# apm Published articles for apm. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to track Webflow Cloud app metrics with Datadog Dashboards DevFeed: [How to track Webflow Cloud app metrics with Datadog Dashboards]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [dashboards](), [Latency](), [API](), [API keys](), [Next.js](), [Node.js](), [Application Performance Management (APM)]() Tags: [api](), [api-keys](), [apm](), [dashboards](), [guides](), [latency](), [metrics](), [node-js]() ### AI overview Tutorial showing how to monitor Webflow Cloud applications by sending request counts, error counts, and latency metrics from Next.js Route Handlers to Datadog's v2 metrics API, then visualizing them in a Datadog dashboard. ### Source excerpt Learn how to track Webflow Cloud app metrics with Datadog by sending request, latency, and error data from your Route Handlers to the metrics API and dashboard. ## 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 ... ## Making Rust observability reliable at scale with OpenTelemetry DevFeed: [Making Rust observability reliable at scale with OpenTelemetry]() Original publisher: [Read original article]() Author: Björn Antonsson; Paul Le Grand des Cloizeaux; Scott Gerring Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [tracing](), [telemetry](), [Traces](), [Instrumentation]() Tags: [apm](), [instrumentation](), [latency](), [observability](), [opentelemetry](), [production](), [rust](), [scale](), [traces](), [tracing]() ### AI overview Datadog describes building an opinionated Rust tracer on OpenTelemetry to improve trace propagation, sampling consistency, and trace quality in production services. ### Source excerpt Learn how Datadog improved Rust tracing by building an opinionated OpenTelemetry-based library to help ensure consistent sampling, propagation, and trace quality at scale. ## Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration DevFeed: [Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration]() Original publisher: [Read original article]() Author: Eric Metaj; Mark Azer Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Developer Tools](), [iac-security](), [Vulnerabilities]() Tags: [apm](), [bitbucket](), [ci-visibility](), [cloud](), [code](), [code-profiling](), [code-security](), [devsecops](), [error-tracking](), [infrastructure-as-code](), [integration](), [observability](), [open-source](), [pull-requests](), [secret-scanning](), [security](), [software-delivery](), [test-optimization](), [vulnerabilities]() ### AI overview Datadog's Bitbucket Cloud Source Code integration links selected repositories with Datadog so teams can investigate production issues with source context and review security findings before merging code. ### Source excerpt Connect Bitbucket Cloud to Datadog to troubleshoot with source code in context and surface test, quality, and security feedback in pull requests. ## Debug live production code without redeploying with Datadog Live Debugger DevFeed: [Debug live production code without redeploying with Datadog Live Debugger]() Original publisher: [Read original article]() Author: Eric Metaj; Sarah Stonehill Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [debugging](), [Instrumentation](), [AI-assisted coding](), [Code]() Tags: [agents](), [ai](), [apm](), [bits-ai](), [code](), [debug](), [debugger](), [debugging](), [developer](), [ide](), [instrumentation](), [production](), [root-cause-analysis](), [tracing]() ### AI overview Datadog Live Debugger helps developers investigate production bugs without changing, restarting, or redeploying application code. It captures runtime details through logpoints, including variable values, method arguments, execution context, and request paths. Bits AI can analyze linked source code, place non-breaking logpoints, interpret collected data, and suggest fixes grounded in production behavior. ### Source excerpt Learn how Live Debugger helps you investigate production code and debug faster using Bits AI. ## 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. ## Trace AWS Lambda durable functions with Datadog DevFeed: [Trace AWS Lambda durable functions with Datadog]() Original publisher: [Read original article]() Author: Joey Zhao; Yiming Luo; Pablo Martinez Bernardo; Ethan Gracer Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry](), [Node.js](), [SDKs]() Tags: [apm](), [aws](), [aws-lambda](), [instrumentation](), [node-js](), [observability](), [python](), [serverless-monitoring](), [telemetry](), [traces](), [tracing](), [workflow]() ### AI overview Datadog instruments AWS Lambda durable executions to connect telemetry from multiple Lambda invocations into one trace, helping developers investigate workflow operations, retries, waits, and failures. ### Source excerpt Trace AWS Lambda durable executions across invocations to investigate operations, retries, failures, waits, and function status in Datadog. ## How we improved APM Java startup by encoding a prefix trie as a JVM constant DevFeed: [How we improved APM Java startup by encoding a prefix trie as a JVM constant]() Original publisher: [Read original article]() Author: Stuart McCulloch Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry]() Tags: [apm](), [instrumentation](), [java](), [jit](), [observability](), [open-source](), [optimization](), [performance](), [startup]() ### AI overview Datadog explains how it reduced Java APM startup overhead by encoding multiple class-name prefix matches as a JVM string constant. The approach improves class matching while preserving targeted instrumentation for observability. ### Source excerpt Learn how the Datadog APM team improved Java startup performance by encoding a prefix trie as a JVM string constant. ## 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. ## +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. ## From zero to traces: Choosing the right APM instrumentation method for your stack DevFeed: [From zero to traces: Choosing the right APM instrumentation method