faster than alternatives like Aurora and Crunchy Bridge. It also delivers the highest recall rates for core vector searches, surpassing competitors like MongoDB and Databricks. In tests involving concurrent retrievals, EDB Postgres AI completed a three-arm agent loop in 27 milliseconds, significantly faster than other platforms. In terms of cost efficiency, EDB Postgres AI offers 76x better price performance than Databricks, 34x better than MongoDB, and 23%–28% better than the nearest managed Postgres competitors when considering query speed. The architecture of EDB Postgres AI allows for the integration of vector, filtered, and full-text retrieval in a single query path, ensuring real-time data access and eliminating the need for separate systems.
What Is Serverless PostgreSQL?
Tech Optimizer
July 12, 2026

What Is Serverless PostgreSQL?

Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.
Azure Databricks Lakebase is Generally Available
Tech Optimizer
March 6, 2026

Azure Databricks Lakebase is Generally Available

Azure Databricks Lakebase is a managed, serverless PostgreSQL solution optimized for the Databricks Platform on Azure, announced by Microsoft as generally available. It separates compute from storage, allowing direct writing of operational data to lakehouse storage and bridging the gap between transactional systems and analytics. Lakebase features instant branching and zero-copy clones, enhancing developer productivity by enabling safe testing environments without infrastructure delays. It operates on a serverless model with autoscaling capabilities, ensuring cost efficiency by charging users only for the compute resources utilized. Lakebase is built on standard PostgreSQL, ensuring compatibility with existing tools and libraries, and supports various extensions. It provides unified governance through Unity Catalog, offering consistent access control and auditing across the Azure Databricks data estate. The platform facilitates AI development by enabling real-time operational context access and low-latency feature serving. Azure Databricks Lakebase integrates with Microsoft Entra ID for security and compliance, simplifying the DevOps burden for developers.
Snowflake unveils Postgres to make enterprise data AI-ready
Tech Optimizer
February 12, 2026

Snowflake unveils Postgres to make enterprise data AI-ready

Snowflake has introduced Snowflake Postgres, which will be generally available soon, designed to unify transactional workloads, analytics, and AI development within its AI Data Cloud. It is fully compatible with open-source Postgres, allowing for seamless migration of existing applications without code modifications. Snowflake Postgres integrates Apache Iceberg through pg_lake, enabling users to manage Iceberg tables using standard SQL, reducing data movement between systems. Companies like BlueCloud and Sigma Computing have adopted it for operational applications and real-time analytics. Alongside Snowflake Postgres, Snowflake has enhanced data governance and interoperability through the Snowflake Horizon Catalog, which allows for better access and governance across various systems. The Horizon Catalog supports querying Iceberg tables and managing data stored in them. Snowflake has also launched Open Format Data Sharing, extending its zero-ETL sharing model to open formats like Apache Iceberg and Delta Lake, and has integrated with Microsoft OneLake for secure data sharing. Additionally, Snowflake has made Snowflake Backups generally available to safeguard business-critical data and ensure compliance with regulatory requirements, allowing for quicker recovery from disruptions.
Snowflake plugs PostgreSQL into its AI Data Cloud
Tech Optimizer
February 12, 2026

Snowflake plugs PostgreSQL into its AI Data Cloud

Snowflake is launching a PostgreSQL database-as-a-service to enhance its AI Data Cloud, allowing organizations to integrate transactional workloads with analytics and AI under a unified governance framework. This service is fully compatible with open-source PostgreSQL, enabling easy migration of existing applications without code modifications. It utilizes pg_lake to read and write directly to Apache Iceberg tables, eliminating the need for data extraction and movement. Snowflake aims to reduce costly data movement between transactional and analytical systems, building on its previous transactional capability, Unistore. This strategic move positions Snowflake to offer a managed OLTP solution, facilitating the development of agentic AI and real-time streaming capabilities. The trend of combining operational databases with analytics is becoming common among vendors, with competitors like Databricks also launching similar services. By consolidating OLTP and OLAP capabilities, organizations can reduce ETL processes and data duplication while maintaining consistent governance across workloads.
Databricks launches PostgreSQL Lakebase to aid AI developers
Tech Optimizer
February 12, 2026

Databricks launches PostgreSQL Lakebase to aid AI developers

Databricks Lakebase has transitioned to general availability, launched on AWS on February 3, following the acquisition of Neon for billion in May 2025. Lakebase is a PostgreSQL database designed for AI development, integrating with Databricks' Data Intelligence Platform to provide an operational database alongside data lakehouse capabilities. It decouples compute from storage to improve resource management and includes autoscaling features to manage costs. Lakebase also offers unified governance through Databricks' Unity Catalog. Analysts highlight its ability to reduce friction between operational and analytical data, enabling real-time applications with up-to-date governed data and minimizing extensive ETL processes. Key features include serverless autoscaling and instant database branching for enhanced developer productivity. Databricks aims to simplify database management at scale and demonstrate a lower total cost of ownership to compete with Snowflake.
pg_lake comes to Snowflake Postgres: What it means for open standards
Tech Optimizer
February 12, 2026

pg_lake comes to Snowflake Postgres: What it means for open standards

The pg_lake extension has been integrated into Snowflake Postgres, enabling users to utilize PostgreSQL as a data lakehouse with support for Apache Iceberg tables. This integration allows for unified analytical and transactional workloads, facilitating direct access to PostgreSQL within the Snowflake environment. Snowflake has also expanded its Snowflake Horizon Catalog to enhance data discovery and governance, now managing data accessed through external query systems like Apache Spark. New features include integration with Microsoft One Lake for secure, bi-directional data access between Snowflake and Microsoft Fabric. pg_lake includes functionalities for version management, data type and format conversions, and caching to optimize performance. Snowflake's capabilities for managing Iceberg tables have been enhanced, allowing external query engines to read and write to these tables while extending access controls for data governance. Additionally, Snowflake's open data sharing features now include Delta Lake and Iceberg tables, enabling users to access data without duplication or movement across clouds. The expansion of open standards supports interoperability, simplifying developers' work with various tools and formats.
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