AI Is Forcing DevOps Teams To Rethink Observability Data Management
DevOps.com, Thursday, March 12th, 2026
As AI coding tools accelerate software delivery, they are also intensifying a problem DevOps and SRE teams have been dealing with for years: the unchecked growth of observability data.
In this conversation, the founders of Sawmills argue that telemetry volume is no longer just a cost issue. It is becoming a data quality problem that affects how effectively teams can monitor systems, troubleshoot incidents and make sense of production behavior.
Ronit Belson and Erez Rusovsky describe how the rise of AI-generated code is making observability harder to manage. Instrumentation is often treated as an afterthought, which means more logs, metrics and traces are being generated without much discipline around relevance, quality or downstream impact. The result is familiar to many DevOps teams: rising observability bills, more noise in monitoring systems and growing difficulty separating useful telemetry from unnecessary data.