AI workloads Archives

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.

Tech Optimizer

July 22, 2026

Google boosts AlloyDB vector search with HNSW preview

Google has introduced a preview of its columnar engine-accelerated HNSW feature for AlloyDB, enhancing vector search throughput by up to four times for users of the pgvector extension in PostgreSQL. This improvement is aimed at approximate nearest neighbor searches using HNSW across large datasets. AlloyDB utilizes a columnar engine that allows HNSW indexes to reside in memory, improving throughput and recall metrics. In benchmark tests on the GloVe 100 Angular dataset, queries per second increased by approximately 4.2x to 4.9x, with recall improving from about 0.78 to over 0.94 at a throughput of around 350 queries per second. The columnar engine's design bypasses traditional PostgreSQL buffer management bottlenecks, enhancing efficiency during graph traversal. The memory architecture is distinct from basic caching techniques, and the feature requires no changes to application code. Users must enable the columnar engine and index caching flags to utilize this feature. Benchmarks were conducted on an AlloyDB C4A machine with 16 virtual CPUs.

Winsage

July 21, 2026

Samsung’s new GAIA PC chip challenges Microsoft’s Copilot+ program — and why I can only see that as a positive for the AI PC industry

NVIDIA announced the RTX Spark at Computex 2026, prompting competition among major silicon market players, including Intel, AMD, and Qualcomm. Samsung is entering the market with its custom silicon for AI-driven PCs, named GAIA, marking its first development of PC-centric silicon since 2012. GAIA will function as a companion Neural Processing Unit (NPU) rather than a traditional System-on-Chip (SoC). It is expected to use a 4nm process and may incorporate processing-in-memory (PIM) capabilities. Lenovo China and HP USA have received samples for testing. Competitors like Snapdragon X/X2, Intel Core Ultra, and AMD Ryzen AI are integrating NPUs with at least 40 TOPS. Samsung's GAIA has not been officially confirmed, and details regarding TOPS, power consumption, and pricing are unclear. The 40 TOPS benchmark by Microsoft for integrated NPUs raises questions about how GAIA would be evaluated alongside Intel or AMD CPUs.

Winsage

July 21, 2026

Samsung’s new GAIA PC chip challenges Microsoft’s Copilot+ program — and why I can only see that as a positive for the AI PC industry

NVIDIA announced the RTX Spark at Computex 2026, prompting Samsung to develop custom silicon for AI-driven PCs, marking its return to the PC silicon market since the Exynos 5 Dual in 2012. The new silicon, named GAIA, is being created by Samsung's System LSI unit as a companion Neural Processing Unit (NPU) to enhance performance by offloading AI tasks. GAIA is expected to utilize a 4nm process and may explore processing-in-memory (PIM) capabilities with specialized DRAM. Samples of GAIA have been sent to Lenovo China and HP USA for evaluation. Many details about GAIA, including performance metrics and pricing, remain undisclosed. Microsoft has not commented on GAIA's implications for its Copilot+ program, which has a minimum threshold of 40 TOPS for integrated NPUs.

Tech Optimizer

July 10, 2026

Google Cloud: PostgreSQL community contribution updates

Google Cloud actively participates in the PostgreSQL ecosystem by supporting community-driven events and contributing to open-source initiatives. Recent key events include: - **PGConf.dev 2026**: Featured strategic discussions on logical replication and global index architecture, with a consensus to adopt a deparsing-based approach for DDL replication. Dilip Kumar presented on global indexes. - **PGConf India 2026**: Attracted over 580 participants, featuring various sessions including keynotes and technical talks by Google Cloud contributors. - **PGDay Paris & PGDay France 2026**: Matt Cornillon was involved in organizing PGDay France, with sessions led by him and Yves Colin. - **PGDay FOSDEM 2026**: Focused on AI-assisted workflows in PostgreSQL development, with a technical talk by Matt Cornillon. - **PGConf Belgium 2026**: The session was selected as supplementary material for a database exam, indicating student engagement. - **Nordic PG Day 2026**: Google participated as a Partner-level sponsor and hosted a dedicated booth. - **Swiss PGDay 2026**: Featured a demonstration on processing vectors in PostgreSQL. - **Postgres Conference 2026 San Jose**: Google sponsored the event, with Vikas Arora discussing PostgreSQL adaptations for AI workloads. Community leadership roles included: - Dilip Kumar on the Program Committee for PGConf.dev 2026 and the Paper Selection Committee for PGConf India 2026. - Matt Cornillon on the organization committee for PGDay France, and Yves Colin on the Program Committee. Acknowledgment was given to various contributors for their dedication to PostgreSQL conferences.

