AI Observability: Monitoring LLMs and RAG Pipelines in Kubernetes

Anyone operating traditional microservices knows: metrics, logs, and traces are the lifeline. However, conventional monitoring approaches hit their limits with AI workloads. A CPU utilization of 10% tells us nothing about whether the response quality of a language model is currently dropping or if the vector search is inefficient.

To productively operate an AI platform in medium-sized businesses, we need an expanded understanding of Observability that bridges the gap between infrastructure (GPU/K8s) and model performance (LLM).

The Three Levels of AI Observability

Complete visibility requires data from three different layers:

1. Infrastructure Metrics (The Foundation)

Before discussing AI logic, resources must be in place. Here, we use tried-and-true methods but with a specific focus.

• GPU Utilization: We use the NVIDIA DCGM Exporter to capture GPU temperature, power consumption, and VRAM usage in VictoriaMetrics.
• Bottleneck Analysis: Monitoring PCIe bandwidth and NVLink utilization is particularly important in distributed training. If data transfer bottlenecks, expensive GPUs sit idle.

2. Service & Middleware Tracing (The Pipeline)

In a RAG architecture (Retrieval Augmented Generation), the LLM is just one part of the chain. A slow response often stems from the vector database or the embedding service.

• Distributed Tracing: With OpenTelemetry, we trace a request through the API gateway, the embedding service, the vector DB (e.g., Qdrant/Milvus) to the final LLM call.
• Vector DB Performance: We monitor search latency and recall rate. If the search takes too long, user experience suffers before the model even generates a single token.

3. LLM-specific Metrics (The Intelligence)

Here, we depart from the traditional IT path. We need to understand what the model is actually doing.

• Token Usage: How many prompt and completion tokens are consumed? This forms the basis for internal FinOps reporting.
• TTFT (Time To First Token): The most important latency metric for generative AI. It determines how quickly the user sees a response.
• Model Evaluation & Drift: We track response quality. We use tools like Arize Phoenix or LangSmith to identify hallucinations or “drift” (the quality of responses deteriorates over time).

Architecture: Integration into the ayedo Stack

We do not build this observability as an isolated solution. Instead, we integrate it seamlessly into the existing Cloud-Native stack:

1. Collector: An OpenTelemetry Collector receives metrics and traces from AI workloads.
2. Storage: VictoriaMetrics stores high-resolution GPU data; Grafana Loki handles logging of model interactions (naturally in compliance with DSGVO/anonymization).
3. Visualization: Special Grafana dashboards correlate GPU load with token throughput. This way, we immediately see: “When we have 80% GPU load, our token throughput per second drops by 20%.”

Conclusion: Trust Through Visibility

AI in enterprises often fails due to a lack of trust in reliability. AI observability transforms the “black box” LLM into a measurable system. Only when you see how your models breathe can you scale them safely and operate them economically.

Technical FAQ: AI Monitoring

Should we log all LLM prompts and responses? Technically yes, but be cautious legally and cost-wise. We recommend a sampling method or logging metadata (token count, latency, sentiment score) and only logging full content in case of errors (after anonymizing sensitive data).

What’s more important: GPU load or token throughput? Definitely token throughput (tokens per second). A GPU can be 100% utilized while generating tokens very slowly (e.g., due to memory constraints). Token throughput is your primary “business metric.”

Can we use standard Prometheus for GPU metrics? Yes, the DCGM Exporter provides Prometheus-compatible formats. However, due to the high cardinality and frequency of the data (many metrics per GPU core), a performant storage like VictoriaMetrics is often more stable and cost-effective in long-term operation.