USE CASE: OMNI-DATA PLATFORM
Accelerate insights and launch on-demand workloads with the
EDB Postgres® AI multi-model data platform
EDB Postgres® AI multi-model data platform
Data silos limit innovation
Enterprises building next-gen applications must leverage various data models at once—including structured, semi-structured, and unstructured data. This often requires many different purpose-built databases, leading to infrastructure sprawl, high complexity, ballooning costs, and data silos that limit innovation.


Simplify with a single platform
EDB Postgres AI (EDB PG AI) provides an omni-data platform for on-demand business application development by combining a seamless integration with lakehouse tables in object storage and open source Postgres multi-model support—unifying structured, semi-structured, and unstructured data in a single location.
Enterprise, multi-model Postgres
Leverage open source Postgres extensions to enable all types of data models—SQL, vector, JSON, time-series, key-value, and more—with enterprise-grade security and observability.
External lakehouse ecosystem integration
Connect seamlessly with unstructured data in an object storage environment that’s up to 18x as cost-efficient as solid state drives, reducing ETL (extract, transform, and load) pipelines and storage costs.
Analytics engine in Postgres
A query engine that scales separately from compute and is optimized for columnar formats enables complex analytical queries directly on operational data in Postgres with no lag on existing workloads—30x faster than the standard Postgres engine.
Unify structured, semi-structured, and unstructured data for on-demand workloads — all while eliminating vendor lock-in and maintaining secure, observable, cost-efficient systems.
Empowered decision-making
Make faster, more informed decisions with all your organization’s data unified in Postgres.
Faster time to insights
Get comprehensive insights in real time with fewer ETL pipelines, reduced latency, and 30x faster analytical queries.
Simplified data management
Eliminate vendor lock-in, reduce infrastructure sprawl, and improve operational agility with seamless, secure data management and robust observability across environments.
EDB Postgres AI enables an omni-data platform
Consolidate structured, semi-structured, and unstructured data into a single data management system. EDB PG AI supports diverse data models including relational, time series, document, vector, and key value and enables 30x faster analytics (OLAP) queries, on average, across Postgres tables and object storage. Unify data silos, drive system consolidation, and unlock cost efficiency while accelerating innovation by your developers.
Demo Video
Demo Video
Improve Analytics Insight without Sacrificing Performance
Improve Analytics Insight without Sacrificing Performance
Learn how to add an analytics node, sync data, and integrate with Databricks.
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Modern IT Challenges Require a Multi-model Data Platform
Modern IT Challenges Require a Multi-model Data Platform
The EDB Postgres AI omni data platform is a multi-model data solution that seamlessly integrates structured, semi-structured, and unstructured data into a single Postgres environment. It enables high-performance queries for on-demand workloads, providing real-time insights while offering secure, observable, and cost-efficient systems.
No more endless searching for the right database to solve your current or emerging business problems. The EDB Postgres AI omni data platform eliminates the complexity of managing multiple purpose-built databases, which often results in infrastructure sprawl, ballooning costs, and data silos. By unifying various data models, the platform simplifies integration, reduces costs, and accelerates innovation.
The platform supports a wide variety of data models—SQL, vector, JSON, time-series, key-value, and more—by leveraging open source Postgres extensions. It even incorporates unstructured data via seamless integration with cost-efficient object storage. This allows enterprises to handle all data types within a single, secure environment, eliminating the need for multiple databases.
Key features include:
- Extensible open source Postgres with enterprise-grade security, observability, and scale.
- External lakehouse ecosystem integration for seamless incorporation of unstructured data.
- Analytics Engine for complex analytical queries across data in Postgres tables and object storage with no lag on existing workloads.
Key benefits include:
- Empowered decision-making with all your organization’s data unified in Postgres.
- Faster time to insight with fewer ETL pipelines, reduced latency, and 30x faster analytical queries.
- More control via zero vendor lock-in, tighter security, and reduced TCO with 18x cost-efficient object storage.
Integration with the lakehouse ecosystem seamlessly connects operational data in Postgres with open table formats like Iceberg and Delta Lake in a cost-efficient object storage environment that’s 18x more efficient than solid state drive storage—and it enables querying of data in object storage with familiar SQL. This reduces the need for complex ETL pipelines, lowering TCO and simplifying data management.
EDB Postgres AI Analytics Accelerator features an Analytics Engine that is optimized for columnar formats well-suited to complex analytical queries. It performs 30x faster, on average, than the standard Postgres engine, allowing real-time insights—and it separates compute from storage, so transactional workloads aren't impacted.
The platform helps businesses make faster data-driven decisions, reduces time to insights, and improves operational control by eliminating vendor lock-in. These capabilities drive higher customer satisfaction, sustainable growth, and enhanced business resilience.
By consolidating data into a single platform and utilizing cost-efficient object storage, the platform reduces infrastructure sprawl, lowers operational complexity, and eliminates the high costs associated with managing multiple purpose-built databases.
Any business can benefit from the omni-data platform as they work to meet the modern expectations of on-demand workloads and personalized experiences. However, the benefits are especially powerful for heavily regulated industries such as healthcare, financial services, and telecommunications, where structured, semi-structured, and unstructured data must be synthesized from varied — and often sensitive — customer sources.