Operations | Monitoring | ITSM | DevOps | Cloud

When your telemetry pipeline starts falling behind, the countdown to production impact has already begun. One Bindplane customer operating a large-scale log ingestion pipeline built on the OpenTelemetry Collector and Kafka hit that breaking point. Instead of keeping pace with incoming data, their pipeline was ingesting just 12,000 events per second (EPS) per partition/collector—and this Kafka topic had 16 partitions. In aggregate, that was roughly 192K EPS.

For many growing businesses, data infrastructure grows and evolves organically. This often results in teams running one technology for log analytics, like a self-managed ELK cluster, and a completely separate technology for application search. While functional, these disparate tech stacks begin to eat into the bottom line. Businesses grapple with fragmented skill sets, inconsistent security models, multiple vendors, and a constant operational tax.

For years, the observability industry has been obsessed with one thing: data volume. We've built incredible pipelines, optimized agents, and scaled storage to handle petabytes of logs, metrics, and traces. The promise was simple: collect more data, get more visibility. But we've hit a wall.

As Kubernetes environments continue to grow in scale and complexity, having a robust monitoring strategy is no longer just good practice, it’s essential for survival. For engineering teams in 2025, effective monitoring and observability is the bedrock of performance, reliability, and cost control. This guide dives into the critical aspects of modern Kubernetes monitoring, from key metrics to the top tools/frameworks and the rising role of AI in managing these complex systems.

When a service slows down, metrics will tell you that it’s happening but logs tell you why. For MySQL, slow queries can be a silent performance killer, gradually chewing through resources until users start complaining. By enabling MySQL’s slow query log and forwarding it to Loki (via Promtail), you can visualize query-level details right alongside your metrics on Grafana dashboards. This makes it easy to correlate what is slow (metrics) with what is causing the slowdown (logs).