Tech Optimizer

July 2, 2026

EDB Named a Leader in Multimodel Data Platforms Evaluation

EDB has been recognized as a Leader in The Forrester Wave: Multimodel Data Platforms, Q2 2026, with EDB Postgres AI (EDB PG AI) achieving the highest scores in Vision, Innovation, Roadmap, and Partner Ecosystem criteria. EDB PG AI integrates transactional, analytical, and AI workloads into a unified platform, supporting open-source frameworks and enabling various deployment options. The platform features governance at the data layer and is designed for operational efficiency, allowing organizations to implement sovereign AI quickly. EDB PG AI can be deployed on-premises, in hybrid environments, or across cloud infrastructures, backed by partnerships with companies like Dell, IBM, and NVIDIA.

Tech Optimizer

July 2, 2026

EDB named Forrester Leader in multimodel data platforms

EDB has been recognized as a Leader in Forrester's Multimodel Data Platforms evaluation for Q2 2026 for its EDB Postgres AI platform, receiving the highest scores in Vision, Innovation, Roadmap, and Partner Ecosystem. The platform is designed to manage mixed translytical and AI workload demands, offering flexibility in deployment across on-premises, hybrid, and multi-cloud environments. EDB's recent product update introduced agentic database and converged analytics functionalities, reportedly accelerating database tuning by up to tenfold and reducing analytics ownership costs by as much as 58%. The platform is supported by a partner ecosystem that includes Dell, IBM, NVIDIA, Red Hat, and Supermicro, which plays a crucial role in influencing database purchasing decisions. EDB's roadmap focuses on advancements in GPU-accelerated workloads, semantic intelligence, governance, and knowledge graph functionalities. The emphasis on sovereign deployment aligns with organizations' needs for control over sensitive data amidst stricter regulations.

Tech Optimizer

June 26, 2026

EDB launches agentic Postgres AI with governance tools

EDB has introduced new features for its Postgres AI platform, including an agentic database and converged analytics capabilities, allowing enterprises to run AI agents alongside transactional workloads on a unified PostgreSQL foundation. The platform includes governance tools that position control mechanisms at the data layer and integrates AI processing with operational data, enabling businesses to connect live records with AI systems without transferring sensitive information. The agentic database can monitor over 200 metrics, identify issues, suggest changes, and apply fixes automatically based on user-defined policies. It consolidates various data types through a single SQL interface, significantly accelerating database tuning processes and enhancing application performance. EDB has also expanded its analytics capabilities with a zero-ETL architecture for real-time analysis and large-scale warehousing. EDB PG AI for ClickHouse targets real-time analysis, while EDB PG AI for WarehousePG focuses on historical analysis at petabyte scale. The platform claims up to 30 times faster query performance compared to legacy systems and improved scaling efficiency. EDB's platform integrates vector search and retrieval for AI agents, demonstrating lower query latency and higher retrieval accuracy than competitors. NTT East is using EDB PG AI for AI-driven network operations, while the governance feature manages agent access at the data querying point using native Postgres roles and row-level security. The platform can be deployed on-premises, in hybrid environments, or across cloud infrastructures, with partnerships including Dell, IBM, Nvidia, Red Hat, and Supermicro.

Tech Optimizer

June 24, 2026

EDB converges analytics on Postgres to support AI agents

EDB's per-core pricing model offers predictable costs compared to consumption-based cloud data platforms, aiding organizations in budgeting. However, predictable billing does not guarantee lower costs, as high-speed operational data processing requires more expensive hardware than lakehouse storage solutions. EDB's architecture, built on a unified Postgres-Iceberg foundation, streamlines data governance by reducing the need for multiple specialized data stores, leading to fewer platforms to manage and enhancing operational efficiency and data governance protocols.

Tech Optimizer

June 20, 2026

EDB Reports Growing Adoption of Postgres AI as Enterprises Build Sovereign AI Foundations

EnterpriseDB (EDB) reported increased global adoption of its EDB Postgres AI (EDB PG AI) platform for managing mission-critical workloads. Research by MIT Technology Review Insights found that organizations prioritizing AI and data sovereignty achieve five times the return on investment. The Industrial Bank of Korea (IBK) migrated 15 core systems to EDB PG AI, reducing licensing costs and enhancing operational flexibility. Shinhan EZ Insurance transitioned its core system to the public cloud using EDB PG AI, achieving 24/7 service and scalability for AI workloads. Other companies like MNTN, Euronext FX, and Kyobo Book Centre are also leveraging EDB PG AI for various applications. EDB has received industry recognition, including being named among the most innovative companies in data and awarded for its data management solutions. EDB PG AI integrates transactional, analytical, and AI workloads, providing a secure and scalable platform for enterprises.