Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Service Reliability Engineering and related technologies.

How Rootly works with Slack | An end-to-end demo.

Rootly is the AI-native on-call and incident management platform that helps you resolve incidents faster, improve system resilience, and streamline on-call operations. It’s your always-on SRE copilot that automates root cause analysis and identifies patterns that drive continuous improvement—trusted by thousands of companies like LinkedIn, NVIDIA, Replit, Elastic, Canva, Clay, Tripadvisor, and Grammarly.

How Prometheus Exporters Work With OpenTelemetry

Running distributed systems means you need clear visibility into how your services behave. Prometheus has been the standard for metrics for a long time, and OpenTelemetry is now giving teams a more consistent way to collect telemetry across their stack. In many setups, you'll have both: existing Prometheus instrumentation that's already in place, and new components instrumented with OpenTelemetry.

Bits AI SRE, Flex Frozen, and GPU Monitoring | DASH 2025

Get a first look at Datadog’s biggest product reveals from DASH 2025. Meet Bits AI SRE, your 24/7 autonomous AI Site Reliability Engineer, Flex Frozen for up to 7 years of managed log retention, and GPU Monitoring for full visibility into your AI workloads. Experience the future of observability in action.

What Are AI Guardrails

When you're shipping LLM features, a lot of the work goes into keeping the model's behavior predictable. You deal with questions like: These are everyday concerns when you integrate LLMs into production systems. Guardrails AI provides a Python framework that helps you enforce those expectations. You define the schema or constraints you need, and the framework validates both the inputs going into the model and the outputs coming back.

Pastries with SREs: From AIOps to GenAI and LLMs (lactose-free latte making)

In this episode of Pastries with SREs, we look at AIOps, where it fell short, where it worked, and how generative AI (GenAI) is reshaping what’s possible in observability today. We explore: If you’re wondering whether generative AI is different this time, this episode offers a grounded, practical look at how it’s evolving observability workflows.

You Can't Fix What You Don't Measure: Observability in the Age of AI with Conor Bronsdon

Only 50% of companies monitor their ML systems. Building observability for AI is not simple: it goes beyond 200 OK pings. In this episode, Sylvain Kalache sits down with Conor Brondsdon (Galileo) to unpack why observability, monitoring, and human feedback are the missing links to make large language model (LLM) reliable in production.

Grafana Tempo: Setup, Configuration, and Best Practices

As systems grow, understanding how a request moves across multiple services becomes harder. Traces help bring this picture together by showing the exact path a request takes, along with the timings that matter. Grafana Tempo is built for this kind of workload. It stores traces efficiently, works well with OpenTelemetry, and keeps the operational overhead low.

SRE vs DevOps vs Platform Engineering: What Are the Key Differences

Software delivery is more complex than ever. Teams need speed, reliability, and scalability to stay competitive. Site Reliability Engineering (SRE), DevOps, and Platform Engineering are three key disciplines that address these challenges. Though these terms are often used together, they are not the same and share distinct differences. In this blog, we’ll discuss each term individually, compare SRE vs. DevOps vs. Platform Engineering, and also show how they work together.

OTel Updates: Declarative Config - A Steadier Way to Configure OpenTelemetry SDKs

Application configs change over time, often in small ways that are easy to miss. They may start simple — a few environment variables, one exporter, nothing unexpected. As your instrumentation grows, you add rules for filtering health check spans, adjust sampling based on attributes, or introduce environment-specific resource settings. Each change makes sense on its own. But months later, the picture can look different across dev, staging, and production.

Embracing failure and chaos to improve system reliability and SRE team performance

In this interview with Alex Hidalgo, Field CTO at Nobl9 and author of Implementing Service Level Objectives (O’Reilly Media), we explore how traditional metrics like MTTR and MTTx can give a false sense of reliability. Alex shares how SRE teams can embrace failure, build psychological safety, and design systems that reflect the human factor behind uptime, outages, and real-world reliability.