# DBtune autonomous optimization blows PostgreSQL bottlenecks away — retained record

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source: [news](https://highsignal.app/data/news) (news:blocksandfiles)  published 2026-10-07 13:36 ingested 2026-10-08 02:55

[original source ↗](https://www.blocksandfiles.com/data-management/2026/10/07/dbtune-autonomous-optimization-blows-postgresql-bottlenecks-away/5301616)

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DBtune autonomous optimization blows PostgreSQL bottlenecks away
Startup DBtune is developing a PostgreSQL autonomous database optimization capability which could make PostgreSQL better than Oracle.
DBtune is headquartered in Malmö/Lund, Sweden, and was started up in 2020 by machine learning researcher Dr.Luigi Nardi as a spin-off from Stanford University and Lund University. It received startup grants from, we understand, the Wallengberg Foundation and Vinnova in the 2020-2021 period, and raised $2.4 million in a July 2023 seed round from VCs and angel investors.
The aim is to use agentic AI and optimize for a specific workload, use case and machine, using server parameter tuning, index optimization and autovacuum monitoring. DBtune says it 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. DBtune v 4.0 uses machine learning and AI to dynamically adapt server parameters, recommend optimal indexing strategies, and monitor autovacuum operations, based on specific workloads and hardware constraints.
Nardi said: “As … PostgreSQL environments grow, maintaining performance and stability requires increasing amounts of specialist attention, manual intervention and infrastructure. 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.”
He believes that “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.”
It identifies and ranks index opportunities based on the workload, showing the expected impact, cost and DDL (Data Definition Language). 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 or duplicates. DBtune also identifies where autovacuum is falling behind and helps diagnose what is causing the problem.
Midwest Tape, a DBtune customer, is a full-service media distributor that provides physical and digital content exclusively to public libraries across North America, Australia, and New Zealand. It uses a dedicated PostgreSQL replica to provide metadata to public libraries with its Hoopla digital content service. Midwest used DBtune for AWS RDS PostgreSQL performance tuning to resolve database query bottlenecks and achieved a 10.8x performance improvement, with average query runtime decreasing to 7 ms from 75.9 ms.
DBtune has benchmarking data following a benchmarking test with Intel Xeon processors to determine whether autonomous optimisation could uncover meaningful performance gains beyond extensive manual parameter tuning. It produced three main results:
• DBtune increased transaction throughput by 87 percent during the initial optimisation phase
• It delivered a further 17 percent improvement on an already heavily optimised system
• It reduced disk I/O activity by 43 percent and WAL generation by 23 percent.
DBtune autonomously identified the dominant bottlenecks, validated their root causes through experimentation and translated those findings into measurable performance and efficiency gains, without changes to the application, schema or hardware.
This autonomous optimisation process delivered a 17 percent throughput improvement on a system that had already undergone significant manual optimisation, which suggests that autonomous optimisation can uncover performance headroom even when traditional tuning efforts have already addressed the most obvious opportunities.
Fully autonomous Postgres is still some way off, but Luigi argues that spiralling cloud costs and the complexity of managing growing application and data demands is increasing the importance of using AI to optimise database instances. While PostgreSQL is already well established in the enterprise market, Luigi believes it will take significant adaptation of the source code to enable autonomous optimisation, but if the PostgreSQL community gets it right it will create an advantage over the likes of Oracle.
Download a product paper here, the MidWest Tape case study here, and access DBtune presentations here.
Bootnote
Autovacuum is a PostgreSQL background daemon (process) that aims to reduce worker-level IO by automatically cleaning up dead rows, reclaiming storage, and updating query planner statistics.
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