Operations | Monitoring | ITSM | DevOps | Cloud

Distributed tracing is a core tool for operating modern microservices platforms. For SREs and DevOps teams, it is often the fastest way to understand latency issues, service dependencies, and unexpected failure modes. But achieving comprehensive tracing coverage is resource-intensive and time-consuming. It usually requires application changes, language-specific instrumentation, agent lifecycle management, and ongoing coordination with development teams.

Some of your users are having a party; dancing away, having a great time. But a couple of users are stuck outside in the rain, knocking on the door, trying to get in. Unfortunately, you can’t hear them because of all the noise happening inside. That’s what it feels like when you gradually roll out new features across your user base without the right monitoring.

Understanding Splunk pricing is crucial for organizations evaluating SIEM solutions. This guide examines licensing models, actual costs, and essential pricing factors to help you make an informed investment decision for your security and monitoring needs.

The CNCF reported that between 39% and 56% of organizations surveyed are now ingesting traces as part of their observability strategy. Tracing has become a cornerstone of any modern observability operation. Customers are regularly handling 10s of billions of spans every day, but with billions of spans, how can teams quickly figure out what is changing, what’s breaking, or what’s slowing down?

What remains of the 2025 AI hype? After a year of “AI will fix everything” promises, engineering teams in 2025 hit a wall of reality: AI is a tool, not a magic bullet. We’re now seeing a more practical approach: identifying broken workflows and tasks where AI can help and leveraging AI strengths like data analysis at speed and scale to derive meaningful, valuable insights. Looking ahead, 2026 will reward organizations that combine AI innovation with a practical approach.

Graylog has always been associated with log management, metrics, SIEM and security monitoring—but it’s also a great tool for creative, low-cost experiments in a home lab. I wanted to use it for real-world sensor data, so I built a DIY temperature and humidity monitor using an ESP-WROOM-32 development board and a DHT22 sensor.

In 2025, Elastic and Google Cloud created a powerhouse of AI-driven insights, providing an end-to-end search, observability, and security journey for our joint customers. We continue to partner on many opportunities for success and have made even further progress this year to empower all our users, especially around generative AI (GenAI). This blog highlights our collaboration with Google Cloud to help you harness the power of data at scale as well as our top moments from Google Cloud Next ‘25.

By Bill Balnave, VP of Customer Success at Mezmo The core promise of modern observability is simple: cut Mean Time To Resolution (MTTR). Yet, despite a boom in tooling and investment over the last four years, the data tells a sobering story: our industry is actually getting worse at finding and resolving issues. Dashboards, once our trusted guide, have become the starting point for a chaotic "dashboard hunt" that rarely leads to the definitive root cause.

It’s 5:14am, and I wake up to the squawking geese sound of my PagerDuty alert (anyone else have this sound? No?). I’m four months into working for my new team as a junior software engineer, and this is my first time being paged in the middle of the night. Most software engineers probably dread this moment, but I kind of love it. Agile ceremonies and Jira tickets suddenly don’t matter, and you’re fully focussed on stopping a customer-impacting fire.