Midwest Tape (distributor for Hoopla) was hitting performance ceilings on their RDS PostgreSQL production database during peak demand.

By using an ML-driven tuning agent, they were able to identify bottlenecks in server-key parameters that manual inspection missed. In a 4-hour session, they achieved a 10x boost in query performance (75ms to 7ms).

This aligns with the "Autonomous Postgres" trend—moving the burden of tuning from the DBA to agents.

Pretty phenomenal, and without changing any code or having to retest/redeploy

Working for almost 15 yrs in the PostgreSQL world, this is the answer to the gap: how do you automatically optimize your Postgres instance in an efficient way? Which is also CISO approved! Well, having an actionable AI optimization solution that does the work... this a fantastic solution for many Postgres users in any platform or any Postgres flavor. Just to avoid any confusion, we are talking here about true optimization and not monitoring.

Impressive case study: zero downtime, workload-aware tuning, and a real 10× latency win on a busy RDS PostgreSQL replica is exactly the kind of practical AI automation databases need.

Unbelievable result. A real 10× latency improvement on production RDS Postgres, with zero downtime and no code changes, is exactly where database ops should be heading.

TLDR: DBtune identified and tuned key server parameters that seem to have had a large impact, including random_page_cost and max_wal_size