Traditional monitoring tools need first-class support for AI workloads. This means natively ingesting and visualizing metrics from AIspecific components – e.g. vector databases, model inference services, and RAG (Retrieval-Augmented Generation) pipelines – alongside standard infrastructure metrics. The platform should also track semantic performance indicators unique to AI, such as hallucination rates, bias/fairness metrics, and model drift over time.
These store embeddings for semantic search. Monitoring should capture index
sizes, vector insertion rates, query latencies, and memory usage. For example, Datadog’s Pinecone integration provides out-of-the-box dashboards showing index operations per second and vector search durations.
Whether using hosted LLM APIs (OpenAI,
Azure OpenAI, etc.) or custom models, SRE tools must measure model-specific KPIs. This includes request throughput, inference latency, GPU utilization, and token usage per request. Datadog’s LLM Observability feature, for instance, traces entire LLM call chains and tracks each prompt’s latency, errors, and token counts.
In retrieval-augmented generation systems, the observability platform should trace end-to-end flows: from retrieval calls (to vector DB or search) through the LLM’s answer generation. Monitoring chain latency and retrieval relevance is crucial.
Identify bottlenecks in your AI pipeline in seconds, not hours.
Be notified before an inference endpoint slows down or a training job crashes.
Make data-driven decisions about GPU provisioning and model deployment scaling.
AI and machine learning workloads operate differently from traditional applications. They are compute-intensive, GPU-dependent, pipeline-driven, and sensitive to data and model drift. Perviewsis delivers end-to-end native observability specifically engineered for AI infrastructure—ensuring consistent model performance, operational uptime, and resource efficiency.
Gain deep visibility into:
Integrated with NVIDIA DCGM, Prometheus exporters, and Kubernetes GPU schedulers for real-time collection.
Monitor each stage of your machine learning pipeline:
Perviewsis connects seamlessly with tools like Kubeflow, MLflow, Airflow, TensorBoard, and KServe, giving you detailed logs, metrics, and traces for each step.
Track AI-specific KPIs:
Set alerts not only on infrastructure thresholds but also on data and model performance thresholds.
Monitor AI workloads wherever they run:
Native integrations and agents make Perviewsis portable across environments with no vendor lock-in.
Automatically detect when a model fails to converge due to resource limits or corrupted data.
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