What Are the 4 Pillars of AI Observability?
AI observability encompasses four key areas essential for managing enterprise AI workloads:
- Observing AI-powered systems and applications
- Monitoring the infrastructure that supports training and inference
- Tracking model performance in real time
- Ensuring responsible AI practices through data privacy, compliance, and security monitoring
These workloads add complexity to IT environments and require observability tools to address the specific demands of AI systems. Unlike traditional observability, AI observability extends to challenges such as tracking training costs, monitoring data and model security, and managing the inherent unpredictability of non-deterministic model outcomes. As organizations scale their AI efforts, these complexities make observability a critical enabler for ensuring operational efficiency.
Case Study: Training an LLM
The process of training LLMs reveals the unique demands of AI infrastructure observability. It involves thousands of GPUs interconnected by high-speed networks, running workloads that generate massive data flows and unpredictable resource spikes. This necessitates real-time monitoring of GPU utilization across up to 16,000 GPUs. Frequent "checkpoints" of model state can create sudden surges in storage throughput, pushing systems to handle bursts of up to 7 terabytes per second. Network performance must be optimized to avoid bottlenecks caused by "fat flows" in data communication.
While most enterprises may not need workloads at this scale, the underlying challenges are just as relevant for training custom AI models at varied scales and training times. AI workloads demand specialized observability tools capable of handling unique requirements such as:
- GPU observability to track utilization efficiency, memory bandwidth, and thermal performance
- Network observability to monitor high-speed interconnects, detect bottlenecks in distributed communication, and help ensure balanced data flows
These issues highlight how AI observability extends beyond traditional observability. Unlike standard infrastructure, AI workloads require insights into specialized hardware, dynamic resource allocation, and performance-critical interconnects. Adapting to these specifications has become central to successfully scaling AI initiatives. Let’s take a look at the four pillars of AI observability.
Pillar 1: AI Application Observability
AI Application Observability extends traditional APM capabilities to meet the unique demands of AI workloads. This includes tracking token usage, model versioning, parameters, tool, and vector database interactions. Also important is tracing LLM chains and workflows, elements that together form the foundation of Compound AI Observability. Feedback observability plays a critical role, analogous to RUM in traditional applications, by monitoring metrics such as chatbot response latency, abandonment rates, and explicit user feedback, along with implicit signals like questions and edits.
Open-source frameworks like OpenLLMetry and OpenLIT, built on OpenTelemetry (OTEL), are emerging as foundational approaches for observing AI systems. These frameworks extend OTEL’s capabilities to handle LLM-specific metrics, cost tracking, GPU monitoring, and prompt versioning, offering a unified framework for AI observability. As enterprises adopt such tools to monitor their AI workloads, modern APM and AI observability solutions must evolve to align with these standards.
Pillar 2: AI Infrastructure Observability
AI Infrastructure Observability expands traditional infrastructure monitoring to include GPU performance, along with other specialized needs for efficient training and inference workloads. While traditional infrastructure monitoring captures standard metrics like latency and throughput, the scale, complexity, and hardware requirements of AI workloads introduce new complexities. GPU Monitoring focuses on the performance of GPUs and their ecosystems, with specific considerations for on-premises and cloud environments:
- Utilization and efficiency: Tracking GPU usage to identify underutilization and optimize workload distribution
- Memory usage: Monitoring memory allocation to prevent overflows that could crash applications or degrade performance
- PCIe throughput: Track data transfer rates to detect and resolve bottlenecks between GPUs and system components
- Thermal performance and power consumption: Monitoring temperatures and energy usage to ensure GPUs operate within safe limits and maintain optimal performance without overheating
Network Monitoring helps ensure that the high-speed interconnects and data flows required for distributed AI systems are efficient and balanced. This includes:
These combined capabilities provide visibility into the infrastructure supporting AI workloads.
Pillar 3: Responsible AI Observability
Responsible AI Observability helps see to it that AI systems are deployed ethically, responsibly, and in compliance with regulatory standards. This approach can be broadly categorized into key areas:
- Data privacy and security: Monitoring the usage of personally identifiable information, enforcing input and output guardrails, and helping to safeguard sensitive information
- Model misuse: Detecting prompt injection attacks, identifying unauthorized usage patterns, and ensuring outputs adhere to ethical boundaries
- Auditability: Capturing detailed logs of model inputs, outputs, and decision flows to enable traceability and transparency during audits or post-incident investigations
- Compliance observability: Translating regulatory policies into measurable observability signals, helping to ensure adherence to evolving laws like the GDPR or the EU AI Act
Responsible AI Observability also accelerates AI adoption by supporting users to address critical challenges such as bias, misuse, and privacy risks. Proactive monitoring helps adopters to simplify audits, build stakeholder confidence, and support the responsible scaling of AI systems. Continuous compliance observability further supports enterprises in adapting to evolving regulations and maintaining ethical AI practices at scale.
Pillar 4: Model Performance Observability
Model Performance Observability helps ensure AI models deliver expected outcomes across their lifecycles. This involves monitoring during training, where metrics like accuracy, drift, and data quality help identify when adjustments are needed. Training-time observability also tracks hyperparameter optimization and resource usage, ensuring seamless transitions to production. Inference-time observability focuses on evaluating the algorithm's quality in real-world scenarios. Metrics like prediction accuracy, confidence scores, and fairness thresholds are critical to assessing how well a model meets its intended goals. Real-time monitoring of these metrics helps detect issues like biased predictions, deviations from expected behavior, or declining performance over time.
For high-stakes applications such as healthcare and finance, understanding the rationale behind a model's predictions is critical. Integrating observability with explainability frameworks enhances transparency, helping ensure fairness and accountability in decision-making.
The Future of AI Observability
AI observability holds immense potential for organizations seeking to navigate the complexities of AI systems. By developing tools to monitor data privacy, detect model misuse, and ensure compliance with evolving regulations, businesses can feel confident that they are deploying AI technologies as effectively as possible.
