Operations | Monitoring | ITSM | DevOps | Cloud

As we move further into 2026, we wanted to pause for a moment and reflect on what the past year looked like for OnPage, not just in terms of features shipped, but in how the platform evolved to better support the way teams actually work in high-stakes environments. 2025 was a foundational year for us.

Freshservice has become a trusted system of record for IT teams managing incidents, service requests, and operational issues at scale. Tickets are logged, categorized, prioritized, and tracked with discipline. SLAs are defined. Dashboards provide visibility. On paper, everything looks covered. Yet many teams still experience missed or delayed responses when incidents truly matter, especially after hours. The gap isn’t in ticket creation. It’s in what happens next.

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.

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.

As organizations continue to adopt OnPage across regions and operational environments, providing an experience that feels natural and intuitive for every user has become increasingly important. Clear communication is essential in time-sensitive workflows, and being able to use the app in one’s preferred language supports clarity, confidence, and consistency. To support our growing global user base, OnPage is introducing multi-language localization across its mobile applications.

Over the last few years, AI has quietly shifted from a fascinating experiment to a core operational system. Enterprises aren’t just building prototypes anymore — they’re deploying LLMs into production environments where uptime directly affects customer interactions, revenue flows, and business continuity. AI has essentially become a new layer of critical infrastructure. Because of that shift, the definition of “reliability” is changing.

When your team shares one support number, someone has to decide who gets the calls when customers need help after hours. And if your team rotates on-call responsibilities weekly, which is common in IT (SRE, DevOps, ITOps, etc), clinical and field engineering teams, you’ve probably relied on manual call forwarding at some point. On paper, it seems straightforward: update the forwarding number each week to point to the person who’s on call. In practice? It often turns into a scramble.

When AT&T officially shut down its email-to-text and text-to-email service on June 17, 2025, a quiet but essential part of many organizations’ communication workflows disappeared overnight. Messages that used to be sent to addresses like simply stopped delivering. For teams who relied on those alerts to reach the on-call clinician, engineer, technician, or service lead — this created an unexpected and urgent gap. This wasn’t just a convenience feature going away.

Secure and seamless communication is at the heart of effective patient care. Whether coordinating handoffs, requesting consults, activating code teams, or managing after-hours coverage, clinicians rely on messaging systems that are reliable, fast, and built to protect patient data.

At 2:07 a.m., one of the core production nodes went down. CPU usage spiked, latency shot through the roof, and requests began timing out across the cluster. Monitoring tools lit up instantly. Datadog dashboards turned red, Prometheus fired alerts, and a webhook pushed incident payloads into Jira. Everything worked exactly as designed. Except no one responded. The alert chain fired flawlessly through machines, but the right human never saw it because it was sent via an automated phone call.