Operations | Monitoring | ITSM | DevOps | Cloud

We’ve made a small improvement to Status Page analytics. Previously, analytics were limited to a fixed view of the last 30 days. While helpful, it didn’t always give you the right lens for the question you were trying to answer. Now, you can choose the time range that fits your needs.

As Agentic AI moves into enterprise environments, IT and security leaders face a critical challenge on how to leverage advanced LLMs without exposing sensitive data, intellectual property, or proprietary configurations to the cloud. You cannot build a self-driving, autonomous IT infrastructure if your security team blocks the deployment, and that’s exactly why the Fabrix.ai platform features an Enterprise-Grade LLM Integration architecture anchored by our built-in Data Security layer.

Short Summary: This guide shows how to use time-based LEFT OUTER JOINs with SQL Server temporal tables, step by step. You’ll see how dbForge tools help you fine-tune these queries so you can get accurate reports for specific points in time, fully understand how your data changes, and confirm that your logic is correct.

On February 15, 2026, Shopify experienced a widespread service disruption that impacted merchants and shoppers around the world. While the provider did not acknowledge the issue until 15:36 UTC, StatusGator’s Early Warning Signals detected unusual activity and alerted customers at 15:00 UTC, just minutes after the first outage reports began coming in. This incident highlights the importance of independent, real time monitoring.

When SendGrid goes down, your transactional emails stop reaching customers. Password resets fail. Order confirmations vanish. Support tickets never arrive. By the time you notice, customers are already complaining. For DevOps and SRE teams, checking SendGrid status shouldn't be a manual process. It shouldn't wait until customers report it either. For a team sending 10,000 transactional emails per day, a 15-minute outage means roughly 100 emails that never arrived.

Artificial intelligence has been a defining theme in IT operations for nearly a decade. Early AIOps initiatives focused on predictive analytics and anomaly detection, promising to reduce operational overhead and improve system reliability. While these capabilities delivered incremental value, they often fell short of transforming how operations actually functioned.

Amazon Web Services remains one of the most popular cloud providers, with 200+ services in 39 regions across the world. Like all providers, they have their share of outages. In 2025, IncidentHub detected 38 AWS outages, of which the one on October 20th had the most widespread impact affecting hundreds of SaaS providers simultaneously. Payments were disrupted, students lost access to classrooms, developer tooling degraded, and some IT teams experienced alerting gaps.

Most engineering leaders know they should be measuring more. What holds many of them back is a quieter concern about whether the organization is actually ready to see the numbers. This tension, however, did not keep Ganesh Datta, our co-founder and CTO, and Randy Shoup, SVP of Engineering at Thrive Market, from diving down this rabbit hole on the Braintrust podcast.

Chatbots are starting to look fairly promising for businesses of all kinds. Customers today are keen to get things resolved faster than ever. Every startup out there is tempted to take the deal. But before jumping onto the bandwagon, you need to do some thinking as to what type of chatbot you must invest in. The decisive question being, which model of conversational AI perfectly aligns with the needs of your organization.

Sometimes the biggest shifts in technology aren’t about collecting more data — they’re about who (or what) gets to act on it. In this episode of “Grafana’s Big Tent” podcast, host Tom Wilkie, Grafana Labs CTO, is joined by Spiros Xanthos, Founder & CEO of Resolve AI, Manoj Acharya, VP of Engineering for Observability at Grafana Labs, and Cyril Tovena, Principal Engineer on the Grafana Assistant team, to discuss agentic AI in observability.