Operations | Monitoring | ITSM | DevOps | Cloud

Note: The original version of this blog post published on ;login: on February 24, 2025. When architecting the flow of observability data such as logs, metrics, traces or profiles, you’ve likely noticed that most solutions ask you to deploy an agent or collector. Understandably, you might be hesitant to deploy yet another application just so you can get your data into your storage system of choice.

CloudWatch metrics can be a very useful source of information for a number of AWS services that don’t produce telemetry as well as instrumented code. There are also a number of useful metrics for non-web-request based functions, like metrics on concurrent database requests. We use them at Honeycomb to get statistics on load balancers and RDS instances. The Amazon Data Firehose is able to export directly to Honeycomb as well, which makes getting the data into Honeycomb straightforward.

Managing containerized workloads and Windows environments requires more than just basic monitoring—it demands deep observability to prevent performance bottlenecks, optimize costs, and accelerate troubleshooting. Virtana’s latest Container Observability enhancements provide IT teams with greater control, visibility, and analytics across Kubernetes and Windows-based workloads.

Modern IT environments are complex, spanning on-premises, cloud, and hybrid infrastructures. Without deep observability at every layer, performance bottlenecks, inefficiencies, and troubleshooting challenges can drain resources and impact business outcomes. Virtana’s latest Infrastructure Observability enhancements are designed to eliminate blind spots, automate performance tuning, and simplify IT operations.

You've got systems spitting out more logs, metrics, and traces than you can handle. Your monitoring costs are through the roof. And somehow, when something breaks at 3 AM, you still can't find the exact data you need. Sound familiar? Welcome to the observability pipeline conversation—no jargon, no fluff.

Have you ever struggled with systems that fail to tell you what went wrong? The kind where you’re digging through logs at 2 AM while alerts keep piling up. In DevOps, clear visibility into your applications isn’t a luxury—it’s essential. This is where instrumentation without code changes can help. It simplifies observability, reducing the manual effort needed to track down issues. If you haven’t explored it yet, you might be making troubleshooting harder than it needs to be.

Across companies of all shapes and sizes, observability practices are maturing and getting attention at the highest levels. At the same time, cost and complexity continue to hinder efforts as teams look to emerging tools to help simplify their processes in hopes of better outcomes. With so much in flux, we went into our third annual Observability Survey hoping to get a window into the ways the community is approaching observability and where it wants it to go next.

Observability metrics provide insights into the performance, behavior, and health of applications, systems, and infrastructure — enabling observability practices, which is how a system’s internal state is understood by examining its data. As organizations continue to collect more and more data, observability metrics are a key telemetry signal for observability.