# Application Performance Management (APM) Application performance management is the operational practice that follows application performance monitoring. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Beyond the 200 OK: Architecting Observability for AI DevFeed: [Beyond the 200 OK: Architecting Observability for AI]() Original publisher: [Read original article]() Author: Adriano Mota Published: 2026-09-11T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [ai observability](), [observability](), [Application Performance Management (APM)](), [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-observability](), [api-logging](), [api-metrics](), [api-monitoring](), [api-security](), [api-testing](), [apm](), [artificial-intelligence](), [blog](), [generative-ai](), [llm](), [mcp](), [observability]() ### AI overview An article about designing observability for AI systems beyond traditional APM metrics, with emphasis on tracking quality, cost, retrieval, and agent behavior. ### Source excerpt Traditional monitoring tools, such as application performance monitoring (APM), were engineered to monitor deterministic software where specific inputs reliably lead to predictable outputs through hard-coded logic. When a traditional API fails, it usually throws a 500 Internal Server Error. But when an AI agent fails, it might return a perfectly healthy 200 OK status code ... ## Monitor Azure Functions across every hosting plan with Datadog DevFeed: [Monitor Azure Functions across every hosting plan with Datadog]() Original publisher: [Read original article]() Author: Duncan Harvey; Kathie Huang; Piyali Banerjee Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Azure](), [serverless monitoring](), [Serverless](), [telemetry](), [tracing](), [Traces](), [Application Performance Management (APM)](), [cpu](), [Node.js](), [Python](), [.NET]() Tags: [apm](), [azure](), [azure-functions](), [cpu](), [digital-experience-monitoring](), [metrics](), [monitor](), [monitoring](), [net](), [node-js](), [observability](), [performance](), [python](), [serverless](), [serverless-monitoring](), [telemetry](), [traces](), [tracing]() ### AI overview Datadog Serverless Monitoring expands observability for Azure Functions across Linux and Windows hosting plans, with telemetry collection, distributed tracing, enhanced CPU metrics, and continuous profiling. ### Source excerpt Trace requests, get enhanced CPU metrics, and profile code across Azure Functions hosting plans with Datadog Serverless Monitoring. ## Control trace volume with OpenTelemetry tail-based sampling DevFeed: [Control trace volume with OpenTelemetry tail-based sampling]() Original publisher: [Read original article]() Author: Bill Meyer; Eddie Cai Published: 2026-08-21T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [OpenTelemetry](), [Traces](), [tracing](), [observability](), [Application Performance Management (APM)](), [configuration](), [Latency]() Tags: [apm](), [architecture](), [distributed-systems](), [how-to](), [latency](), [learn](), [metrics](), [observability](), [opentelemetry](), [rideshare](), [synthetic](), [traces]() ### AI overview A guide to using OpenTelemetry tail-based sampling to reduce exported trace volume while retaining errors, slow requests, and other diagnostically valuable traces. It explains the difference between head- and tail-based sampling, the role of Span Metrics, and the collector architecture required to evaluate complete traces. ### Source excerpt Learn how to configure tail-based sampling in the OpenTelemetry Collector to drop noisy traces, keep the ones that matter, and control APM costs. ## Port's Integration Catalog: 200+ Tools for Your Context Lake DevFeed: [Port's Integration Catalog: 200+ Tools for Your Context Lake]() Original publisher: [Read original article]() Author: Alina Barenboim Published: 2026-08-10T16:27:08Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [CI/CD](), [Kubernetes](), [GitHub](), [Provisioning](), [incident management](), [Application Performance Management (APM)](), [Security](), [Code review](), [Deployment](), [DevOps](), [GitHub Actions](), [GitLab]() Tags: [apm](), [ci-cd](), [deployment](), [devops](), [git](), [github](), [github-actions](), [gitlab](), [incident-management](), [jenkins](), [kubernetes](), [review](), [security](), [vulnerabilities]() ### AI overview Port's Integration Catalog describes more than 200 integrations that unify engineering-tool data in a queryable Context Lake. It covers Git, Kubernetes, CI/CD, incident management, APM, security scanning, service mesh, policy violations, deployments, build results, and code activity, with support for custom integrations through Ocean, connectors, APIs, events, and MCPs. ### Source excerpt Explore Port's 200+ integrations across CI/CD, Kubernetes, incident management, security, and more, all feeding one Context Lake. ## BrowserStack Newsletter July 2026 DevFeed: [BrowserStack Newsletter July 2026]() Original publisher: [Read original article]() Author: Devanshi Jain Published: 2026-08-05T10:02:29Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Test