Datadog | The Monitor blog

Monitor Oracle AI Database across clouds with Datadog

Datadog provides dedicated sub-integrations for Oracle AI Databases on AWS, Azure, and Google Cloud to bridge visibility gaps in performance and cost attribution. By combining Data…

Best practices for scaling A/B testing with experiment protocols

Datadog Experiments introduces "protocols" to help organizations scale A/B testing by transforming approved methodologies into reusable configurations that standardize metrics, ran…

Optimize AI costs with Agent Observability

Datadog Agent Observability helps organizations manage complex AI agent costs by attributing spend to specific applications and workflow steps rather than just opaque provider bill…

Build better SLOs with RUM and synthetic SLIs

Effective Service Level Objectives (SLOs) require balancing Real User Monitoring (RUM) and synthetic data to accurately track user experience benchmarks beyond backend metrics. The…

Beyond container image signing: How we enforce software supply chain policies at Datadog

Container image signing alone cannot detect compromises introduced before an image is built, such as poisoned dependencies or malicious source changes, necessitating a more compreh…

How Kubernetes DRA can improve GPU utilization and cut costs

The legacy Kubernetes device plugin model limits GPU utilization through rigid partitioning and a lack of awareness regarding specific hardware capabilities or topology. Kubernetes…

Monitor the performance of critical steps in user journeys with RUM operations

Datadog’s Operations Monitoring allows teams to isolate and track specific technical steps, such as payment processing, to measure their reliability and latency independently of un…

Introducing Scout 1.0: A new SRE model powering Bits AI with frontier performance at a fraction of the cost

Datadog has introduced Scout 1.0, a specialized AI model designed to power "Bits Investigation," an AI SRE that investigates production issues. By utilizing reinforcement learning …

How Datadog defines service criticality at scale

To optimize deployment and reliability efforts at scale, Datadog developed a system to classify service criticality by combining multiple data signals, including LLM-analyzed postm…

Unify data across Datadog BYOC with cross-cluster queries for teams and AI agents

Datadog’s Bring Your Own Cloud (BYOC) feature introduces cross-cluster queries, allowing users to search logs across multiple regions or business units with a single query. This fu…

Run incident response in your FedRAMP High environment

Datadog has extended its FedRAMP High certification to include Datadog Incident Response within the US1-FED GovCloud environment, allowing federal agencies to manage sensitive inci…

Define user actions on your web app with visual labeling in Product Analytics

Datadog’s Visual Labeler enables non-engineers to define meaningful user actions by simply selecting web elements via a browser extension, eliminating the need for code changes or …