How we measure data completeness at scale
To ensure reliability for both users and automated AI agents, Datadog developed a system that monitors the completeness of its massive ingestion pipelines by dividing complex workf…
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Datadog | The Monitor blog
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5 pitfalls to avoid when measuring DevEx in the AI era
To effectively measure Developer Experience (DevEx), organizations should avoid the pitfalls of tracking individual output or assuming that AI adoption automatically equates to inc…
Debug and evaluate your AI app from your coding agent with Datadog Agent Observability
Datadog has introduced a suite of new "Agent Skills" designed to help coding agents automate the complex tasks of evaluating and debugging AI applications. By leveraging the Datado…
Datadog acquires Adaptive ML
Datadog is acquiring Adaptive ML to enhance its ability to build and deploy specialized, high-performance AI agents. By integrating Adaptive ML’s fine-tuning platform with Datadog’…
Datadog achieves GovRAMP High authorization
Datadog has achieved GovRAMP High authorization, reinforcing its ability to provide secure, unified observability and security for state, local, tribal, territorial, and education …
Preparing for OMB M-26-14: How Datadog supports federal logging maturity
OMB Memorandum M-26-14 introduces a new risk-based cybersecurity framework for the federal government, shifting from prescriptive logging requirements to a maturity-based model foc…
Reduce CDN log costs with searchable archives
Datadog’s Observability Pipelines and Archive Search solve the costly dilemma of managing high-volume CDN logs by routing raw data to low-cost storage while forwarding critical sig…
How we saved over $3 million in idle compute costs with Datadog Kubernetes Autoscaling
Datadog developed Datadog Kubernetes Autoscaling (DKA) to automate multidimensional workload scaling and provide intelligent resource recommendations, addressing the trade-off betw…
Automatically enrich security logs with MITRE ATT&CK context before they reach your SIEM
Observability Pipelines' new MITRE ATT&CK Enrichment Packs solve the challenge of fragmented security telemetry by automatically mapping diverse vendor logs to a standardized frame…
How to migrate feature flags without breaking production
Feature flag migrations can be executed safely by replacing high-risk, large-scale cutovers with an incremental approach that allows two systems to run in parallel. By redirecting …
How we migrated a live routing system using AI-assisted refactoring
To overcome scaling limits caused by an inadequate key-value storage model, Datadog redesigned its Stream Router architecture using a relational schema to better manage complex dat…
Using Evaluation Frameworks with Agent Observability
Datadog Agent Observability enables AI teams to operationalize LLM evaluations by natively supporting existing open-source frameworks, such as DeepEval and Pydantic Evals, within i…