#137 The hidden performance tax you're paying on every cloud deployment

In this episode, Ronald and Jan sit down with Luigi Nardi, founder and CEO of DB tune, at KubeCon. Luigi brings a rare mix of academic depth (PhD in computer science, postdocs at Imperial College London and Stanford, professor at Lund University) and startup pragmatism. The conversation digs into why database tuning is fundamentally a combinatorial optimization problem that humans aren't wired to solve well, and why AI is uniquely suited for it.

DB tune focuses entirely on Postgres and deploys a narrow, production-safe AI agent that reads performance metrics and iteratively adjusts server parameters (GUCs) until the system converges on an optimal configuration. No LLMs, no hallucinations — just purpose-built ML that operates in a closed feedback loop. The agent integrates with AWS RDS, Aurora, Azure Flexible Server, Google Cloud SQL, and Cloud Native PG (the Kubernetes Postgres operator).

Luigi shares a standout story: a water management company ran the agent on their production system — with a hospital's water supply on the line — and achieved a 2.5x performance improvement in just a few hours. He also explains how tuning isn't a one-time exercise: cloud workloads change, hardware scales up and down, and DB tune's model called "Newton" was specifically engineered to prevent unstable, oscillating parameter changes. The episode closes with a compelling FinOps angle: tuning doesn't just make your database faster, it can also shrink your instance size and cut infrastructure costs — a perfect fit for the Kubernetes-native world.

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