Date -2026-03-31 Location - WILMINGTON, Del.

— EnterpriseDB (EDB), the leading sovereign AI and data company today announced the results of an independent benchmark study by McKnight Consulting Group demonstrating that EDB Postgres AI (EDB PG AI) for WarehousePG delivers up to 58% total cost of ownership (TCO) savings compared to leading cloud data warehouses. Alongside these benchmark results, EDB released its Q1 platform updates, delivering a suite of new features engineered to support the rigorous demands of the agentic AI era.

As enterprises move toward AI-driven automation, the rise of agentic AI is forcing analytics and operations to converge. Traditional enterprise data stacks—built by attaching specialized platforms for transactions, analytics, and AI—introduce fragmented governance, unpredictable latency, and runaway compute costs. 

Because AI agents retrieve, analyze, decide, and act against live enterprise data in continuous, high-volume workflows, they dramatically amplify these inefficiencies. 

To successfully transition from AI experimentation to scale, organizations require a unified, sovereign foundation where analytics, operations, and AI are governed together by design.

McKnight Benchmark: Consistent Performance at Lower Cost 

The McKnight Consulting Group evaluated EDB PG AI for WarehousePG against Snowflake, Databricks, Amazon Redshift, and Hive on Apache Iceberg using a 10TB extended TPC-DS dataset. The rigorous testing focused on high-concurrency mixed workloads that simulate the reality of modern enterprise business intelligence (BI) and agentic workflows.

Key findings from the benchmark report include:

• Unmatched Cost Efficiency: EDB PG AI delivered up to 58% annual cost savings over scaled cloud data warehouse deployments—in one instance costing $222,886 annually compared to Snowflake’s multi-cluster cost of $351,953.
• Superior Concurrency Handling: EDB PG AI demonstrated the lowest performance slowdown (2.7x) when scaling from one to five concurrent users, significantly outperforming Snowflake (3.9x), Redshift (4.0x), and Databricks (4.1x).
• Elimination of Unpredictable Pricing: By utilizing a core-based, capacity-pricing model, EDB PG AI insulates enterprises from the consumption-based pricing spikes that plague high-frequency dashboarding and agentic querying with cloud data warehouses.

"Currently, many organizations are trapped in a cycle of operational friction, facing system instability during peak reporting periods or scaling back their data science ambitions to stay within budget," said William McKnight, President of McKnight Consulting Group. "The results demonstrate that while cloud warehouses suit high-performance analytics for the most demanding queries, EDB PG AI for WarehousePG works efficiently for the high-concurrency analytics that power daily operations, providing consistent performance with better cost efficiency. This reveals the merits of a hybrid approach." 

"As agentic AI collapses the traditional boundaries between transactional, analytical, and AI workloads, enterprises can no longer afford the latency and unpredictable costs of fragmented cloud data warehouses," said Nancy Hensley, Chief Product Officer at EDB. "This benchmark proves that you don't have to trade cost for scale or sovereignty. With our Q1 platform updates, we are providing the unified, predictable, and governed foundation required for the next generation of autonomous agentic workflows."

Q1 2026 Platform Updates: The Sovereign Safe Harbor for the Agentic Era 

To further enable organizations to build and deploy autonomous agents at scale, EDB’s Q1 release introduces major enhancements across the EDB Postgres AI platform:

• GPU-Accelerated Analytics: Through integration with Apache Spark accelerated by NVIDIA cuDF, the EDB PG AI Analytics Engine offloads analytical workloads to GPUs, enabling 50–100x faster, predictable analytics on large datasets (3TB+).

• Enhanced Agent Studio: A visual drag-and-drop canvas powered by Langflow lets users build, test, and deploy AI agents faster with native MCP support, enabling agents to interact directly with Postgres databases as tools.
• Upgraded Vector Engine: New VectorChord support delivers 100x faster, more cost-efficient indexing to power production-scale agentic workloads, while remote model connectivity from providers like Hugging Face eliminates risky data movement.
• WarehousePG Enterprise Manager (WEM): A new unified visual interface simplifies the management of Massively Parallel Processing (MPP) workloads, integrating real-time telemetry, SQL tuning, and security management into a single pane of glass.
• Agentic Database Management: A native chatbot now enables administrators to manage their database estate, adjust user roles, and receive health recommendations using natural language—moving manual operations toward interactive dialogue.
• Red Hat Ansible Automation Platform Certification: EDB PG AI is now tested and certified as the mission-critical data layer for the Red Hat Ansible Automation Platform, delivering multi-AZ high availability and sub-30-second failover.

For more information and to download the full McKnight Consulting Group benchmark report, A Comparative Performance and Cost Analysis of Modern Analytical Data Platforms, visit www.enterprisedb.com/resources/mcknight-predictable-analytics-at-scale. 

About EDB

EDB Postgres® AI (EDB PG AI) is the first open, enterprise-grade sovereign data and AI platform—secure, compliant, and scalable, on-premises and across clouds. Built on Postgres, the world’s leading database, EDB PG AI unifies transactional, analytical, and AI workloads, enabling organizations to operationalize their data and LLMs while maintaining control over sovereign environments. EDB PG AI is supported by a global partner network and delivers up to 99.999% availability as well as hybrid management and a built-in AI factory. As one of the most active contributors to the PostgreSQL project, EDB is deeply invested in the vitality of the global community. To learn more, visit www.enterprisedb.com. 

Media contact:
Steph McGuirk
Interdependence 
stephanie@interdependence.com