Datadog | The Monitor blog

Datadog Database Monitoring introduces Database Investigator, an agentic feature that surfaces root causes and remediation steps for database performance issues.

Datadog Sheets enables flexible spreadsheet-style analysis of live cloud cost data within Cloud Cost Management.

Datadog for Government achieves FedRAMP High certification to support sensitive agency workloads with unified observability, security, and NIST compliance.

Datadog Cloud SIEM's Case Management provides end-to-end workflows to transition from security signals to structured investigations.

Two PhD candidates at Datadog's AI Research Lab discuss their contributions to Toto, a timeseries foundation model.

Datadog Database Monitoring provides Supabase developers with query-level visibility, explain plans, and one-click setup for diagnosing performance issues.

April 2026's This Month in Datadog covers the MCP Server, Datadog Experiments, Bits AI Security Analyst, and more.

Datadog Reference Tables and Observability Pipelines enable central enrichment of logs before routing to SIEM or data lake destinations.

ARFBench is a time series question-answering benchmark built from real Datadog incidents to evaluate AI anomaly reasoning.

Datadog supports TCP, UDP, and ICMP protocols in network path testing to diagnose application performance issues.

The post discusses latency between product signals in experiments and how prioritizing fixes can drive growth.

Bits AI Security Analyst handles time-intensive steps in cloud credential compromise investigations, letting engineers focus on human judgment.

Datadog Forms and Sheets collect structured developer feedback and analyze it alongside operational data.

Datadog helps Google Cloud teams evaluate AI agents, optimize GPU/TPU infrastructure, and strengthen security.

Datadog's UK availability zone on AWS enables organizations to host observability data in the UK with end-to-end visibility.

Datadog embeds widget metadata into screenshots using invisible watermarks for self-describing visualizations at scale.

Datadog's Azure DevOps Source Code integration enables code health analysis, accelerated troubleshooting, and quality enforcement.

Datadog Governance Console centralizes usage insights and automates policy enforcement to reduce risk and control costs.

The post argues that A/B testing is valuable for a wide variety of engineering purposes beyond growth and product.

Datadog IaC Security catches GitHub Actions misconfigurations in the diff before they reach production.

Datadog Observability Pipelines helps teams transform and normalize logs and metrics from OpenTelemetry for routing to ClickHouse and Datadog.

Single Step Instrumentation rules allow control over which services are traced by Datadog APM to reduce unnecessary trace data.

Datadog App and API Protection provides in-process security monitoring for Python AWS Lambda functions to detect application-level attacks.

Best practices for running offline evaluations to optimize AI agents in pre-production.

Datadog's open source SAST solution uses AI to surface code vulnerabilities more accurately and efficiently.

The Recorded Future integration enriches logs, ingests alerts, and prioritizes threats in Datadog Cloud SIEM with real-time intelligence.

Instrument Boomi integration flows with OpenTelemetry and Datadog to collect and correlate process, JVM, and database telemetry.

Guidance on which platform engineering metrics to collect and how to interpret them to quantify the platform's impact on software delivery.

How misaligned predicates and column order hurt index scan performance and how to detect this pattern using DBM to cut query latency by over 99%.

Applying a detection-based threat model to secure the GitHub ecosystem by identifying key inputs, identities, and associated risks.