Most teams handle database performance the same way they always have. A slow query gets flagged, someone checks the logs, and the investigation begins. By the time the cause is found, users are already affected. It’s reactive, time-consuming, and often avoidable.

AI is starting to improve this process. Not by taking over, but by helping teams notice changes earlier and respond with more clarity.

Use the data you already collect

Databases generate a steady stream of performance data. Query timings, CPU usage, lock events, row counts, and execution plans are all there. Much of it sits unused unless something breaks.

Most monitoring tools rely on static thresholds. These can be useful, but they don’t account for workload variation or evolving system behaviour. What’s normal during a high-traffic hour on Monday looks very different from a quiet Saturday night.

AI helps by learning how your systems behave over time. It doesn’t need new data, just better use of what’s already available. Instead of fixed limits, you get alerts and insights based on patterns, making it easier to tell when something has truly changed.

Reduce time to identify the problem

When performance drops, the hardest part is often finding out why. Teams dig through dashboards, query history, and deploy logs. It takes time and rarely points to a clear cause right away.

AI can speed this up. If a query starts to behave differently, or if a plan changes after a schema update, that pattern becomes easier to see. The system flags what’s changed and helps you focus your investigation faster.

You’re still in control. The system just gets you closer to the root cause with less manual work.

Avoid alert fatigue

Many teams deal with noisy alerts. Some are triggered by predictable traffic spikes. Others come from changes that aren’t important. Over time, these alerts lose meaning and critical issues can get missed.

AI can help reduce noise by using context. Instead of firing alerts based on a static threshold, it understands historical behaviour and identifies unusual deviations. The same query that runs slower during peak hours won’t cause concern unless it slows down beyond expected variance.

Where DBmarlin fits

DBmarlin gives teams the visibility they need to understand what’s happening inside their databases. It captures query performance, execution plans, wait events, and resource usage over time, and presents all of it in a visual timeline you can explore. It also baselines the normal behaviour of your database and the SQL Statements it is executing so that you can see clearly whether when something has truly changed or it’s still behaving normally.

DBmarlin remains focused on giving teams clarity over complexity. With Co-pilot, it’s now easier to move from “something changed” to “here’s what to do about it” without slowing down or switching tools.

Want to try it out?

👉 Want to see how DBmarlin can help your organisation? Start your free trial today.