automation](), [Artificial Intelligence](), [Playwright](), [Selenium](), [Cypress](), [ide](), [Application Performance Management (APM)](), [dashboards](), [Extension](), [Emulator](), [simulator]() Tags: [2026](), [agentic-ai](), [automation](), [automation-testing](), [cursor](), [cypress](), [features](), [ide](), [newsletter](), [product](), [selenium](), [testing]() ### AI overview BrowserStack's July 2026 newsletter announces Test Companion, an agentic AI feature for IDE-based test automation, and BrowserStack Load Testing, which combines backend load generation, real-browser performance, and APM metrics. It also highlights updates for certificates, screenshots, emulators, simulators, mobile browsers, Chrome extensions, audio logs, and diagnostic data. ### Source excerpt Read what's new at BrowserStack in July 2026 - New launches, product features, and more! ## +14% activated users for AppSignal: designing a new homepage in code DevFeed: [+14% activated users for AppSignal: designing a new homepage in code]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Application Performance Management (APM)](), [error tracking](), [Tooling](), [Code](), [Figma](), [Artificial Intelligence]() Tags: [agentic-development](), [ai](), [apm](), [case-study](), [code](), [design](), [design-for-devtools](), [developer-marketing](), [developer-products](), [engineering](), [error-tracking](), [figma](), [product-development](), [tooling]() ### AI overview Evil Martians designed and shipped a new AppSignal homepage primarily in code, combining code, Figma, and AI during exploration and prototyping. The selected direction was validated through an A/B test that showed a 14% increase in activated users over the previous site. ### Source excerpt We designed and shipped a fresh AppSignal homepage in code, then validated it in an A/B test with a 14% lift in activated users. ## Use OpenTelemetry-native observability with Datadog from ingestion to investigation DevFeed: [Use OpenTelemetry-native observability with Datadog from ingestion to investigation]() Original publisher: [Read original article]() Author: Shanel Huang Published: 2026-07-20T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [OpenTelemetry](), [observability](), [telemetry](), [Application Performance Management (APM)](), [infrastructure monitoring](), [Instrumentation](), [Traces](), [log management]() Tags: [apm](), [infrastructure-monitoring](), [instrumentation](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [open-source](), [opentelemetry](), [sdks](), [serverless](), [telemetry](), [traces](), [tracing]() ### AI overview This article explains how Datadog supports OpenTelemetry-native observability from instrumentation through ingestion and investigation. It covers vendor-neutral OTLP ingestion through the OTel Collector's standard HTTP exporter and Datadog's direct OTLP intake for metrics, logs, and traces, including use cases such as managed services and serverless environments. ### Source excerpt Learn how you can use vendor-neutral telemetry while preserving Datadog's infrastructure and APM experiences. ## Datadog named Leader in 2026 Gartner® Magic Quadrant™ for Observability Platforms DevFeed: [Datadog named Leader in 2026 Gartner® Magic Quadrant™ for Observability Platforms]() Original publisher: [Read original article]() Author: Yanbing Li Published: 2026-07-15T00:00:00Z Content type: news Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability](), [agent observability](), [OpenTelemetry](), [Application Performance Management (APM)](), [Incident response](), [Artificial Intelligence]() Tags: [agent-observability](), [ai](), [apm](), [incident-response](), [llm](), [observability](), [opentelemetry]() ### AI overview Datadog announces that Gartner named it a Leader in the 2026 Magic Quadrant for Observability Platforms for the sixth consecutive year. The article highlights unified observability and security, autonomous alert investigation, visibility into AI agents and LLM applications, telemetry pipelines with OpenTelemetry support, and end-to-end application performance monitoring. ### Source excerpt Datadog has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms for the sixth consecutive year. Learn more. ## BrowserStack Newsletter May 2026 DevFeed: [BrowserStack Newsletter May 2026]() Original publisher: [Read original article]() Author: Devanshi Jain Published: 2026-06-24T04:07:56Z Content type: news Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Application Performance Management (APM)](), [dashboards](), [Artificial Intelligence](), [Mobile Testing](), [iOS](), [Cypress](), [Figma](), [Robot Framework](), [Storybook]() Tags: [2026](), [ai](), [ai-agents](), [features](), [launch](), [metrics](), [newsletter](), [performance](), [product](), [testing]() ### AI overview BrowserStack's May 2026 newsletter highlights a QA Leadership Summit session on building trustworthy AI testing workflows, the launch of BrowserStack Load Testing, and product updates across mobile testing, visual review, accessibility, scanning, and