As enterprises integrate AI applications, optimizing network infrastructure becomes essential. While many organizations are focused on enhancing their data center networks and accelerating AI traffic across the WAN, an often-overlooked factor is network observability.
Research from Enterprise Management Associates (EMA) reveals that only 47% of enterprises feel their network observability tools are adequately equipped for monitoring AI traffic. This stat should concern any leader involved in AI project management. AI workloads can be particularly sensitive to latency, packet loss, and congestion, generating unpredictable traffic patterns and demanding reliable connectivity across various environments. Without real-time insight into network performance, AI tasks may fail.
The Importance of Network Observability for AI
EMA’s research encompassing 250 IT professionals highlights that organizations with well-prepared observability tools are five times more likely to achieve success in their AI networking strategies. Such companies typically have:
- An AI center of excellence that directs strategy
- Significant budgets allocated for AI development
- Less anxiety over compliance and privacy concerns
Therefore, investing in observability isn’t merely a technical enhancement; it acts as a crucial indicator of strategic effectiveness.
Focus Areas for Visibility
AI workloads are commonly distributed across hybrid architectures—spanning private data centers, public clouds, and edge computing platforms. EMA emphasizes that end-to-end network observability is vital for managing AI networks. The top priority among network teams is improving visibility in public cloud environments and the interconnects that link enterprise networks to cloud providers. Many enterprises are also leveraging emerging GPU-as-a-service providers, which may pose additional visibility challenges due to less mature observability capabilities.
As underscored by the research, enhancing visibility in data center network fabrics and WAN edge connectivity services is equally critical.
Real-Time Data Monitoring Needs
Achieving thorough observability requires many companies to enhance how they collect network data. Currently, most observability tools utilize SNMP polling to gather metrics at intervals that typically last five minutes. However, many survey participants voiced the need for real-time monitoring—69% believe SNMP’s capabilities are insufficient for AI networks.
Real-time telemetry can address visibility gaps, as AI traffic bursts that lead to congestion may last only seconds, a duration that five-minute polling could overlook. To attain precise metrics, organizations will need to move toward adopting streaming network telemetry, though support for this technology remains inconsistent among infrastructure vendors.
Advanced Analytics for AI
Network teams also require their observability tools to intelligently analyze AI network traffic. Nearly 59% of participants expressed interest in tools that can identify AI applications within network data, allowing them to monitor performance, optimize traffic, and detect unauthorized AI usage.
Furthermore, 46% desire advanced analytic capabilities that can forecast traffic congestion related to AI applications, while 42% want anomaly detection tailored to AI traffic patterns. Lastly, 34% seek tools capable of analyzing traffic across GPU clusters. Such functionalities are critical for preempting issues before they hinder AI application performance in scenarios where even milliseconds can be decisive.
The Necessity of Phenomenal Observability
As AI reshapes network management, securing investments in real-time and intelligent network observability will be pivotal. As complexities and demands for AI workloads grow, effective observability will separate successful implementations from failures.
For further insights on AI networking, refer to the following resources:
