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End-to-end network observability for AI workloads

AI-driven technologies require new practices after they're integrated into networks. For AI to run properly, networks must have full network observability.

Verlaine Muhungu
By
Published: 22 Aug 2025

This new era of AI is marked by rapid innovation and intense competition. AI's swift integration into enterprise and research settings introduces a unique set of networking requirements.

The future of AI innovation relies on networks that are smart, responsive and easy to see from start to finish. Tech giants and other businesses are all vying for success with ambitious projects, such as the United Arab Emirates' Stargate and xAI's Colossus, slated to be the world's largest AI supercomputer.

As infrastructures expand, network engineers must adopt new methodologies to meet evolving demands. These include everything from machine learning (ML) model training to real-time inference. These operations generate enormous volumes of data and need ultralow latency. They rely heavily on seamless data movement across diverse and distributed computing environments.

AI-driven systems also create new challenges for network administrators. Complexities such as GPU bottlenecks, multi-cloud deployments and unpredictable east-west traffic patterns demand a modern, more comprehensive approach to network visibility.

Using modern observability tools, implementing advanced monitoring and following best practices help organizations run AI smoothly and safely at a large scale. Full network visibility is no longer a nice-to-have -- it's a must for building next-generation smart systems in a competitive tech industry where user demands affect network infrastructures.

End-to-end observability architecture

To meet AI's growing demands, network teams are scaling infrastructures daily through massive initiatives. Addressing AI's complex challenges requires a holistic approach to achieve true end-to-end observability.

The best observability setups connect every layer to give real-time clarity on how AI systems perform, stay secure and run at peak health. This architecture must cover all critical AI processing layers, including data centers, public and private cloud environments, and edge computing nodes.

Ensure the data center can handle AI workloads. Low-latency connectivity between compute nodes and storage systems is crucial for AI workloads. Observability tools must be capable of detecting congestion, latency spikes and packet loss in real time. Some tools assist network teams in maintaining performance and visibility in complex AI environments.

Modern AI workloads rely heavily on cloud-native services, containers and orchestration platforms. To support this, observability must provide deep insights into service-to-service communication, virtual networking and cloud-native telemetry.

Finally, edge observability is essential. It must monitor device performance and the network path to the core. This ensures efficient communication between edge devices and centralized systems.

Monitoring tools for AI networks

A plethora of tools and technologies can meet the visibility demands of AI networks. These tools offer capabilities beyond traditional monitoring, including the following:

  • Streaming telemetry. Streaming network telemetry provides real data frequency from networks, which enables faster anomaly detection.
  • AI-enhanced network analytics. Data is crucial when using ML to detect patterns and predict performance issues based on historical data and real-time metrics.
  • Packet brokers and deep packet inspection. These technologies examine and analyze AI-specific patterns to identify bottlenecks and performance degradation.
  • Integration with network management systems. Observability platforms must seamlessly integrate with existing network management and orchestration tools to centralize visibility and accelerate troubleshooting.

The following is a nonexhaustive list of network observability tools that monitor AI workloads. The tools and platforms highlighted in this article were selected based on vendor documentation, hands-on experience with enterprise monitoring tools and recent industry reports.

The selection criteria emphasized monitoring AI workloads across network, application and infrastructure layers, with a particular focus on GPU usage, low-latency performance, and hybrid or multi-cloud environments.

Tool or platform Function Use case AI relevance
AppDynamics Application performance monitoring Tracks performance across distributed microservices Monitors AI apps built on containerized, cloud-native stacks
Cisco Nexus Dashboard Insights Telemetry and analytics for data center networks Provides proactive alerts and anomaly detection for Application Centric Infrastructure environments Ensures consistent low-latency networking between compute and storage nodes
Cisco ThousandEyes

Network performance monitoring and internet visibility

 Detects latency, packet loss and outages across hybrid/multi-cloud networks Critical for real-time AI workload delivery over complex infrastructures
Elastic Stack (ELK) Log analytics and search Log collection and troubleshooting across systems Helps detect AI pipeline failures, model errors or infrastructure events
Grafana Visualization of monitoring data Dashboards for network, service and infrastructure telemetry Custom dashboards for monitoring GPU, service latency and AI model performance
OpenTelemetry Telemetry data collection (traces, metrics, logs) Observability framework for microservices Captures end-to-end flow of AI inferences and service-to-service calls

Prometheus

 Open source metrics monitoring and alerting Ideal for Kubernetes-based AI workloads Offers visibility into container metrics, GPU usage, and memory and CPU thresholds

Best practices for end-to-end visibility in AI networks

A clear strategy is necessary for implementing strong network observability for AI workloads. The following are key best practices to guide AI deployment:

  • Deploy observability at multiple layers. To gain a comprehensive understanding of AI traffic, it's essential to monitor not only the network layer, but also the application and transport layers.
  • Baseline and benchmark performance. Establishing performance benchmarks for AI workloads is crucial to identify deviations and optimize routing or resource allocation. This ultimately leads to improved management.
  • Automate alerting and remediation. Networks experience numerous events. Using AI-driven alerts and automation facilitates rapid responses without human intervention.
  • Embrace open standards and APIs. It is essential to choose tools that support OpenTelemetry standards. These tools should offer flexible APIs to simplify integration and ensure long-term scalability.

Verlaine Muhungu is a self-taught tech enthusiast, DevNet advocate and aspiring Cisco Press author, focused on network automation, penetration testing and secure coding practices. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition.

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