automation. The Load Testing platform combines backend load generation, real-browser performance, and APM metrics, using AI agents to correlate data and identify root causes. ### Source excerpt Read what's new at BrowserStack in May 2026 - New launches, product features, and more! ## Monitor OVHcloud with Datadog DevFeed: [Monitor OVHcloud with Datadog]() Original publisher: [Read original article]() Author: Ellie Cohen; Eddie Cai Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [log management](), [observability](), [Application Performance Management (APM)](), [telemetry](), [Cloud](), [data](), [AWS IAM]() Tags: [apm](), [cloud](), [iam](), [infrastructure](), [infrastructure-monitoring](), [kubernetes](), [log-management](), [logs](), [metrics](), [observability](), [traces]() ### AI overview This post explains how to centralize OVHcloud logs in Datadog Log Management and collect metrics and APM traces from OVHcloud infrastructure. It focuses on consistent observability across multi-cloud environments, including centralized search, alerting, analysis, and out-of-the-box monitoring. ### Source excerpt Centralize OVHcloud logs, host metrics, and APM traces in Datadog for consistent visibility across your entire multi-cloud environment. ## DASH 2026 End-to-End Observability: Guide to Datadog's newest announcements DevFeed: [DASH 2026 End-to-End Observability: Guide to Datadog's newest announcements]() Original publisher: [Read original article]() Author: Datadog Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability](), [OpenTelemetry](), [Instrumentation](), [telemetry](), [real user monitoring](), [Application Performance Management (APM)](), [Front end](), [Kubernetes](), [dashboards](), [Monitoring](), [Cloud](), [Network]() Tags: [announcements](), [cloud](), [dash](), [digital-experience-monitoring](), [frontend](), [infrastructure](), [instrumentation](), [integrations](), [kubernetes](), [metrics](), [monitoring](), [observability](), [open-source](), [opentelemetry](), [saas](), [telemetry](), [traces](), [tracing]() ### AI overview DASH 2026 announcements expand Datadog's end-to-end observability coverage from frontend monitoring to the physical network. The roundup highlights faster instrumentation, deeper digital experience monitoring, broader cloud and infrastructure integrations, and native OpenTelemetry support, including correlated metrics, logs, and traces for infrastructure and Kubernetes workflows. ### Source excerpt A roundup of everything we announced at DASH 2026, including Service Remapping, new RUM and APM Single Step Instrumentation options, and Pipeline Simulation. ## Monitor Nebius AI Cloud with Datadog DevFeed: [Monitor Nebius AI Cloud with Datadog]() Original publisher: [Read original article]() Author: Ellie Cohen; Eddie Cai Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Cloud](), [observability](), [GPU](), [log management](), [telemetry](), [Large Language Model](), [Application Performance Management (APM)](), [agent observability](), [Kubernetes](), [AI Models]() Tags: [agent-observability](), [apm](), [cloud](), [gpu](), [gpu-monitoring](), [infrastructure-monitoring](), [kubernetes](), [llm](), [log-management](), [logs](), [observability](), [performance](), [telemetry]() ### AI overview This article explains how to monitor Nebius AI Cloud workloads with Datadog by centralizing logs, collecting infrastructure metrics and APM traces, tracing LLM applications, and correlating signals across GPU, training, inference, Kubernetes, and other cloud environments. ### Source excerpt Monitor Nebius AI Cloud workloads with Datadog. Centralize logs, track GPU performance, trace LLM apps, and get unified multi-cloud observability. ## A Software Engineer's Guide to Observability: Part 2 - APM & Distributed Tracing DevFeed: [A Software Engineer's Guide to Observability: Part 2 - APM & Distributed Tracing]() Original publisher: [Read original article]() Author: Andreas Gounaris Published: 2026-06-02T12:27:24Z Content type: tutorial Language: en Sources: [Blueground Engineering blog]() Topics: [Application Performance Management (APM)](), [tracing](), [observability](), [telemetry](), [Instrumentation](), [debugging]() Tags: [ai](), [apm](), [debugging](), [devops](), [distributed](), [engineering](), [guide](), [instrumentation](), [monitoring](), [observability](), [tracing]() ### AI overview This guide explains how APM and distributed tracing complement logs by showing application latency, errors, throughput, and time spent across code paths and services. It covers instrumentation, span collection, and tracing workflows for investigating performance problems. ### Source excerpt Blueground Engineering's observability guide to APM: why tracing matters, auto-instrumentation strategies, custom span best practices, and AI-enhanced debugging workflows In Part 1, we covered logging as your forensics tool for understanding what happened after you've identified a problem. Logs are great at telling you ## Mitigating Denial-of-Service Vulnerability from Unrecoverable Stack