#64 - Using AI to Reduce Observability Costs

Exploring how to manage observability tool sprawl, reduce costs, and leverage AI to make smarter, data-driven decisions.

It's been a hot minute since the last episode of the Reliability Enablers podcast.

Sebastian and I have been working on a few things in our realms. On a personal and work front, I’ve been to over 25 cities in the last 3 months and need a breather.

Meanwhile, listen to this interesting vendor, Ruchir Jha from Cardinal, working on the cutting edge of o11y to help reduce costs from spiraling out of control.

(To the skeptics, he did not pay me for this episode)

Here’s an AI-generated summary of what you can expect in our conversation:

In this conversation, we explore cutting-edge approaches to FinOps i.e. cost optimization for observability.

You'll hear about three pressing topics:

* Managing Tool Sprawl: Insights into the common challenge of juggling 5-15 tools and how to identify which ones deliver real value.

* Reducing Observability Costs: Techniques to track and trim waste, including how to uncover cost hotspots like overused or redundant metrics.

* AI for Observability Decisions: Practical ways AI can simplify complex data, empowering non-technical stakeholders to make informed decisions.

We also touch on the balance between open-source solutions like OpenTelemetry and commercial observability tools.

Learn how these strategies, informed by Ruchir's experience at Netflix, can help streamline observability operations and cut costs without sacrificing reliability.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com