Operations | Monitoring | ITSM | DevOps | Cloud

In a high-pressure incident, speed is everything. During incidents, engineers often jump between tools like traces, logs, and metrics, losing context at every step. This ruins the investigation state, slows down the process, and increases MTTR.

We’re proud to announce that Coralogix has earned 196 badges across 15 categories in the G2 Spring 2026 Reports, our strongest G2 performance to date. Placing in 369 reports, this represents a significant leap from Spring 2025, when we placed in 318 reports and earned 141 badges. These results are a direct reflection of the trust our customers place in Coralogix and their willingness to share honest feedback on the world’s largest software review platform.

For mobile-first organizations, the distance between a “slow app” and a “resolved ticket” is often filled with guesswork. Mobile performance is notoriously difficult to capture because it lives at the intersection of device hardware, network stability, and local code execution. Today, we are closing that gap with the launch of Coralogix Mobile Performance.

If you’ve worked with an observability pipeline, you’ve probably experienced schema problems: a field disappears, a type shifts from string to number, or a new label quietly appears. The causes are everywhere. Different teams adopt different naming conventions. A dependency upgrade changes the shape of a library’s log output. Over time, these small, reasonable decisions compound into schema sprawl: dashboards break, alerts misfire, and teams scramble to find out what happened.

In the world of modern microservices, scale is a necessary challenge. Enterprise service inventories start modestly with a handful of components, only to balloon to hundreds over time. Traditional monitoring approaches cannot support that weight. The more organizations build, the more work they create, often only to keep systems running.

There’s a moment after an alert where you’re not fixing anything yet. You’re trying to answer a much simpler question: Is it actually down? Sometimes it’s obvious. Sometimes it’s 20 alerts at once with no clear starting point. Sometimes it’s a small upstream degradation that might cascade. Sometimes it’s just a spike that resolves on its own. That first phase is orientation. Is the signal real or transient? Is it isolated or spreading? Root cause or symptom?

Sometimes observability can feel like a second job. With many traditional tools, users must become experts in a proprietary language to ask a simple question. In these cases, developers or SRE’s can find themselves spending more time manually sifting through raw text, building complex data pipelines from scratch, and bouncing between fragmented dashboards than actually solving problems.

Alert fatigue rarely begins as a single mistake. It grows as systems scale, teams grow, and “just in case” monitoring becomes the default. A few extra alerts, another threshold, and soon the on-call channel becomes overwhelmed. Engineers get interrupted for noise or stop trusting pages; either way, real signals get missed. Reliability drops, and productivity quietly declines. Most teams respond tactically: tune thresholds, change notifications, suppress noise.

During high-priority incidents, SRE teams frequently lose critical time switching between monitoring platforms and ticketing systems. Context switching like this forces engineers to manually update incident states by copying and pasting data. The inevitable result is increased risk of information gaps and slower Mean Time to Recovery (MTTR).

Modern incident response doesn’t fail due to a lack of alerts firing. It fails because teams are overwhelmed by the sheer volume and the lack of context around them. Today, most observability and monitoring platforms generate a flood of alerts. Each one is triggered independently, even when they are symptoms of the same issue. Engineers are left trying to reconstruct the full picture while jumping between dashboards, Slack messages, and tickets.