Press Release: DBtune takes the next step toward Autonomous PostgreSQL

Malmö, Sweden, October 6, 2026 — DBtune, the company behind Agentic AI for Autonomous PostgreSQL optimization, today released DBtune 4.0, advancing its vision for Autonomous PostgreSQL. The release expands DBtune beyond server parameter tuning, adding index optimization and autovacuum monitoring to create a unified approach to PostgreSQL optimization.

Consistently in the top four relational databases in DB engines rankings, PostgreSQL is well established in enterprise environments, but maintaining performance and stability becomes harder as PostgreSQL environments grow in size, number and complexity. Database performance and health management require significant investigation, maintenance and optimization, consuming valuable team capacity. Adding compute and storage often becomes the quickest way to protect performance and application SLAs, but this also increases infrastructure costs and adds to the complexity of managing growing application and data demands, while making it harder to improve performance.

“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 CEO of DBtune. “At the same time, organizations 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 optimization — 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 optimization and autovacuum monitoring into a unified approach to Agentic AI for Autonomous PostgreSQL optimization.”

DBtune 4.0

DBtune 4.0 brings together three areas of PostgreSQL optimization — server parameter tuning, index optimization and autovacuum monitoring — supported by broader performance and health monitoring. As PostgreSQL moves toward more autonomous operation, monitoring needs to become more comprehensive compared to historical approaches. This is achieved by providing the context needed to understand what is affecting performance and stability, as well as where optimization has the greatest impact. DBtune transforms passive monitoring into pro-active optimization.

Some of the key highlights of this release include:

• Server parameter tuning: DBtune tests PostgreSQL server parameter configurations against the workload and machine type, and applies the best-performing settings automatically, or lets the user approve changes first.
• Index optimization: DBtune identifies and ranks index opportunities based on the workload, showing the expected impact, cost and DDL. This includes recommendations for new indexes that can significantly speed up specific critical queries, as well as existing indexes that may require maintenance due to bloat or removal because they are obsolete. The user remains in the loop and decides whether to apply each change.
• Autovacuum monitoring: DBtune identifies where autovacuum is falling behind and helps diagnose what is causing the problem.

Together, these capabilities help keep PostgreSQL operational, stable and tuned for high performance, while reducing repetitive database optimization work. This helps teams deliver a high-performing user experience, free up capacity for strategic priorities and innovation, and get more value from existing infrastructure.

Measurable results in production

DBtune is already delivering measurable performance improvements for customers. At Midwest Tape, DBtune reduced average query runtime from 76 ms to 7 ms in about four hours — an improvement of about 10x.

“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.

“DBtune 4.0 uses workload-aware AI technologies to improve PostgreSQL performance, allowing us to automatically identify what needs attention, define what has 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.”

About DBtune

DBtune is a Stanford University spin-off founded in 2021 by Dr. Luigi Nardi and headquartered in Malmö, Sweden. The company grew out of a decade of research into AI and machine learning optimization algorithms. DBtune’s core database optimization technology emerged when PostgreSQL Core Team member Magnus Hagander recognized the potential of this research to address complex database tuning challenges. Today, DBtune brings together engineers, AI specialists and database experts to advance the development of Autonomous PostgreSQL.