Operations | Monitoring | ITSM | DevOps | Cloud

Top 15 Application Performance Metrics for Developers and SREs in 2026

Every application tells a story of user intent, system behavior, and business impact. To truly understand how your application performs, you need to go beyond logs and errors. You need metrics that provide actionable visibility across your stack. Application performance metrics are the foundation for delivering high-quality digital experiences, and they empower DevOps teams, developers, engineers, and site reliability engineers (SREs) to respond faster, scale smarter, and continuously improve.

Web Performance Metrics: Why INP Is Your Most Practical UX Performance KPI

Every developer has seen this scene: a user clicks a button, nothing happens, they click again—still nothing—and by the third frustrated tap, three overlapping modals explode onto the screen. The page wasn’t slow to load. It was slow to respond. This highlights the importance of perceived performance—how fast and responsive a website feels to users—which can shape user satisfaction regardless of actual load times.

Redefining Application Management Services - the AIOps Way

For years, Application Management/Maintenance Services (AMS) have been the go-to solution for IT leaders trying to keep their business applications stable and running. The AMS pitch was simple: Hand over your apps to us, and we’ll manage and maintain them for you! And for a long time, that model has delivered promising results. It allows internal teams to focus on innovation while service providers handle the operational heavy lifting.

Debugging AI Agents in Production Without Losing Your Mind

AI agents are powerful, but debugging them in production is hard. Non-deterministic behavior, LLM latency, and token costs create observability challenges that traditional monitoring tools don't address. In this webinar, engineers from Inkeep and SigNoz walk through how Inkeep monitors its AI agent framework in production using OpenTelemetry-native observability.

Easily Map Logs to OCSF with Datadog Observability Pipelines

Normalizing security logs into the Open Cybersecurity Schema Framework (OCSF) is often complex, manual, and time-consuming. With Datadog Observability Pipelines, you can easily transform logs into OCSF format—right in your own environment—before routing them to destinations like Splunk, CrowdStrike, and AWS Security Lake. This video show how Security teams can use Observability Pipelines to: Collect, process, and transform logs into OCSF format automatically.

Taking Server Monitoring to the Next Level

For many years, uptime and availability have been basic standard measures of server health monitoring. But if a server is up and responding to a ping or HTTP request, does that really mean that all is well? In reality, uptime and availability alone often provide a false sense of security. A server can be technically “up” while being seconds away from a crash, running out of memory, operating with an expired license, or silently failing critical updates.

Beyond the Blue Link: UX Patterns for Google's AI Overviews, AI Mode & Answer Engines

The blue link is dying—but not in the way we expected. When Google’s AI Overviews began appearing at the top of the search results page, the SEO community panicked. Publishers watched click-through rates plummet. The Pew Research Center confirmed their fears: searchers who encounter an AI summary are half as likely to click on traditional search results (8% vs. 15%).

Build custom apps in seconds with conversational AI in App Builder

Using a drag-and-drop interface, engineering teams can create apps that support troubleshooting, improve day-to-day operations, and offer self-service access without leaving Datadog. With the new conversational AI feature, teams can turn an idea into a working app in seconds. Watch the video to see how it works..
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Essential digital experience metrics for development teams

For the team that's down in the trenches untangling legacy code, writing unit tests, and just trying to come up with sensible variable names, it's easy to lose sight of the other end of the process, where code meets customer. You test, you deploy, nothing breaks, and you move on. However, it's just as important to keep an eye on code quality in production, and how it's experienced. Experience, though, is hard to quantify. What do you measure? How do you measure it? How do you improve it? And why do you care? We lay out answers in this post.

What is OTLP and How It Works Behind the Scenes

If you have worked with observability tools in the last decade, you have likely managed, and been burnt by, a fragmented collection of tools and libraries. Each observability signal required its own tool, data formats were incompatible and had little or no correlation. For example, log records would not link to traces, meaning you had to guess which traces led to which events. The OpenTelemetry Protocol (OTLP) solves this by decoupling how telemetry is generated from where it is analyzed.

Check out features we announced at AWS re:Invent in the latest episode of This Month in Datadog

Tune in for spotlights of Bits AI SRE, now generally available, and Datadog’s MCP Server, which connects AI agents to our platform by ingesting prompts and mapping them to Datadog resources and data. Plus, we cover how to: Search logs at petabyte scale in your own infrastructure with CloudPrem Break down costs drivers at the prefix level with Storage Management Create workflows that adapt to real-world complexity with Agent Builder Detect and block credential leaks with Secret Scanning.

OpenTelemetry Collector Contrib - A Hands-on Guide

As application systems grow more complex, it becomes ever more important to understand how services interact across distributed systems. Observability sheds light on the behavior of instrumented applications and the infrastructure they run on. This enables engineering teams to gain better track system health and prevent critical failures. OpenTelemetry (OTel) has standardized how we generate and transmit telemetry, and the OpenTelemetry Collector is the engine that processes and export this data.