The integration of Tines and Datadog Observability Pipelines enables security teams to automate the enrichment and filtering of log data in real time. By using Tines to drive autom…
Datadog’s Bits Database Optimization feature provides a controlled, disposable environment to benchmark query rewrites using real production schemas and representative synthetic da…
Datadog’s build-time allowlisting feature enables engineering teams to balance data privacy with observability by protecting sensitive, runtime-generated information while preservi…
To effectively secure AI agents, organizations must move beyond monitoring final API calls and instead implement a comprehensive "AI Bill of Materials" (AI-BOM) that inventories al…
Datadog’s Live Debugger allows developers to investigate production bugs in real-time without the need for code changes or redeployments by using non-breaking logpoints to capture …
As low-cost OneGov AI promotions approach expiration in 2026, federal agencies must evaluate their platform usage and costs to prepare for FY27 renewals. Datadog Cloud Cost Managem…
Datadog achieved over $1 million in monthly AI cost savings by "rightsizing" model configurations, such as migrating from Claude Opus to Sonnet and adjusting tool effort levels. By…
To accommodate the rise of AI agents, platform teams must evolve "Golden Paths" from human-centric workflows into structures that support autonomous execution patterns, such as low…
Datadog has expanded its observability for Azure Functions by introducing a Serverless Compatibility Layer that enables telemetry collection across all hosting plans and runtimes. …
OpenTelemetry tail-based sampling reduces observability costs and trace volume by selectively retaining high-value data, such as errors and latency spikes, after a trace completes.…
Datadog has developed a Systemic Risk Detection Pipeline and Risk AI Agents to shift security focus from isolated findings to interconnected "risk paths" that pose the greatest org…
Datadog Code Security’s AI-native SAST addresses the unique security risks of LLM applications, such as prompt injection and excessive agency, which traditional pattern-based tools…