Operations | Monitoring | ITSM | DevOps | Cloud

Disclosure: This comparison is written by our product marketing team that works closely with IT operations and on-call workflows. While we build incident alerting software ourselves, this guide is designed to help teams understand how different tools fit different operational needs. We believe there is no single “best” tool. Only the right fit for a given team.

Today’s higher education institutions operate complex digital ecosystems that were unimaginable a decade ago. Behind every college lies a portal of interconnected systems for registration, financial aid, course management, and campus services. The students using those systems are digital natives who can order food in seconds on their phones or have packages delivered the same day they order them.

When something breaks in a modern digital service, customers feel it instantly. Pages stall, requests time out, and carts are abandoned, while frustration grows long before a root cause is identified. What the world never sees is the engineering effort required to keep these systems healthy in the first place. Site Reliability Engineers (SREs) carry that responsibility every day.

On December 11, 2025, GitHub experienced intermittent disruptions that frustrated users across the globe. Developers everywhere started seeing random errors, 503s, unicorns, and CI pipeline failures. Very quickly it became clear something was wrong, even though GitHub’s status page still said ALL SYSTEMS OPERATIONAL. After the incident was over, GitHub published a postmortem that revealed the cause: scrapers. Automated tools hit GitHub with enough traffic to overwhelm key backend systems.

In Part 1, we talked about all the hidden complexity inside AI systems: the pipelines, GPUs, embeddings, vector databases, orchestration layers, and everything else that quietly determines how reliable an AI-first product really is. But all of that software still rests on something far less glamorous: the physical infrastructure underneath it.