Operations | Monitoring | ITSM | DevOps | Cloud

On January 16, 2026 the social media platform X (formerly known as Twitter) and its AI chatbot, Grok, experienced a widespread outage affecting users around the world. This incident underscores why proactive outage detection matters. StatusGator’s Early Warning Signals spotted meaningful signs of disruption long before any official provider acknowledgment appeared publicly and helped organizations prepare or respond faster than waiting for status pages or press releases.

When a major carrier like Verizon goes down, the impact is immediate and widespread. On January 14, 2026, thousands of users across the United States found themselves without cellular service, unable to make calls, send texts, or access data. While social media erupted with reports of “SOS mode” on iPhones, official acknowledgment from the provider lagged behind for hours.

We’ve added new API capabilities that give you more control over your monitoring workflows – directly from code. You can now pause and resume website and ping monitors via the StatusGator API, exposing the same pause functionality that’s available in the UI.

We’ve added new organization-level security and privacy controls to give admins more control over how their StatusGator account is accessed and how data is used. You can now manage these settings from Organization settings → Security options.

Everything you need to know to run SCOM like a professional View & Download Whitepaper.

VictoriaMetrics is a fast, cost-efficient, and highly scalable time-series database designed as a drop-in replacement for Prometheus storage. It is widely used for collecting, storing, and querying metrics at scale, while remaining lightweight enough to run as a single binary or container. Because it is fully Prometheus-compatible, VictoriaMetrics supports standard PromQL queries and integrates seamlessly with Grafana.

In today's global enterprises, distributed infrastructure is the norm, not the exception. Organizations operate across continents and are driven by customer proximity and regulatory requirements. For the Elastic Stack, this reality often translates into a multi-cluster deployment model, where data is collected and stored in multiple geographically dispersed Elasticsearch clusters. But, why adopt complexity? The decision to decentralize data storage is generally driven by three critical factors.

This article is part of our series on how Datadog’s engineering teams use LLM Observability to iterate, evaluate, and ship AI-powered agents. In this first story, the Graphing AI team shares how they instrumented their widget- and dashboard-generation agents with LLM Observability to detect regressions and debug failures faster. Visibility into how large language model (LLM) applications behave in real time is essential for building reliable AI-driven systems at Datadog.

Modern, hybrid environments change continuously. But, legacy ITOps workflows assume stable infrastructure. IT environments don’t behave in predictable ways. Infrastructure changes continuously, services spin up and shut down on demand, and data formats evolve with every deployment. Most ITOps workflows, however, are still designed around the assumption of stability. That mismatch drives failure. Static runbooks expect environments to stay put.