Dynatrace integrates natively with Red Hat OpenShift to provide deep, automatic observability across containers, Kubernetes, and hybrid cloud workloads. Together they deliver enterprise‑grade platform operations with AI‑driven insights that improve application reliability, performance, and DevOps velocity.

Dynatrace integrates with NVIDIA GPUs and AI infrastructure to provide real‑time observability for accelerated computing and AI workloads. The joint value is end‑to‑end visibility from GPU resources to applications, enabling faster troubleshooting and optimized performance of AI‑driven systems.

Dynatrace integrates deeply with AWS services (EC2, EKS, Lambda, RDS, and more) using automatic discovery, the Dynatrace AWS DevOps agent, and native cloud integrations. Together they provide continuous, AI‑driven observability across the full CI/CD and runtime lifecycle, enabling faster deployments, proactive issue detection, and reliable cloud‑native operations at scale.

Dynatrace integrates natively with Microsoft Azure services such as AKS, App Service, SQL, and Monitor APIs to provide AI‑powered observability at scale. Together they help customers modernize applications on Azure while maintaining performance, reliability, and cost efficiency.

Dynatrace integrates with Google Cloud services including GKE, Compute Engine, and Cloud Run to deliver automatic, real‑time observability. The combined value enables teams to confidently run cloud‑native and microservices architectures with precise performance insights and automated root‑cause analysis.

Dynatrace integrates with Tanium by combining real‑time endpoint data with application and infrastructure observability. Together they bridge the gap between IT operations and endpoint management, improving security posture, incident response, and operational resilience.

Nutanix and Dynatrace together provide end‑to‑end visibility and control for enterprise AI and cloud‑native workloads, from virtual infrastructure and GPUs through Kubernetes platforms to AI/LLM services. By correlating application, model, cost, and infrastructure signals in real time, organizations can simplify operations, accelerate root‑cause resolution, and confidently deliver reliable, cost‑efficient AI services across private and hybrid cloud environments.