What is AI Monitoring and Observability?

Full visibility into GPU utilization, inference performance, costs, and capacity across your AI infrastructure. Oaken AI provides ai monitoring and observability services for established businesses looking to implement AI that delivers measurable results.

Who needs ai monitoring and observability?

AI Monitoring and Observability is designed for established businesses — professional services firms, local businesses, agencies, and e-commerce companies — that want to save time and reduce manual work through AI automation. If your team spends hours on repetitive tasks each week, this service can help.

How long does ai monitoring and observability take to implement?

Oaken AI delivers working systems in as little as 7 days. We start with your highest-impact bottleneck and build a functional system before expanding to other areas. No multi-month assessments or slide decks — just results.

Do I need technical expertise for ai monitoring and observability?

No. Oaken AI handles the entire technical implementation. You do not need to hire an AI team, learn to code, or understand machine learning. We build systems your existing team can use and maintain.

AI Monitoring & Observability | GPU Metrics & Capacity Planning | Oaken AI

See Everything, Miss Nothing

AI infrastructure without observability is a black box. You cannot optimize what you cannot measure. GPU utilization, inference latency, token throughput, error rates, queue depth, and cost per query are the metrics that determine whether your AI infrastructure is delivering value or burning budget. We build monitoring stacks that give you real-time visibility and historical data for capacity planning, cost allocation, and performance optimization.

GPU Utilization Metrics

Real-time GPU compute utilization, memory usage, temperature, and power draw via NVIDIA DCGM. Detect underutilized GPUs (wasted budget) and overloaded GPUs (degraded performance) before users notice.

Inference Performance

Time-to-first-token, tokens-per-second, end-to-end latency percentiles (p50/p95/p99), and queue wait time. Track performance degradation over time and correlate with model updates or traffic changes.

Cost Attribution

Cost per query, cost per token, and cost per department or project. Allocate infrastructure costs to consuming teams. Identify expensive query patterns that could be optimized with caching or smaller models.

Capacity Planning

Historical usage trends extrapolated to predict when current capacity will be exhausted. Lead-time-aware procurement alerts ensure new hardware is ordered before capacity runs out.

Observability Stack

Collect

DCGM, Prometheus, OpenTelemetry

Store

Time-series DB and log aggregation

Visualize

Grafana dashboards and reports

Alert

PagerDuty, Slack, email notifications

Collect

DCGM, Prometheus, OpenTelemetry

Store

Time-series DB and log aggregation

Visualize

Grafana dashboards and reports

Alert

PagerDuty, Slack, email notifications

Monitoring & Observability Stack

Metrics Collection

Comprehensive observability requires metrics from multiple layers of the stack: hardware, inference engine, application, and business.

NVIDIA DCGM (Data Center GPU Manager). Collects GPU-level metrics: SM utilization, memory utilization, temperature, power draw, ECC errors, NVLink throughput, and PCIe bandwidth. Exports to Prometheus via dcgm-exporter. Essential for understanding whether your GPUs are the bottleneck or whether the constraint is elsewhere.

Inference engine metrics. vLLM, TGI, and Triton all expose Prometheus metrics: request count, latency histograms, batch sizes, KV cache utilization, and model load status. These metrics reveal whether the inference engine is efficiently using the available GPU compute.

Application-level telemetry. OpenTelemetry traces follow a request from the API gateway through authentication, routing, queuing, inference, and response delivery. Distributed tracing identifies which stage contributes the most latency. Structured logs with correlation IDs enable debugging individual requests.

Business metrics. Queries per user, queries per department, model selection distribution, and feature utilization. These metrics informcapacity planning and demonstrate AI adoption ROI to leadership.

Dashboards and Alerting

We build Grafana dashboards (or integrate with Datadog, New Relic, or your existing monitoring platform) that surface the metrics that matter for different audiences.

Operations dashboard. Real-time view of GPU utilization, inference latency, error rate, and queue depth. Red/yellow/green status indicators. Alert integration with PagerDuty, Opsgenie, or Slack for on-call notification.

Capacity planning dashboard. 30/60/90 day utilization trends with projected exhaustion dates. Cost forecast based on current growth rate. Procurement lead time indicators that trigger hardware ordering before capacity constraints.

Cost allocation dashboard. Per-department and per-project cost breakdown. Cost per query trends. Comparison of actual spend versus budget. Identifies optimization opportunities like caching, model downsizing, or batch scheduling.

Who This Is For

Monitoring and observability is essential for any organization running AI infrastructure in production. Without it, you are operating blind, unable to diagnose performance issues, plan capacity, or justify infrastructure investment to leadership.

AI Monitoring and Observability