Today’s end users have little to no patience for performance issues. Jitters, slow load times, and full-blown outages can quickly lead to brand damage, lost customers, and diminished revenue. That’s why it’s essential for DevOps and engineers to be able to quickly identify and resolve issues before users ever notice them. Doing this requires collecting and analyzing massive amounts of telemetry data – metrics, traces, and logs.

This article was originally published in APM Digest here. Multicasting in this context refers to the process of directing data streams to two or more destinations. This might look like sending the same telemetry data to both an on-premises storage system and a cloud-based observability platform concurrently. The two principal benefits of this strategy are cost savings and service redundancy.

Dynamic observability is the concept that the amount of data collected should scale based on signals from your environment. Elastic infrastructure is not a new concept. Much of the internet is powered by services that provision more resources based on signals derived from metrics like cpu load, memory utilization and queue depth. If we can use tools to right size our infrastructure, why can’t we also use tools to right size the amount of data we collect?

A truism amongst operations professionals is that any alert your observability platform produces should be actionable, otherwise it is just noise. Auto-remediation is a hard problem, so the most common action triggered by an alert is for an engineer to gather more data and context.

Observability has become one of the largest line items in the IT budget, second only to cloud costs. A main reason for this is teams are often stuck collecting significantly more data than they need. This is where Circonus Passport helps. Rather than filter data after it’s collected like current observability data pipeline management tools, Passport is used to filter data before it’s collected.

Sky-high observability costs or visibility gaps? This is the unfortunate trade-off many organizations have to make when it comes to determining how much telemetry data they should collect and send to their observability tools. Teams either collect more data than they need and pay the price, or they collect less and suffer visibility gaps. Today, this all changes.