for your stack]() Original publisher: [Read original article]() Author: Tarun Kothandaraman Published: 2026-07-22T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry](), [Processes]() Tags: [apm](), [applications](), [datadog-agent](), [instrumentation](), [opentelemetry](), [process](), [traces](), [tracing]() ### AI overview The article explains how to choose between Datadog Single Step Instrumentation and manual tracer installation for distributed tracing. It distinguishes automatic tracing behavior from the method used to install the tracer, and notes when manual or custom instrumentation may still be useful. ### Source excerpt Learn why Single Step Instrumentation is our default recommendation for setting up APM traces, and when to consider other methods. ## 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. ## How Sherlocks AI uses Temporal to orchestrate AI agents for incident resolution DevFeed: [How Sherlocks AI uses Temporal to orchestrate AI agents for incident resolution]() Original publisher: [Read original article]() Author: Akshat Sandhaliya Published: 2026-07-07T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [incident](), [SRE](), [observability](), [Cloud](), [tracing](), [Graphs](), [Databases](), [Kubernetes]() Tags: [ai-agent](), [ai-agents](), [apm](), [community](), [dashboards](), [databases](), [incident](), [kubernetes](), [logs](), [monitoring](), [observability](), [sre](), [temporal]() ### AI overview Sherlocks AI describes using Temporal Cloud to run durable AI-agent investigations for production incidents. The platform also uses workflows for knowledge-graph updates, infrastructure scans, and event ingestion, with retries, checkpointing, and parallel execution to improve reliability. ### Source excerpt Sherlocks AI on how Temporal Cloud runs durable AI agent investigations, infra scans, Knowledge Graph updates, and event ingestion for SRE teams. ## 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. ## Comprehensively connect your service data with Service Remapping DevFeed: [Comprehensively connect your service data with Service Remapping]() Original publisher: [Read original article]() Author: Mariana Frangos Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry]() Tags: [apm](), [instrumentation](), [kubernetes](), [log-management](), [logs](), [metrics](), [software-catalog](), [telemetry]() ### AI overview Datadog Service Remapping standardizes service names across APM traces, logs, and metrics using existing infrastructure tags. It enables cross-product telemetry correlation without code changes or redeployments. ### Source excerpt Learn how Datadog Service Remapping unifies your telemetry across APM, logs, and metrics by letting you fine-tune service definitions without any code or configuration changes. ## 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 ## pg\_stat\_ch: a PostgreSQL extension that exports every metric to ClickHouse DevFeed: [pg\_stat\_ch: a PostgreSQL extension that exports every metric to ClickHouse]() Original publisher: [Read original article]() Author: Kaushik Iska Published: 2026-02-13T13:22:22Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [Database](), [data]() Tags: [analytics](), [apm](), [clickhouse](), [extension](), [open-source](), [postgresql](), [streams]() ### AI overview ClickHouse announces pg_stat_ch, an open-source PostgreSQL extension that records each query execution as an event and streams it to ClickHouse for query-performance analysis. ### Source excerpt We're open sourcing pg_stat_ch: a PostgreSQL extension that turns every query execution into a fixed-size ~4.6KB event and streams them into ClickHouse. ## 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. ## A Software Engineer's Guide to Observability: Part 1 - Logging DevFeed: [A Software Engineer's Guide to Observability: Part 1 - Logging]() Original publisher: [Read original article]() Author: Andreas Gounaris Published: 2025-09-17T08:04:38Z Content type: tutorial Language: en Sources: [Blueground Engineering blog]() Topics: [Logging](), [observability](), [log management](), [Software](), [distributed-systems](), [Artificial Intelligence](), [Large Language Model]() Tags: [agents](), [apm](), [distributed-systems](), [engineering](), [guide](), [incident](), [json](), [llms](), [logging](), [logs](), [observability](), [software](), [telemetry]() ### AI overview Blueground Engineering's guide explains the role of logs in observability and recommends using them primarily as a forensic tool for investigating failures. It discusses reconstructing timelines, preserving context, identifying root causes, validating system behavior, and making structured logs useful to LLMs and AI-assisted incident analysis. ### Source excerpt Blueground Engineering's observability guide to logging: why log, what to capture, JSON structure, correlation IDs, and AI-ready practices with policy and refs ## A Software Engineer's Guide to Observability DevFeed: [A Software Engineer's Guide to Observability]() Original publisher: [Read original article]() Author: Stratos Pavlakis Published: 2025-09-17T08:04:24Z Content type: tutorial Language: en Sources: [Blueground Engineering blog]() Topics: [observability](), [telemetry](), [tracing](), [log management](), [distributed-systems](), [OpenTelemetry](), [Microservices](), [SRE]() Tags: [ai](), [apm](), [distributed-systems](), [guide](), [logging](), [logs](), [metrics](), [microservices](), [monitoring](), [observability](), [opentelemetry](), [sre](), [telemetry](), [tracing]() ### AI overview A practical guide for Engineering Managers and SREs on observability, explaining when to use logs, tracing, and metrics. It provides a high-level framework before covering tools such as OpenTelemetry, ELK, and Datadog, with policies and reference implementations. ### Source excerpt A practical observability guide for Engineering Managers & SREs: understand when to use logs, tracing, and metrics, with policies, examples, and AI-driven SRE. [Next page]()