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The latest News and Information on Observabilty for complex systems and related technologies.

DevOps and CI/CD Practices for Scaling Mobile App Backends in Fintech

Fintech mobile applications operate in an environment where performance, reliability, and security are equally important. A slow payment request, an API outage, or a failed deployment can affect thousands of transactions and quickly undermine customer trust. As a product grows, its backend must handle rising traffic without sacrificing stability.

Signal vs. Spend: Building Cost-Aware Observability at Slack - O11yCon 2026

It started with a single log line taking up a massive amount of volume: 500 million emissions per hour. Pulling that thread led Emma and Steven into Slack's broader logging pipeline: 311 billion logs per day at 4.4M/sec peak, with no volume limits, no per-service attribution, and no feedback to the teams generating the noise.

Building an AI Observability Agent: Lessons from the Trenches - Stripe at O11yCon 2026

Stripe shares lessons from building an incident investigation agent, from context-window blowups to why the final 5% still needs a human. In this O11yCon 2026 talk, they dig into what it takes to go from 'it works' to 'it works reliably,' including how pointing agents at like Honeycomb's speeds up on-call investigations.

AMA Recap: More Answers From the Observability Engineering Authors

Last week, we sat down with the authors of Observability Engineering for a live AMA. We ended up getting so many questions (pre-submitted and live) that we couldn't get through them all. Charity, Liz, George, and Austin kindly stuck around afterward to answer more, ranging from low-hanging observability fruits and telemetry to AI and what software engineers can do that Claude can't. Missed the live session? Watch it on demand now.

Running LLM Workloads in Production: An Operations Playbook for Teams That Did Not Sign Up for This

Somewhere in the past two years, AI quietly became an operations problem. The proof of concept your product team shipped - a support-ticket summarizer, a natural-language search box, a code-review assistant - graduated into a production dependency, and now it pages you. The failure modes are unfamiliar: latency distributions with tails measured in tens of seconds, upstream providers that throttle without warning, costs that scale with user enthusiasm rather than infrastructure size, and outputs that can be wrong in ways a health check will never catch.

Spend More Time Talking to Humans

A few months ago, I noticed something happening. I would spend all day working with LLMs—prompting them, reviewing their work, and correcting them—and when I wasn’t working on my own code, I was reviewing LLM-generated code. By the end of the day, I was exhausted. This was a very unusual thing for me: I’ve been a software developer at startups for 30 years, and while sometimes I might have gotten stressed out, I had never been exhausted by the actual act of writing code.

Progress WhatsUp Gold Recognized as a SPARK Matrix Leader in Network Observability

We’re proud to share that the Progress WhatsUp Gold solution has been recognized as a Leader in the QKS Group SPARK Matrix: Network Observability report, ahead of other vendors such as SolarWinds, Paessler PRTG and LogicMonitor to name a few. The recognition highlights the WhatsUp Gold network monitoring capabilities that help organizations gain deeper visibility into complex network environments while delivering impactful benefits for customers.

Agent Observability Deep Dive Demo | Grafana Cloud

Grafana AI Observability is our new database and platform for observing AI Agents. Over the past year at Grafana Labs, we built Agents and we needed a way to understand how they are performing, what are the costs associated with them, what's the error rate or time to the first token as well as how they are behaving. Grafana Staff Engineer, Ivana Hučková provides a deep dive demo on how Grafana AI Observability connects our experience building Agents with our experience building observability systems.

SaaS vs Self-Hosted Observability (Including BYOC): Cost, Latency, Control and Governance

Most teams don't choose an observability deployment model, they inherit one. Someone signed up for a SaaS trial three years ago, telemetry volume grew 40x, and now Finance is asking why the observability line item is the fourth-largest infrastructure cost on the P&L. Or the opposite: a self-hosted Prometheus and Elasticsearch stack was stood up when the company had twelve services, and now it needs a team of two just to keep it upright while everyone else waits on slow dashboards during incidents.