Operations | Monitoring | ITSM | DevOps | Cloud

When a production incident strikes, a sudden latency spike, a cascading API failure, a service returning 500s at scale, every minute of downtime has a cost. Root cause analysis (RCA) is the process that turns that chaos into a clear answer: what actually broke, and why. Not the symptom that triggered the alert. The underlying cause.

A critical production alert wakes you up: p99 latency just hit 4 seconds. You drag yourself to a terminal, open five dashboards, start correlating log timestamps with trace IDs, dig through 47,000 log lines across eight services, and 90 minutes later, you finally find the culprit: an N+1 database query introduced in a deployment that shipped four minutes before the spike started. An Atatus AI SRE Agent would have identified that root cause and drafted a remediation plan in 28 seconds. Not approximation.

Modern engineering teams are drowning in telemetry data. A mid-sized Kubernetes cluster running 50 microservices can generate millions of log lines per minute. Add distributed traces, Prometheus metrics, cloud provider events, and application-level instrumentation and you're looking at terabytes of observability data every day. The problem isn't just volume. It's what you do with it.

Your logs know too much. Every debug statement, every traced request, every APM span can carry the risk of capturing something they shouldn't. A customer email. A JWT token. A credit card number. An API key that was never meant to leave your payment service. It doesn't look like a breach. There's no alert. Your observability platform just quietly accumulates sensitive data like indexed, replicated, and accessible to every engineer with log query access.

Your dashboards are green. Your thresholds are calm. Then a cascade failure starts and you don't know until users flood your status page. Traditional monitoring is reactive by design. Anomaly detection in observability changes that equation entirely.

Your on-call engineer gets paged. A critical service is down. Error rates are spiking. They open Sentry for errors. Flip to Grafana for metrics. Pivot to Kibana to search logs. Then jump to Lumigo, but that only covers the Lambda functions, not the Node.js backend throwing the actual errors. Three tabs become five. Five become eight. Half the incident is gone and your team is still piecing together what happened instead of fixing it. Sound familiar?

In microservices and cloud-native environments, vulnerabilities buried in transitive dependencies or runtime behaviors can go undetected for weeks. During that time, your attack surface keeps expanding and production systems remain exposed. The longer remediation is delayed, the greater the risk of exploitation, compliance failures, and operational disruption.

Splunk isn't bad software. It's genuinely powerful. But in 2026, a lot of engineering teams are asking a fair question: are we getting $300K worth of value out of this? More often than not, the answer is no. We went through 15 alternatives - read the docs, tested where we could, and talked to engineers who made the switch. This is what we found.

By incorporating observability tools into your stack, you can better understand how your complex infrastructure operates, reduce downtime, and empower developers to identify and fix problems quickly. However, it now takes considerably more work, time, and money to build the best observability tools for your infrastructure and applications. According to a Splunk survey, over half of the firms polled employ eight or more observability tools.

New Relic and Splunk are two prominent tools in the world of observability and monitoring, each serving distinct purposes. New Relic is used for Application Performance Monitoring (APM), offering a full-stack observability platform. It is important to note that New Relic is not a SIEM tool, its primary focus is performance monitoring. On the other hand, Splunk is used for log management, machine data analytics, and is widely utilized as a SIEM tool.