Operations Guide
AI Observability Platform Guide (2026) - Monitoring Architecture
AI observability requires comprehensive monitoring: collect metrics (latency, cost, errors), aggregate logs (request/response traces), trace requests end-to-end, and design actionable dashboards.
Direct answer
AI observability requires comprehensive monitoring: collect metrics (latency, cost, errors), aggregate logs (request/response traces), trace requests end-to-end, and design actionable dashboards.
Fast path
- Metrics collection: latency (p50/p99), error rates, token usage, cost per request.
- Log aggregation: request/response traces, model decisions, error context.
- Distributed tracing: track AI requests across services, identify bottlenecks.
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Implementation Steps
- Metrics collection: latency (p50/p99), error rates, token usage, cost per request.
- Log aggregation: request/response traces, model decisions, error context.
- Distributed tracing: track AI requests across services, identify bottlenecks.
- Dashboard design: real-time metrics, historical trends, alert thresholds.
- Alerting: configure alerts for latency spike, error rate, cost anomaly.
Frequently Asked Questions
What metrics for AI observability?
AI observability metrics: latency (p50/p99 response time), error rate (4xx/5xx percentage), throughput (requests/sec), token usage (input/output counts), cost per request, model utilization, and queue depth.
How to trace AI requests?
Trace AI requests: assign unique request ID, log at each processing step, track latency per stage, capture model/provider, record input/output summary. Use distributed tracing tools (OpenTelemetry, Jaeger) for end-to-end visibility.
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- Provider and model split recommendations
- Budget guardrail design by traffic stage
- KPI plan for spend, quality, and conversion