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.

The last few years have felt less like a product roadmap and more like a scene from science fiction. Artificial intelligence didn’t simply arrive, it erupted. In what feels like a blink, we’re building software by prompting instead of programming. Our words now generate code, compose music, translate languages, and create entire digital experiences.

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.

Life is pretty good. I’ve shipped a mobile app and I’m (happily) drowning in telemetry. Battery impact, time in foreground/background per screen, crash rates, slow frames, network retries – the works. The data is brilliant; the challenge is turning signals into reliable alerts that catch real issues which are relevant to my app’s functions. So… what should I actually listen for, and where should I set the thresholds?

In large tech organizations, keeping track of every laptop, desktop, and endpoint is one of the IT department’s toughest challenges. Each device needs to be accounted for, properly assigned, and compliant with the organization’s policies, all while teams, offices, and contractors constantly change.

Modern systems move fast. Features ship daily, user behavior shifts hourly, and risks surface in minutes. In that reality, logs are not just a troubleshooting artifact. They are your most expressive data source. Logs capture the words developers write to their future selves. They carry the full story of requests, users, experiments, errors, feature flags, and revenue events.