Operate

Monitoring tells you when predefined things break. Observability helps you understand why things break—including failure modes you didn't anticipate. If debugging production issues requires tribal knowledge, if you can't trace a request across services, or if your dashboards don't help during incidents, you have monitoring but not observability. The distinction matters when you're troubleshooting at 3 AM.

Start with user journey mapping to understand what "reliable" means to your customers. Set initial SLOs based on business requirements and reasonable assumptions—they don't need to be perfect. Measure for 2-3 months, then refine based on actual data. The practice of measuring and discussing reliability is more valuable than perfect initial targets.

Alert fatigue usually stems from alerting on symptoms instead of impact, lack of severity differentiation, and missing context for response. We focus on alerting that indicates user-facing impact, clear severity levels that drive appropriate response, and runbooks that make alerts actionable. The goal is fewer, better alerts—not more coverage.

Done properly, chaos engineering reduces risk by finding weaknesses before customers do. We start with game days in non-production environments, graduate to controlled production experiments with limited blast radius, and always implement abort conditions. The risk of not testing resilience is discovering your systems can't handle failure during an actual incident.

Connect operational metrics to business outcomes. Incident frequency and duration impact customer experience and revenue. On-call burden affects retention and hiring. Toil consumes engineering capacity that could ship features. We help you build the business case using your own data—showing what reliability costs, what outages cost, and where investment has the best ROI.

Start by measuring the actual burden—page volume, hours interrupted, sleep impact. Then categorize: which alerts are actionable? Which indicate real problems vs. noise? Which could be automated? Often, 20% of alert types cause 80% of pages. Quick wins come from eliminating noise and automating common responses. Our On-Call & Automation Assessment provides a structured approach.

Prioritize by frequency × time × error rate. Tasks that happen often, take significant time, and are error-prone when done manually have the best automation ROI. Common high-value targets: environment provisioning, certificate rotation, log cleanup, common incident remediation, and deployment verification. Start with runbooks you already have documented—they're automation specifications waiting to be implemented.

We assess across dimensions: observability coverage, SLO adoption, incident response effectiveness, resilience practices, automation maturity, and on-call health. Each dimension has concrete indicators—not vanity metrics, but measures that correlate with actual operational outcomes. Our assessments provide a baseline and roadmap for improvement.