New Relic has established itself as a top observability platform with full-stack monitoring. Unifying all telemetry data — metrics, events, logs, and traces — into one platform delivers deep performance insights and enables faster troubleshooting without juggling multiple tools. Also, New Relic prioritizes developers with tools like CodeStream, integrating error details and telemetry directly into the IDE.

I’ve read a steadily increasing stream of articles about using AI in SRE, and I have yet to find one that inspires my trust. Each article makes impressive claims about the capabilities of AI and the way it can be applied to SRE tasks, but the vast majority are light on details. AI tools, and especially LLMs, are growing incredibly quickly, and I feel that these tools have a ton of potential.

In the area of IT infrastructure management, three terms often surface: observability, monitoring, and telemetry. These concepts, while interconnected, each play a unique role in maintaining system health and performance. Observability, monitoring, and telemetry form the backbone of any robust IT environment. Yet, their differences and interrelations can sometimes blur, leading to confusion. This article aims to demystify these terms, providing clarity on their distinct roles and how they work together.

Jaeger, a renowned distributed tracing system, has been a trusted companion for developers and operations teams seeking to unravel the complexities of microservices architectures. However, as the landscape continues to evolve, the time has come to explore Jaeger alternatives that offer distinct features and advantages.

Observability has evolved from simple system monitoring to a comprehensive discipline, blending metrics, logs, and traces into unified insights. Today, it is the backbone of modern infrastructure management and application performance optimization. As we move forward, the integration of AI and security into observability platforms is shaping the future, making them more proactive, intelligent, and robust.

DataDog and Prometheus are both popular monitoring solutions used to collect and analyze metrics and monitor the performance of systems, but Prometheus is open source and Datadog is proprietary. Datadog provides a unified platform for monitoring, troubleshooting, and optimizing modern cloud-native applications and infrastructure. Prometheus is the most popular tool for monitoring time series metrics. So, how to choose between Datadog and Prometheus?

This is an updated version of an earlier blog post to reflect current definitions of a telemetry pipeline and additional capabilities available in Mezmo Our recent blog post about observability pipelines highlighted how they centralize and enable telemetry data actionability. A key benefit of telemetry pipelines is users don't have to compare data sets manually or rely on batch processing to derive insights, which can be done directly while the data is in motion.