DBtune 4.0 adds index tuning to PostgreSQL optimisation

DBtune has released DBtune 4.0 for PostgreSQL optimisation, extending the product beyond server parameter tuning.

The new version adds index optimisation and autovacuum monitoring, bringing three areas of PostgreSQL management into a single system alongside broader health and performance monitoring.

PostgreSQL is widely used in large organisations, but performance management often becomes more difficult as deployments grow in scale and complexity. As a result, database teams can spend significant time on investigation, maintenance and tuning, while rising compute and storage use increases cloud spending.

The release is part of DBtune's push towards what it calls Autonomous PostgreSQL, using AI-based software to automate parts of ongoing database optimisation work.

For server parameter tuning, the software tests PostgreSQL configurations against a given workload and machine type. It can then apply the best-performing settings automatically or leave final approval to the user.

The index optimisation function identifies and ranks potential index changes based on workload data, and shows the expected impact, cost and data definition language for each recommendation. It covers proposals for new indexes, as well as maintenance or removal of existing indexes where bloat or obsolescence is detected.

Autovacuum monitoring is intended to help database teams spot where PostgreSQL's autovacuum process is falling behind and diagnose the reasons. In PostgreSQL environments, autovacuum plays a central role in maintaining database health and performance by cleaning up outdated row versions and helping prevent table bloat.

DBtune said combining those areas with wider monitoring is intended to move database management from passive observation to more direct optimisation and intervention. The system is designed to show what is affecting performance and stability, and where changes are likely to have the greatest effect.

Dr Luigi Nardi, Founder and Chief Executive Officer of DBtune, linked the update to rising demands on database teams and tighter pressure on infrastructure budgets.

"As those PostgreSQL environments grow, maintaining performance and stability requires increasing amounts of specialist attention, manual intervention and infrastructure," said Dr Luigi Nardi, Founder and Chief Executive Officer of DBtune.

"At the same time, organisations are under pressure to manage rising cloud costs while preparing their technology stacks for increasing AI adoption. We believe Autonomous PostgreSQL is the next step: using Agentic AI to reduce repetitive management work during database operations. Achieving that requires a fundamentally different approach to database optimisation - one DBtune has spent a decade researching and several years turning into a product. DBtune 4.0 advances that vision by expanding beyond server parameter tuning, bringing index optimisation and autovacuum monitoring into a unified approach to Agentic AI for Autonomous PostgreSQL optimisation."

Customer results

DBtune pointed to production use at Midwest Tape as evidence of the software's effect on performance. In that deployment, average query runtime fell from 76 milliseconds to 7 milliseconds in about four hours, which it described as an improvement of about 10 times.

"We see a large improvement in query performance, around 10x. DBtune identified the fix and applied it automatically, giving us more stability on one of our most critical production databases," said Josh Lorenzen, Senior Database Developer at Midwest Tape.

The broader release is also intended to help users reduce repeated manual work carried out by database administrators, while making better use of existing infrastructure rather than relying first on added cloud resources.

Marc Linster, Fellow at DBtune, described the release as part of a broader effort to combine monitoring, diagnostics and automated action within PostgreSQL operations.

"DBtune 4.0 uses workload-aware AI technologies to improve PostgreSQL performance, allowing us to automatically identify what needs attention, determine what will have the greatest impact and turn those insights into action through automation, recommendations and diagnostics," said Marc Linster, Fellow at DBtune.

"That broader approach is an important step toward Autonomous PostgreSQL."