Predictable Analytics for the Agentic Workforce

EDB Postgres AI + RAPIDS Accelerator for Apache Spark: High-performance, GPU-accelerated analytics with Apache Iceberg integration.

The challenge: The agentic scaling wall

AI agents generate volatile, non-linear queries that bypass traditional database optimizations.

  • Failed queries: 15% of standard Postgres queries timed out at 3TB.
  • Manual limits: Index tuning only provided 10% speedup while slowing other operations by 29x.
  • ETL latency: Data shuffling stalls real-time decision-making.

The solution: 100% completion at scale

99x

Faster analytics

Spark-RAPIDS vs. tuned Postgres at 1TB scale.

14.8x

Hardware ROI

Speedup on NVIDIA RTX PRO™ 6000 Blackwell Server Edition vs. CPU-only Spark at 3TB.

100%

Query completion

Successful execution rate where standard Postgres timed out.

100x

Data scalability

Capacity increase for analytics without sacrificing performance.

Validated performance (Jan 2026)

Scale Factorvs. Tuned Postgresvs. Spark CPUMedian Speedup
1TB99x faster3.9x14.2x
3TB>53x faster*7.8x33.2x
10TBPostgres timed out7.1x fasterN/A
*Note: 53x is an understatement; 15% of standard Postgres queries failed to complete within the 2-hour test limit.

On PostgreSQL, several queries do not complete within the allotted two-2 hour timeout, and that number grows with the scale factor.