Operations | Monitoring | ITSM | DevOps | Cloud

When you’re just starting out, you might picture yourself managing your IT infrastructure like Tom Cruise in Minority Report—key information projected in front of you, predicting events before they happen, controlling everything at the speed of thought with cinematic gestures on some kind of holographic computer.

We’re excited to announce a powerful new addition to your monitoring toolkit: DNS Monitoring is now available on UptimeRobot! DNS (Domain Name System) is a core component of internet functionality. When DNS records are misconfigured, hijacked, or simply expire, they can lead to serious outages, broken email services, or even security risks. That’s why we’ve introduced DNS Monitoring – to help you stay in control of your domain’s health at all times.

LangChain apps are powerful, but they’re not easy to monitor. A single request might pass through an LLM, a vector store, external APIs, and a custom chain of tools. And when something slows down or silently fails, debugging is often guesswork. In one instance, a developer ended up with an unexpected $30,000 OpenAI bill, with no visibility into what triggered it. This blog shows how to avoid that using OpenTelemetry and LangSmith. With this setup, you’ll be able to.

An enterprise client reports latency. Your dashboards say everything is fine. They blame you. You blame them. Nobody can prove it either way. This is where most monitoring efforts hit a wall. Too often, the conversation gets stuck on dashboards and tools instead of the one thing that really matters: truth. Observability isn’t about collecting metrics or building pretty dashboards.

If you manage an enterprise network, then you’ve definitely come across MPLS. Although many businesses rely on MPLS technology for large, high performing networks, they can suffer from network problems, like network congestion, that can impact user experience. Monitoring MPLS using a Network Monitoring tool is key to identifying and solving network issues that impact MPLS performance.

Knowing how to create a database is no longer just a backend task; it’s a core skill for building systems that perform under pressure and scale with complexity. With global data volumes expected to reach 175 zettabytes by 2025, developers must design systems that turn raw information into something meaningful, accessible, and usable. But what does that look like in practice? How do you go from raw data requirements to a reliable schema? Which engine should you choose?