Space Exhaustion for React, Next.js, and APM Users DevFeed: [Mitigating Denial-of-Service Vulnerability from Unrecoverable Stack Space Exhaustion for React, Next.js, and APM Users]() Original publisher: [Read original article]() Published: 2026-01-13T17:00:00Z Content type: article Language: en Sources: [Node.js Blog]() Topics: [Node.js](), [vulnerability](), [DDoS](), [Next.js](), [React](), [Application Performance Management (APM)](), [Availability](), [JavaScript]() Tags: [apm](), [availability](), [javascript](), [next-js](), [node-js](), [react](), [security](), [vulnerability]() ### AI overview Node.js describes a denial-of-service vulnerability in which stack space exhaustion can cause an immediate, uncatchable process exit when async hooks are enabled. The issue affects applications using React Server Components, Next.js request-context tracking, and APM instrumentation. A January 2026 Node.js security release adds a mitigation, while applications and frameworks should adopt stronger defenses such as limiting attacker-controlled recursion. ### Source excerpt Node.js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts. ## GraphOS Router APM Dashboard Templates for Datadog DevFeed: [GraphOS Router APM Dashboard Templates for Datadog]() Original publisher: [Read original article]() Author: Matthew Ratzke Published: 2025-10-07T07:00:47Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphOS](), [datadog](), [Application Performance Management (APM)](), [observability](), [telemetry](), [tracing](), [GraphQL]() Tags: [announcement](), [apm](), [datadog](), [graphos](), [graphql](), [instrumentation](), [observability](), [telemetry](), [tracing]() ### AI overview Apollo is launching APM dashboard templates for Datadog that provide platform and SRE teams with observability views for GraphOS Router performance. The templates map Router spans and metrics into Datadog, expose GraphQL errors, support router-to-subgraph drill-down, and correlate performance with deployments and versions. ### Source excerpt Today we're launching APM dashboard templates for Datadog, so platform and SRE teams can get best practices observability into GraphOS Router performance in just minutes. Previously teams would need to determine the important information to monitor, what telemetry is available, and then how to configure the dashboards. This could take hours or days of an engineers time to initially set up and refine over time. ## Are you checking your top two transactions? DevFeed: [Are you checking your top two transactions?]() Original publisher: [Read original article]() Author: Pedro Gil Carvalho Published: 2023-07-28T13:19:09Z Content type: opinion Language: en Sources: [Pedro Gil Carvalho]() Topics: [Application Performance Management (APM)](), [Maintainers](), [Software]() Tags: [apm](), [customer-experience](), [datadog](), [elk](), [maintainers](), [self-hosted](), [software]() ### AI overview This short engineering tip recommends periodically reviewing the one or two transactions that consume most of a software service's time. Using available APM data, engineering leaders can discuss performance with maintainers and identify architecture improvements affecting costs or customer experience. ### Source excerpt Short edition today, because not everything needs to be a ten page essay. ## Diagnosing Android ANRs with Runtime Traces and APM Data DevFeed: [Diagnosing Android ANRs with Runtime Traces and APM Data]() Original publisher: [Read original article]() Author: Amanjeet Singh Gurtatta Published: 2022-01-21T17:52:38Z Content type: tutorial Language: en Sources: [Amanjeet Singh]() Topics: [Android](), [observability](), [Application Performance Management (APM)](), [debug](), [Traces](), [Crashlytics](), [Google Play]() Tags: [android](), [android-vitals](), [anr](), [apm](), [crashlytics](), [debug](), [firebase](), [google-play](), [mobile-performance](), [trace]() ### AI overview This article explains why Android Application Not Responding events are difficult to reproduce and diagnose. It describes how the Android runtime responds with SIGQUIT and ANR traces, then introduces approaches APM libraries can use to collect ANR data, including watchdog monitoring. ### Source excerpt Solving the ANRs Riddle on Android ## Monitoring Node.js Using Datadog APM DevFeed: [Monitoring Node.js Using Datadog APM]() Original publisher: [Read original article]() Author: GameChanger Published: 2020-01-29T19:36:34Z Content type: tutorial Language: en Sources: [GameChanger]() Topics: [Application Performance Management (APM)](), [Node.js](), [observability](), [Monitoring](), [API](), [dashboards](), [Traces](), [Logging]() Tags: [api](), [apm](), [dashboards](), [datadog](), [logging](), [metrics](), [node](), [node-js](), [observability](), [traces]() ### AI overview This article explains how GameChanger integrated Datadog APM to diagnose performance issues in a high-volume Node.js API server. It describes APM concepts, including request-level tracing, function calls, database queries, response tracking, dashboards, logs, and infrastructure