Operations | Monitoring | ITSM | DevOps | Cloud

You spun up Azure SQL for your app backend, added Cosmos DB for global performance, and let your devs explore PostgreSQL freely. Everything worked — until the invoice hit. Your engineers need high availability and performance. Your CFO wants predictability. And you’re stuck trying to untangle what, exactly, is driving your Azure bill. You’re not alone. Between service types, pricing tiers, and throughput models, Azure database pricing can surprise even experienced teams.

Choosing the wrong metric type in Prometheus can lead to inaccurate dashboards, false positives in alerting, and missed indicators of system failure. Gauge metrics are intended for tracking values that can go up and down, such as memory usage, queue depth, or the number of active connections. Unlike counters, which only increment (or reset on restart), gauges reflect the current state of a resource at scrape time.

If you’re stuck between choosing Spike vs. PagerDuty for your on-call management, you’re at the right place. I wrote this blog post to end your confusion and help you make a better choice. I’ve presented a comparative analysis for these two tools across 4 key criteria (keep reading to find what they are). For each criterion, there’s either a winner or a tie. When it’s a tie, each tool gets one point. If there’s a winner, that tool gets two points.

This recognition highlights our commitment to simple, cost-effective IT monitoring—no frills, just results.

In your role as a network administrator, maintaining smooth, uninterrupted system performance isn’t just a one-time task; it’s your daily mission. Whether you're managing hundreds of endpoints, virtual machines, or hybrid cloud environments, CPU monitoring is one of the most critical tools in your toolkit. Without it, diagnosing performance slowdowns, service lags, or outages becomes reactive guesswork.

Four months ago, Claude Code was announced and we were requesting invites to its "Research Preview." Now? We've gone from no Claude Code to simultaneously running four or five Claude agents, each working on different features in parallel. It sounds chaotic, but it's been a natural progression as we've learned to trust AI more and as the tools have dramatically improved.

AI has emerged as a defining force in modern technology, spearheading transformation across industries. Yet, despite its promise to revolutionize workflows and unlock unprecedented efficiency, most DevOps organizations face significant hurdles in adopting AI safely and effectively. Concerns about complexity, scalability, and governance hold many decision makers back.