Datadog achieved Amazon Web Services (AWS) Migration & Modernization Competency status for AWS Partners.
This designation recognizes that Datadog has demonstrated technical proficiency and proven customer success automating and accelerating customer application migration and modernization journeys.
AWS launched the AWS Migration & Modernization Competency to allow customers to easily and confidently engage highly specialized AWS Partners that help AWS customers modernize their applications, either before or after they are moved to AWS. The AWS Migration & Modernization Competency takes on the heavy lifting of identifying and validating industry leaders with proven customer success and technical proficiency in migration and application modernization tooling.
Achieving the AWS Migration & Modernization Competency differentiates Datadog as an AWS Partner with deep domain expertise delivering software products that help customers embrace cloud and application transformation, reducing licensing costs, optimizing operational costs, and improving performance, agility, and resiliency. These tools can perform an application portfolio assessment, identifying the applications that are candidates for modernization; augment and automate developer tasks to carry out the modernization of legacy applications.
“Datadog is proud to achieve the new AWS Migration & Modernization Competency status,” said Ilan Rabinovitch, SVP, Product and Community at Datadog. “Our team is dedicated to helping companies achieve their business transformation goals by providing deep visibility into AWS, on-premises and hybrid environments during every phase of a cloud migration. This visibility enables organizations to move to the cloud with greater confidence.”
“The AWS Migration & Modernization Competency raises the bar again by choosing to solve the hardest challenge faced in application migration and modernization,” said Bill Platt, GM, AWS Migration Services. “I am confident that solutions, validated by the AWS Migration & Modernization Competency, will provide a complete portfolio of migration and modernization solutions to customers and partners.”
AWS is enabling scalable, flexible, and cost-effective solutions for organizations ranging from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Competency Program to help customers identify AWS Partners with deep industry experience and expertise.
Datadog’s cloud monitoring platform brings together infrastructure metrics, application traces, log data, and synthetic monitoring, allowing organizations to improve their agility to reduce their time to market, reduce risk during the modernization of their infrastructure and applications, reduce their operational and development costs, and enable visibility across the stack for all teams and stakeholders. Datadog supports a wide range of AWS services and is a member of the AWS Partner Network (APN).
The Latest
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...