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This white paper introduces Dell’s Generative AI LLM-Serving Observability Framework, which is designed to ensure reliable, optimized LLM operations. It addresses key challenges in monitoring, performance, and ROI evaluation for enterprise AI maturity.

In today's AI-driven landscape, the reliability and performance of Large Language Model (LLM) services are more critical than ever. While many organizations are well on their way to some form of generative AI (GenAI) deployment and use case implementation, service availability, reliability, and optimization must be considered to gain true AI maturity.

In a recent study that Enterprise Strategy Group^[1] conducted, organizations cited model services, monitoring, and operations as stop gaps:

• Model maintenance and monitoring (31 percent)
• Evaluation and optimization of performance GenAI solutions (31 percent)
• LLM operations (28 percent)

Observability is a vital component for any GenAI-based solution, offering visibility into usage, quality, costs, feedback, latency, and more. Effective observability is a precursor to evaluating the ROI and value of GenAI solutions, requiring high-quality usage data, feedback, and a mix of manual and automated evaluation metrics.

As organizations increasingly rely on AI to drive business outcomes, the need for robust observability frameworks is paramount to address this reliance. Dell Technologies has developed a comprehensive GenAI LLM-Serving Observability Framework to monitor and maintain LLM performance in real-time, setting a new standard in the industry .

This white paper highlights the strategic benefits of our framework, including its key features, value in upholding service level agreements (SLAs), and robust root cause analysis capabilities.

The architecture and operational processes enable a future state in which the analytics engine drives continuous improvement through feedback loops, machine learning, and agents.

The following figure shows a functional view of our AI acceleration Platform and its various capabilities:

By using real-time monitoring, predictive analytics, and automated alerts, the framework provides unparalleled visibility and control over LLM operations. It plays a critical role in enhancing internal customer satisfaction, maintaining SLA compliance, and guaranteeing service availability for our highest-volume use cases.

The following figure describes the key components of the framework:

[1] Enterprise Strategy Group "Assessing AI Maturity: Measuring the State of the Market and What Organizations Stand to Gain by Advancing Their AI Sophistication", March 2025

The LLM Serving Observability Framework is equipped with several critical features essential for maintaining the robust operation of LLM services. Each feature has been meticulously designed to provide granular insights into system performance, enabling proactive management and prompt resolution of issues.

The framework employs real-time monitoring to track the health and performance of LLM services continuously. Using tools such as Grafana Dashboard, it provides detailed visualizations of service metrics, including:

• Request rates to enable you to understand the volume of incoming requests handled by the LLM services.
• Latency statistics to track the response time of services to ensure quick and efficient processing.
• Error rates to monitor the frequency of errors to identify and resolve issues promptly.

The following figure shows the Grafana dashboard:

Analyze the resource consumption to optimize infrastructure and avoid bottlenecks. This task includes monitoring:

The following figure shows the Grafana dashboard:

The following figure shows the Grafana dashboard:

The observability framework is integrated with an automated alerting system. When predefined thresholds are breached or anomalies are detected, alerts are instantaneously triggered, ensuring prompt notification and resolution.

The following figure shows a real-time, automated alert for a model capacity breach:

By integrating predictive analytics, the framework can forecast potential system failures or capacity bottlenecks. This proactive approach allows teams to undertake preventative measures, which enhance the reliability and uptime of their LLM services.

A standout feature of the framework is its exceptional root cause analysis capabilities. When an issue arises, the framework can dissect and analyze the problem across various dimensions, such as:

Model-source

Identification of whether the issue stems from a particular model

Client-specific interactions

Examination of specific clients to understand if the issue is isolated or widespread

Request patterns

Analysis of the patterns and sequences of requests leading up to the issue

Real-world scenario

Use in a real-world scenario, for example, Black Friday holiday shopping

During high-traffic events such as Black Friday and Cyber Monday holiday shopping, we can monitor key metrics, including energy consumption. For instance, tracking the energy use of specific model serving instances helps us understand the impact on overall performance. This comprehensive monitoring helps improve services and set up SLAs, enabling timely interventions.

The following figure shows an example of real-time monitoring reflecting platform status:

Implementing the framework, especially when integrated with a high-availability architecture (for example, a multi-data center setup), can lead to significant cost savings and improved operational efficiency. This combination ensures the continuity of services, translating into substantial financial benefits and a stronger competitive position.

Strategic benefits include:

Scalability and flexibility

The framework supports business growth and adapts to changing needs.

Customer satisfaction

Improved reliability and performance lead to higher customer satisfaction and retention.

The architecture of the LLM-Serving Observability Framework is designed for scalability, reliability, and ease of integration.

