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The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.

How LivePerson optimized Logstash and Kafka performance on GCP through benchmarking

By benchmarking five GCP machine types across both Logstash and Kafka, LivePerson's observability team found that infrastructure selection (not just pipeline configuration) is one of the highest-leverage cost optimization decisions at scale.

Service Desk Automation: What It Is and How to Get Started

How much of service desk work is problem solving and how much is repeat work that continues every day? Most service desks follow the same pattern daily. Password resets, access requests, software installs, approvals, and routine fixes keep coming in. These tasks are simple on their own, yet together they take most of the team’s time and push important incidents further down the queue. The main challenge is the constant flow of repeat work that reduces time for focused tasks.

How Support Uses Honeycomb to Debug Honeycomb

You'd think that working at an observability company means everyone knows exactly where to find everything in the data. It doesn't. Especially not on the support team. We're the ones who get the tickets. We're in the telemetry every day trying to figure out what went wrong for a customer, and we do that by pointing Honeycomb at itself. Here's how that actually works, and how it's changed.

May 2026 Early Warning Signals

In May 2026, StatusGator detected 854 Early Warning Signals across SaaS, cloud, developer, and infrastructure services. Of those incidents, 695 were never acknowledged by providers, while 159 were eventually confirmed on official status pages. Throughout the month, StatusGator’s Early Warning Signals continued to surface emerging outages before many providers published updates, giving teams valuable time to investigate and respond.

Microsoft DNS management in OpUtils: One console for complete control

For network administrators, managing DNS has traditionally meant juggling zones and records across separate server interfaces, manually tracking changes, and responding to resolution failures after they’ve already caused disruption. We’re excited to introduce Microsoft DNS management in ManageEngine OpUtils, bringing DNS zone and record administration directly into the same console you already use for IP address management (IPAM).

What's New in Tempo 3.0

Tempo 3.0 introduces a major architectural shift that decouples the read and write paths, with Kafka handling durability on the write side and a new live store serving recent traces on the read side. Blocks are now written at a replication factor of one instead of three, significantly reducing storage overhead. This release also brings TraceQL metrics to general availability, adds comparison operators for filtering metric results at query time, and introduces a new Tempo CLI redact command for removing sensitive trace data on demand without waiting for retention to expire.

Inside the Grafana AI Team Weekly: AI Observability for the OTel demo and LLMSpec (May 12, 2026)

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann demos how he integrated AI Observability into the OTel demo, complete with the guards feature he introduced last week, and Principal Software Engineer Yas Ekinci gives a rare glimpse of LLMSpec, the internal counterpart of the o11ybench benchmark that we use to evaluate Assistant.

Shifting Streams and AI Surges: What Our Data Reveals About the OTT Landscape

OTT data from early 2026 shows streaming hierarchies holding steady while AI platforms reshuffled rapidly. Claude has substantially increased traffic since January, overtaking Gemini, and is on pace to challenge ChatGPT by fall. Doug Madory digs into the data in this new analysis.

Tempo 3.0 release: a new architecture for scale and lower TCO, TraceQL metrics GA, and more

Tempo started with a simple goal: make distributed tracing easier to run at scale. As tracing adoption has grown, however, so have the challenges, including higher data volumes, more complex architectures, and increasing demand for real-time insights directly from traces. Over the last year, we’ve been evolving Tempo’s architecture to meet that moment. And today, we’re sharing the results of those efforts with the release of Tempo 3.0.