Datadog | The Monitor blog

Avoid Azure secret rotation with secretless authentication

Datadog has introduced secretless authentication for its Azure integration, replacing manual client secret management with an identity-based model using federated credentials throu…

Investigate account-level churn risk with Product Analytics account segments

Datadog Product Analytics now supports account segments, allowing teams to combine business context—such as ARR and renewal dates—with user behavioral data. This feature shifts the…

How we improved APM Java startup by encoding a prefix trie as a JVM constant

To improve Java application startup performance, Datadog's APM team reduced class-matching overhead by 30% by optimizing how classes are selected for instrumentation. They achieved…

Instrument serverless apps with agentic onboarding

Datadog’s new serverless agentic onboarding simplifies the instrumentation of applications across various cloud platforms by automating complex, manual configuration tasks. Using a…

Understanding unfixed Kubernetes CVEs: What you can and can’t detect

Following updates to four long-standing Kubernetes CVEs, vulnerability scanners may now report these issues in clusters that were previously undetected. Because fixing the underlyi…

Find, analyze, and collaborate on user sessions in Datadog Session Replay

Datadog Session Replay streamlines the process of identifying and resolving user friction by centralizing session discovery, AI-powered analysis, and cross-team collaboration into …

This Month in Datadog - July 2026

The July episode of *This Month in Datadog* introduces several new "Bits" AI features, including Bits Release for automated code validation and Bits Chat for interacting with Datad…

Prioritize security findings with the Datadog Runtime Prioritization Engine

The Datadog Runtime Prioritization Engine (RPE) addresses the challenges of managing security in dynamic cloud environments by using live telemetry to automatically identify resour…

A practical guide to React error monitoring

This article outlines strategies for implementing effective error handling in React applications by tracking errors at the render, business, and global levels. It emphasizes the im…

Engineering the Datadog Agent for FedRAMP High® Certification

To achieve FedRAMP High certification, Datadog had to move beyond its previous proxy-based architecture, which could not sufficiently enforce strict cryptographic boundaries and fa…

Investigate every security event with an AI agent, without the frontier bill

To enable cost-effective security monitoring at scale, a new two-stage pipeline uses a small state-space model called Mambark to act as an efficient retriever for massive event str…

Normalize security logs to Google SecOps UDM with Observability Pipelines

Google SecOps packs for Observability Pipelines automate the normalization of diverse security logs into a standardized Unified Data Model (UDM) before they reach Google SecOps. Th…