The architecture of the LLM-Serving Observability Framework is designed for scalability, reliability, and ease of integration. The following list provides a high-level overview of the architectural components and their interactions:

• The agent monitoring component is deployed on each server hosting LLM services to collect detailed metrics and logs.
• The metrics aggregator component is a centralized service that aggregates data from all monitoring agents for processing.
• The data storage component stores the aggregated metrics and logs, and typically employs time-series databases for metrics and NoSQL databases for log data.
• The alerting system component monitors real-time data against predefined thresholds and triggers alerts when anomalies are detected.
• The dashboard and visualization component uses Grafana and other visualization tools to present a comprehensive view of system health.
• In its future state, the analytics engine performs predictive analysis and root cause analysis using advanced machine learning algorithms.

  As we establish a feedback loop, we can identify patterns and adjust our alerting mechanisms to improve the time to resolution. We can:

  • Predict anomalies by analyzing historical data. The engine can forecast potential issues before they occur, allowing for proactive measures.
  • Identify root causes. Machine learning algorithms help identify the underlying causes of anomalies, facilitating quicker and more accurate resolutions.
  • Optimize alerting. Continuous feedback refines our alerting system, ensuring that alerts are timely and relevant, reducing false positives and improving response times.
  • Adapt to changes. The feedback loop enables the system to learn and adapt to new patterns and behaviors, maintaining its effectiveness over time.

By using this advanced analytics engine, we can enhance our ability to maintain optimal performance and efficiency, ensuring a robust and resilient infrastructure.

The following figure shows the LLM Serving Observability process flow:

LLM-serving observability is a rare paradox: it is critical to the optimization of generative AI systems and use case performance, yet it is incredibly challenging. Modern data estates, the foundation of most enterprise organizations, are sprawling and highly complex. Therefore, the generative AI LLM-Serving Observability Framework is a strategic asset for any organization using LLM services. By delivering real-time monitoring, automated alerts, predictive analytics, and advanced root cause analysis, it ensures the high availability, performance, and reliability of LLM services. This framework represents a pivotal advancement in the operational management of generative AI, offering substantial value to teams that are dedicated to maintaining stringent SLAs.

For further details about the deployment and monitoring processes, see the following appendixes.

To learn more about the GenAI LLM Serving Observability Framework, request a demo, or start a pilot project, contact the Dell AI Acceleration Team:

• Arnaud.de.Jaegher@Dell.com
• Travis.Cain@Dell.com
• Lukas.Cerveny@Dell.com
• Shamik.Kacker@Dell.com

Dell Technologies and the authors of this document welcome your feedback on this document and the information that it contains.

For feedback about this document, contact the Dell Technologies Solutions team by email.

This appendix provides examples of several use cases.

Examine specific clients to understand if an issue is isolated or widespread, as shown in the following figure:

During high-traffic events such as Black Friday and Cyber Monday holiday shopping, we monitor key metrics, including energy consumption. For instance, tracking the energy use of specific model serving instances helps us to understand the impact on overall performance.

The following figure shows that we also have views at the individual use-case level, in which we connect infrastructure performance to specific use cases by monitoring:

• GPU and CPU utilization metrics
• Speed, latency, and overall usage

  

This comprehensive monitoring helps us improve our services and set up SLAs, enabling timely interventions.

• Resource utilization levels
  Monitor resource use levels to analyze the resource consumption to optimize infrastructure and avoid bottlenecks, as shown in the following figure:

  

• GPU consumption

  Track the use of GPU resources to ensure that they are being used efficiently. Monitor the temperature of the GPU to detect any overheating issues early.

• CPU consumption

  Monitor CPU use to identify any processes that might be consuming excessive resources. This information helps to balance the load and prevent performance degradation.

• Energy consumption

  Measure the overall energy consumption of the system to identify areas in which energy efficiency can be improved. This measurement helps reduce operational costs and environmental impact.

• GPU temperature

  Regularly check the temperature of the GPU to identify any anomalies that can indicate potential hardware failures or inefficiencies. Maintaining optimal temperature levels is crucial for the longevity and performance of the GPU.

By closely monitoring these parameters, you can detect anomalies at a lower level and take proactive measures to maintain optimal performance and efficiency of your infrastructure.

The robust root cause analysis capabilities enable quick identification and resolution of issues. This efficiency in troubleshooting not only reduces the mean time to repair (MTTR) but also ensures that any underlying issues are thoroughly addressed, preventing recurrence.

The architecture of the LLM Serving Observability Framework is designed for scalability, reliability, and ease of integration. This appendix provides a high-level overview of the architectural components and their interactions.

This appendix provides examples of several use cases.

LLM-serving benchmarks are an essential component of quality control and root cause analysis (RCA) that monitor each step of the inferencing process. We can identify and address any changes in latency and time per operation (TPO) metrics. By locating the source of delays—whether they stem from load balancing, the model itself, the Gateway-orchestrator, or other components—we can effectively troubleshoot and resolve performance issues, as shown in the following figure:

To resolve performance issues, we have embedded mechanisms to track and log all calls. Performance issues are reflected through alerting thresholds visible on our real-time dashboards, as shown in the following figure: