Datadog is advancing toward Federal Risk and Authorization Management Program (FedRAMP) High authorization, which will ultimately enable federal agencies to more effectively monitor, secure and optimize their critical applications and infrastructure while adhering to stringent compliance frameworks.
US federal agencies are required to meet rigorous security and compliance standards. Datadog for Government previously achieved Moderate-Impact authorization through agency sponsorship. By pursuing FedRAMP High authorization, Datadog can better align with the federal government’s modernization and digital transformation initiatives, and prepare government IT leaders and engineers to leverage Datadog’s full-stack and unified platform for observability and security.
“Tool sprawl, siloed data and limited visibility across complex environments remain common problems for federal agencies,” said Yrieix Garnier, VP of Product at Datadog. “Datadog’s unified platform is uniquely positioned to solve these problems for organizations. Today’s announcement builds on our commitment to the U.S. public sector and is another milestone to providing the highest level of cloud security and observability for government agencies.”
Being "In Process" means that Datadog is actively working towards achieving full FedRAMP authorization at the High impact level, which requires stringent security controls for protecting highly sensitive data. This status signifies Datadog has completed a readiness assessment and is undergoing the process of gaining an Authority to Operate (ATO) from an agency sponsor. Datadog for Government is working to achieve FedRAMP High authorization in the second half of this year.
The Latest
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...