Operations | Monitoring | ITSM | DevOps | Cloud

Tracing is one of the most transformative gifts of observability. It allows engineers to follow a single request through a distributed system and see every span and dependency along the way. However, even with that visibility, some of our most basic questions stay unanswered. Why did a specific span behave differently today than it did yesterday? Why did latency rise even when nothing “broke”?

Observability data has a habit of outgrowing everything else. As telemetry volume, variety, and velocity increases, staying organized gets harder. Governance becomes messy, and the cost of digging through “everything” keeps rising. Over the past year, Coralogix’s DataPrime engine has been addressing these challenges by laying a new foundation for observability at scale.

As teams grow and telemetry scales, it becomes harder to keep track of which metrics matter. Labels pile up, cardinality increases, and costs start rising faster than anyone expected. At the same time, dashboards often stay quiet and alerts go untouched. The truth is, most teams don’t actually know how and how much of their metric data is being used, let alone which metrics are driving cost. This is exactly the problem we set out to solve.

At Coralogix, we’re constantly looking to evolve the measurements we take to better understand the efficiency of our infrastructure. We constantly assess and investigate sources of cost in our cloud infrastructure, to ensure we’re getting the best return on investment. This activity, often referred to as FinOps, is becoming a cornerstone of engineering teams.

For SREs juggling multiple services, third-party dependencies, and constant alerts, a critical service slowdown can quickly turn into chaos. APM Dashboards may show everything is fine, yet users are still experiencing problems. That gap—between application telemetry and real-world performance—can turn a five-minute fix into a two-hour war room. ‍

You can cut your log costs by removing repetitive, low-value logs early and keeping only the parts that genuinely help you understand issues. Modern systems generate logs far faster than you expect. Even when your workload stays stable, infrastructure components, retries, and background workers continue producing a steady stream of repeated entries.