Operations | Monitoring | ITSM | DevOps | Cloud

The internal DevOps team at JFrog needed to provision cloud resources, create environments, and manage infrastructure for our developers. Unfortunately, it involved wasting a significant amount of time on repetitive tasks, that was slowing down the pace of innovation and taking away our developers’ focus from building new features and industry leading products.

As artificial intelligence (AI) rapidly evolves from a research novelty to an enterprise essential, one often-overlooked component is under intense pressure: the network. AI’s hunger for data and real-time performance is forcing enterprises, cloud providers, and data center operators to rethink how they interconnect data centers. Enter Managed Optical Fiber Networks (MOFN) — a modern solution for modern DCI challenges.

We’ve been thinking a lot about synthesis lately. Netdata already samples every metric every second at the edge. Engineers told us the remaining pain point was synthesis, the ability to pull hours or days or months of high‑resolution time‑series into a concise explanation they could hand to a teammate (or use themselves to debug faster).

Server performance metrics help you figure out what’s going wrong, where your bottlenecks are, and how your system handles load. They give you the data to plan capacity, fix issues before they escalate, and build more reliable infrastructure. In this guide, we’ll go over the core metrics that matter, how to monitor them effectively, and the tools that can help along the way.

For startups that need fast, flexible, and realistic environments If you're a startup moving off Heroku in 2025, you’re not alone. What once felt like magic — git push, instant deploy, no infrastructure to manage — now feels expensive, restrictive, and increasingly disconnected from how modern teams work.

…and why it matters even more in the age of AI-generated code Software development is changing fast. Thanks to tools like GitHub Copilot, Cursor, and Windsurf, developers can now generate large chunks of code in seconds. Product velocity is exploding. Startups are shipping faster than ever before. But there’s a catch: More code ≠ better code.

Real-time data integration today is not just about speed—it’s about resilience, consistency, and scalability under pressure. Businesses that master these architectures report up to 23% higher profitability because it moves reliably when it matters most. But building for that kind of reliability is where most systems fall short. Many pipelines labeled “real-time” are only fast until they hit traffic spikes, schema drift, or conflicting data models.