6 comments

  • Fpoye 3 months ago

    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.

  • lnardi 3 months ago

    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.

  • elly156 3 months ago

    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.

  • shark_2709 3 months ago

    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.

  • mlinster 3 months ago

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

  • maattdd 3 months ago

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