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Native Support for AI Infrastructure Monitoring

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.

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Key Components to Monitor:

Vector Databases (e.g. Pinecone, Weaviate)

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. 

Model Inference Services

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.

RAG Pipelines

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. 

Benefits

Accelerated Root-Cause Analysis

Identify bottlenecks in your AI pipeline in seconds, not hours.

Proactive Alerting

Be notified before an inference endpoint slows down or a training job crashes.

Optimized Resource Allocation

Make data-driven decisions about GPU provisioning and model deployment scaling.

Enhanced Collaboration

Correlate model performance with infrastructure logs and metrics for better cross-team visibility.

Complete Observability for Modern AI Workloads

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.

Key Capabilities Built for AI Systems

GPU & Accelerator Monitoring

Gain deep visibility into:

 

  • GPU utilization, memory bandwidth, power draw, and thermal state
  • TPU and custom AI accelerator metrics (e.g., NVIDIA A100, AMD MI300, Google TPU)
  • Job-level and container-level GPU resource usage

Integrated with NVIDIA DCGM, Prometheus exporters, and Kubernetes GPU schedulers for real-time collection. 

End-to-End ML Pipeline Observability

Monitor each stage of your machine learning pipeline: 

 

  • Data ingestion & preprocessing
  • Model training & tuning (CPU/GPU/memory usage, training duration, checkpoint failures)
  • Model validation & A/B testing (accuracy, precision, drift detection)
  • Model deployment & inference (latency, error rate, QPS)

Perviewsis connects seamlessly with tools like Kubeflow, MLflow, Airflow, TensorBoard, and KServe, giving you detailed logs, metrics, and traces for each step.

Model-Centric Metrics & Alerting

Track AI-specific KPIs:

  • Model loss, accuracy, F1 score
  • Inference latency and throughput
  • Model versioning and performance by version
  • Real-time drift detection (input feature drift, prediction drift)
  • Confidence score anomalies

Set alerts not only on infrastructure thresholds but also on data and model performance thresholds.

Multi-Cloud and Hybrid Ready

Monitor AI workloads wherever they run:

  • Kubernetes clusters (EKS, AKS, GKE, OpenShift)
  • On-prem GPU clusters (NVIDIA DGX, Supermicro)
  • Cloud-based AI platforms (SageMaker, Vertex AI, Azure ML)
  • Edge AI devices (Jetson, Coral, custom inference hardware)

Native integrations and agents make Perviewsis portable across environments with no vendor lock-in.

AI Infrastructure Monitoring in Action

Example use cases:

Troubleshooting training failures

Automatically detect when a model fails to converge due to resource limits or corrupted data.

Optimizing inference at scale

Visualize endpoint latency and GPU saturation across multiple edge locations to fine-tune autoscaling.

Detecting drift in real-time

Correlate changes in input data distribution with drops in model performance and trigger retraining pipelines.

Benefits to AI and MLOps Teams

Faster Time to Resolution

Drill down from an inference error to GPU logs, container metrics, and pipeline execution history—all in one platform.

Lower Operational Costs

Identify underutilized GPUs or over-provisioned nodes and optimize usage.

Improved Model Reliability

Ensure production models are running on healthy infrastructure with stable inputs and outputs.

Built-in Compliance

Track audit trails of model changes, data versions, and deployment history to support AI governance and regulatory requirements.

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