Monitor an AI gateway by combining request and token counters, latency histograms, error classifications, traces, and structured logs. Track gateway time separately from provider time; for streaming, measure time to first token and the gaps between tokens. Then group the signals by provider, model, operation, request mode, and team so an aggregate change can lead to an individual request for diagnosis.
Choose what to measure at the gateway boundary
Instrument the point where a request enters the gateway and assign a request or trace identifier before it is sent upstream. This gives you a consistent way to connect a gateway observation with the provider call and its logs. Use bounded attributes that help explain differences without creating an unmanageable number of metric series.
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- Traffic and attribution: request count, provider, requested and response model when available, operation, route, consumer or team, environment, and streaming mode.
- Usage: input, output, and total tokens when reported by the gateway or provider. Keep token totals distinct from request counts: a change in tokens per request can affect capacity or cost even when traffic is steady.
- Timing: end-to-end gateway duration and upstream provider duration. For streaming, add time to first token (TTFT), inter-token latency (often called TPOT), and total stream duration.
- Failures: request and error counts, with an error classification and useful dimensions such as provider, model, operation, and request mode.
Kong’s AI metrics reference describes request latency, provider duration, token use, TTFT, TPOT, and error attributes, with dimensions for analysis. Its general Gateway OpenTelemetry reference documents additional metric configuration and version requirements: Kong AI Gateway OpenTelemetry metrics and Kong Gateway OpenTelemetry metrics.
Separate gateway latency from model latency
One end-to-end duration cannot tell you whether time was spent in gateway processing, provider processing, or delivery of a streamed response. Keep those measurements separate. A large gap between end-to-end duration and provider duration can point toward work or waiting elsewhere in the path; provider duration that rises in step with total latency points toward upstream processing.
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Use histograms and percentile views to see the slow tail rather than relying on an average. For streaming requests, TTFT tells you how long a user waits for the first token, while inter-token timing describes the pace after generation begins. Total stream duration captures the full response. These are distinct user and operational experiences, so do not substitute one for another.
Build dashboards around operational questions
Group dashboards by provider, model, operation, request mode, and consumer or team. Include request rate, token rate split by input and output where available, error rate, latency percentiles, streaming TTFT and inter-token timing, and cost only where usage and applicable rates support it. Cloudflare’s AI Gateway analytics documentation describes request, token, cost, error, cache, time-filtering, and GraphQL analytics: Cloudflare AI Gateway analytics.
Design each view to answer a decision, not merely to display a metric:
- Are requests or tokens rising, and which model, provider, operation, or team accounts for the change?
- Are errors concentrated on a provider, model, operation, or streaming path?
- Did provider time, gateway time, TTFT, or the slow latency tail change?
- Are token use or estimated costs changing independently of request volume?
Alert on sustained error-rate increases, tail-latency regressions, or unusual token and spend patterns. Set thresholds from your own service objectives and baseline; the cited product documentation does not establish a universal threshold. Google Cloud’s API Gateway guide demonstrates views for traffic, latency, and errors and a log query for requests exceeding a chosen latency value; its 300-millisecond example is a query illustration, not a general target: Google Cloud API Gateway monitoring.
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Use metrics, traces, and logs together
Metrics show aggregate rates and distributions; traces show the path and timing of an individual request; logs preserve request-level event details and classifications. When a dashboard shows a spike, pivot from the affected metric dimensions to a representative trace, then inspect its associated structured log and gateway or provider response classification.
- Find the time window and dimensions where the rate, latency, or usage changed.
- Choose a representative request using its trace or request identifier.
- Inspect the trace to locate time spent in the gateway and upstream provider, including streaming timing when available.
- Use the associated log to understand the event, error type, and response details that explain the trace.
Cloudflare documents exporting AI request spans through its OpenTelemetry integration, including request, model, provider, token, cost, and custom metadata: Cloudflare AI Gateway OpenTelemetry integration. AWS API Gateway describes using CloudWatch metrics and logs with X-Ray traces, and notes that some API Gateway error classes and test invocations may not produce normal logs or metrics. Check the documented logging behavior before treating the absence of a record as proof that no request or failure occurred: AWS API Gateway REST API monitoring.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Export telemetry securely and verify its completeness
Use the gateway’s supported metrics or trace interface to export to an OTLP-compatible collector or monitoring backend. If the product exposes Prometheus metrics, configure scraping as its documentation specifies. Protect telemetry endpoints with network controls or authentication; Kong cautions that its data-plane metrics endpoint is not generally exposed publicly by default and should usually remain protected. Its monitoring guide covers Prometheus metrics and logging for token, cost, cache, and MCP visibility: Kong: Monitor AI LLM metrics.
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Validate that each path actually emits the fields your dashboards depend on. Token counts may be absent for some streaming or passthrough responses, and product configuration may determine whether latency or error attributes are populated. Treat cost and token totals as estimates if some requests lack usage data or model rates: a missing count is not zero consumption.
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Preview and version status also matter. Kong labels its AI OpenTelemetry metrics Tech Preview and says not to use that feature in production; its documentation lists required flags and version support. Azure API Management’s AI Gateway tier is in preview: its documented OTLP export currently covers token usage only, while MCP request volume, latency, and errors are available in portal views with Application Insights. Confirm current coverage and maturity against the relevant product documentation before making an operational dependency: Microsoft Azure API Management AI Gateway tier.
Compare implementations by coverage, not just export format
Before choosing a gateway telemetry path, check whether it provides the signals and attribution your operators need, whether it can export to your backend, and whether the data is complete for streaming and passthrough traffic.
| Option | Documented coverage | Export and maturity | Important qualification |
|---|---|---|---|
| Kong AI Gateway OpenTelemetry metrics | Request and provider latency, token usage, TTFT/TPOT, costs, and error-related attributes | OTLP metrics; Tech Preview | Kong says not to use this AI metrics feature in production; follow its documented version, flags, and attribute requirements. |
| Cloudflare AI Gateway | Analytics for requests, tokens, costs, errors, cache; OTLP spans with request and model/provider metadata | Analytics and OpenTelemetry integration are documented | Confirm which fields are present for the specific request path and provider. |
| Azure API Management AI Gateway tier | Preview OTLP metric for token use; MCP request volume, latency, and errors in portal views with Application Insights | OTLP export; preview | The documented OTLP export currently covers token usage only. |
Product capabilities change, especially for preview features and provider usage reporting. Recheck the linked documentation for current version support, metric configuration, privacy controls, payload capture, endpoint protection, and retention before relying on a particular signal.
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