Operations | Monitoring | ITSM | DevOps | Cloud

When application performance monitoring detects a spike in latency or error rates, the immediate challenge is determining the underlying cause. APM logs address this by correlating performance metrics with the specific log events that occurred at the same time. Instead of switching between monitoring dashboards and manually searching through log files, APM log correlation consolidates both views.

Building a great developer experience is about more than just the code. It’s about creating a unified ecosystem where your tools work together seamlessly. That’s been the vision behind our work on the Mezmo MCP Server, and I’m excited to share it with you. At its core, the MCP Server is a universal remote for your data pipeline.

When assessing SaaS observability solutions, customers often explore features that are built into the platform, but there ia a whole collection of deployable libraries across all SaaS vendors. In Coralogix, we lead the way in deployable assets, with 4400+ alerts, dashboards, parsing rules, metric generation rules and more. But why should you care about these deployable assets, and why do they accelerate insight generation so profoundly?

Every system in your environment—cloud, on-prem, or hybrid—generates log files. They capture everything from user actions to system failures, security events, and performance issues. But with so many log types and so much raw data, it’s easy to get buried in noise and miss what matters.

In today’s technological landscape, mobile applications are on the rise, boosting efficiency, portability and accessibility in daily life, across a spectrum of industries, from financial services to food delivery. As mobile apps become more essential, the quality of their features, performance, and user experience is critical.l.

Service Level Objectives (SLOs) are a cornerstone of modern software engineering. Defining alerts around SLOs has become standard practice, but many of the common patterns in use today miss the early signals that can tell a customer before an SLO breach has occurred.

When a web service slows down or errors spike, metrics can tell you what changed (active connections rise, error rate increases), but the root cause can sometimes be found in your logs (which IPs are hammering POST endpoints, 4XX/5XX occurrences). Put the two together and you get the full observability picture. Time-series metric trends to spot incidents, and line-level details to fix them fast.