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Dynatrace delivers full-stack observability for AI with NVIDIA Blackwell and NVIDIA NIM

The Dynatrace® unified, AI-powered observability platform delivers full-stack AI and LLM observability, including of NVIDIA Blackwell and NVIDIA NIM systems, and AI-driven insights to meet the scale and complexity of enterprise AI deployments. This provides enterprises with all the insights needed to detect customer-facing issues, helping IT teams maintain performance, reliability, and security across their AI workloads.

NVIDIA Blackwell systems provide high-performance infrastructure for enterprise AI, and now, thanks to the Dynatrace integration with the NVIDIA Enterprise AI Factory reference design, enterprises can add Dynatrace Full-Stack Observability to NVIDIA Blackwell infrastructure. This magnifies the value of the NVIDIA Blackwell platform by providing real-time performance insights, anomaly detection, and dependency mapping.

Keep high performance and security top of mind with unified observability and security

Figure 1. The Dynatrace AI Observability platform
Figure 1. The Dynatrace AI Observability platform

Dynatrace aligns with high data security and privacy standards typical of on-premises NVIDIA Blackwell deployments, particularly in regulated industries such as finance and healthcare. Its unified data model, Smartscape® topology mapping, and Davis® AI engine provide deep visibility into the full stack—from GPU metrics and containerized workloads to distributed applications and user experiences, enabling tailored observability for workloads running on  NVIDIA Blackwell. Integrating NVIDIA Data Center GPU Manager or other telemetry sources is straightforward, allowing teams to monitor GPU health, utilization, thermal thresholds, and memory bandwidth alongside traditional infrastructure metrics.

Dynatrace technology allows for automated discovery and instrumentation of services running on NVIDIA Blackwell-accelerated systems. Whether monitoring high-throughput GPU compute tasks, Kubernetes clusters, or microservices, Dynatrace ensures low-overhead performance monitoring with minimal manual configuration.

AI-powered, real-time insights improve performance and explainability

With Dynatrace Full-Stack AI Observability, you can monitor real-time performance, trace prompts end-to-end, and ensure compliance, optimizing cost and throughput for your AI and LLM workflows and agents, offering various use cases such as

  • Monitor service health and performance, tracking real-time metrics and offering clear visibility into service incidents.
  • Validate service quality by measuring response speed or identifying performance hotspots.
  • End-to-end tracing and debugging pinpoint the root cause of errors and failures in the LLM chain, troubleshoot issues in complex pipelines, and trace dependencies across the entire system spanning multiple LLMs, RAG pipelines, and agentic frameworks.
Figure 2. Sample dashboards provided for tracking service health and performance
Figure 2. Sample dashboards are provided for tracking service health and performance

Unified AI-powered observability

Dynatrace delivers full stack observability for your LLMs and Generative AI applications running on NVIDIA Blackwell systems. Its ability to provide visibility into complex, high-performance environments allows enterprises to fully leverage Blackwell’s capabilities while maintaining operational excellence and system reliability, improving the performance, explainability, and compliance of your AI workloads and agents.

Figure 3. Dig deeper into the possibilities of AI and LLM observability on the Dynatrace Playground
Figure 3. Dig deeper into the possibilities of AI and LLM observability on the Dynatrace Playground

Visit the Dynatrace Playground to learn more and gain hands-on experience with prepopulated data, so you can experience the possibilities of AI and LLM observability with Dynatrace. If you’re interested in using Dynatrace for your own AI workloads, visit our documentation and start benefiting from full stack observability for AI and LLM.

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