monitoring, and briefly discusses the supporting Node.js client library and open-sourced utility code. ### Source excerpt Intro GameChanger's Team Manager is built on top of a Node.js API server. Although we've had a lot of monitoring and logging throughout it's lifetime, most of our metrics were at an aggregate level. By alerting on symptoms, it was easy to spot that something was broken, but not necessarily what or why. With the server handling over 100 million requests per day, over 1000 per second, we needed a better way to drill into what the server was doing and figure out causes. Operating at that scale meant that local testing was at best an approximation of production realities. Integrating Datadog APM (Application Performance Monitoring) has solved a lot of our observability issues. It wasn't always straightforward to work with--some of the tooling isn't obvious and the documentation is all over the place--but now that things are up and running, it'd be hard to imagine a world without it. We've also open sourced some of the APM utility code we made along the way as an NPM package. Outline What is APM? What does Datadog provide? What challenges did we face? What doesn't Datadog provide? A feature wishlist Introducing @gamechanger/datadog-apm Conclusion What is APM? APM Stands for Application Performance Monitoring. It can take different forms from one offering to another, but it generally consists of breaking down what your server is doing while accomplishing a specific unit of work. For example, if your server offers an API, the APM will keep track of when a request starts, what functions are called to handle that request, what database queries are performed, and what the response is. A representative flame chart from Datadog's APM for one API request. In Datadog's words, APM "provides you with deep insight into your application's performance - from automatically generated dashboards for monitoring key metrics, like request volume and latency, to detailed traces of individual requests - side by side with your logs and infrastructure monitoring". What does Datadog provide? In ad ## Fishing For Correlations DevFeed: [Fishing For Correlations]() Original publisher: [Read original article]() Author: GameChanger Published: 2019-02-12T19:51:49Z Content type: article Language: en Sources: [GameChanger]() Topics: [observability](), [Application Performance Management (APM)](), [Instrumentation](), [Koa](), [Monitoring](), [Node.js](), [PostgreSQL](), [Logging](), [async/await]() Tags: [back-end](), [instrumentation](), [logging](), [metrics](), [monitoring](), [node-js](), [observability](), [performance-monitoring](), [postgresql]() ### AI overview The article describes GameChanger's effort to improve observability during performance degradation in a new back-end system. It discusses instrumenting a Node.js and Koa application, using PostgreSQL, Datadog metrics and APM, and Loggly for centralized logging. The team can monitor system and request metrics but still faces challenges correlating logs, timelines, and behavioral trends. ### Source excerpt Have you ever participated in a firefight where the root cause seems unclear, then suddenly the symptoms self-resolve? Sometimes inadvertent action may cause it, other times it appears miraculous. If you find yourself in this situation, or excusing recovery with "it was a one-off blip," your system likely lacks sufficient observability. With development and operations increasingly converging, application instrumentation continues to concern many teams building new platforms and maintaining legacy ones. The general rule of thumb when introducing observability into a system is to instrument everything. However, this philosophy creates a user experience challenge. How do we deliver the salient system correlations to the people who can best leverage them? The platform team at GameChanger recently had the opportunity to tackle this question when we encountered performance degradation while scaling a new back-end system. We are not yet finished, but we have progressed meaningfully. Diving into the black box Modern Node.js, using async/await with Koa, powers GameChanger's nascent Team Manager product. PostgreSQL serves as the primary datastore and PgBouncer manages connection pooling to the database. We already use Datadog to monitor many of our legacy systems. Starting with Datadog for a new system made sense for a number of reasons: Vendor familiarity: engineers already understand the operational model and where to look for diagnostics Breadth of functionality: support for a wide variety of metrics, application performance monitoring (APM) and centralized logging, and mature monitor types, ensure our telemetry can scale alongside the codebase Low adoption cost: integrating new services with our existing Datadog configuration is trivial After building an MVP, our baseline instrumentation included typical system level metrics, such as load average, CPU utilization, and used memory, as well as custom metrics for all requests like duration and status code. Such metrics enabl