Once an app is launched to market, it’s up to the engineering team to ensure that it continues to meet its SLAs. See how we use VMware Tanzu Observability (Wavefront) and Sentry to proactively monitor and fix issues before they become production problems. Every engineering leader has experienced the anxiety and stress of taking an app to production. It’s a mix of excitement and trepidation – your creation will be used in real life, but what if something goes wrong?

Last week, the first OpenObservability conference took place. This event had amazing content contributions from open source project leaders, users, and influencers. We’ve seen massive growth and adoption in the open source observability space from the inspiring work being done across tracing, logging, and especially metrics. The new data stores and capabilities are growing at breakneck speed. There are more choices— yet more complexity—than ever before.

A Harrowing Landscape The increasing complexity of modern services is forcing IT Ops teams to employ a growing landscape of disparate tools to monitor the health of their IT Stack. In fact, the number of tools has grown so much in the last few years, that one wonders how IT Ops teams are even able to effectively configure, maintain, ingest, and process all the events that these tools create.

This post is the first in a three-part series on how to effectively monitor the hosts and systems in your ecosystem, and we're starting with the one you use most: your personal computer. Metrics are a key part of observability, providing insight into the usage of your systems, allowing you to optimize for efficiency and plan for growth. Let's take a look at the different metrics you should be monitoring.

Moving to a scalable, distributed microservice architecture poses a great deal of challenges for any organization. It gets harder to understand the system and pinpoint where errors originate. Logs get much messier, and stitching together a coherent picture of a particular request can be time-consuming or downright impossible. Distributed tracing can help with all of that.

I’ve tackled this question before: how much should my observability stack cost? While the things in that post are true now as ever, I did end on one somewhat vague conclusion. When it came to figuring out exactly what you need in your stack by drawing a straight line from the business case to the money you spend, my conclusion was that “it depends.” That’s how we approached pricing at Honeycomb: it depends on your needs, so we should give you many different options.