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Monitor an AI gateway by counting requests and token usage, measuring gateway and provider latency separately, and recording errors with dimensions that let you trace an individual request. Use metrics to spot aggregate changes, traces to inspect request paths, and logs to examine event details. The exact signals and export options depend on the gateway, its version, and whether its telemetry features are generally available.
Decide what each request should tell you
Choose the request boundary first: identify when a request enters the gateway and when its response or stream finishes. Propagate a request or trace identifier across the gateway and, where supported, its provider call. Use that shared identifier to connect aggregate measurements with the corresponding trace and structured log.
Attach bounded, useful attributes rather than arbitrary user-supplied values. Common breakdowns include provider, requested or returned model, operation, route, consumer or team, environment, and whether the response is streaming. Check what your gateway actually supports: not every integration reports every dimension or preserves token data on every request path.
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Count requests and failures
Record total requests and failures as counters. Slice them by available attributes such as provider, model, operation, and request mode. A failure rate is meaningful only when its numerator and denominator use consistent request definitions; decide, for example, whether a client disconnect or a rejected request counts as a gateway failure.
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Track input and output tokens
When the gateway or provider supplies usage data, record input, output, and total tokens separately. Break down usage by model, provider, operation, and consumer or team where supported. Separate request counts from token counts: request volume shows traffic, while tokens better reflect the amount of model work and can support cost attribution.
Token totals and cost calculations are not necessarily complete. Some provider paths may omit token counts, especially for streaming or passthrough responses. Cost reporting also depends on the gateway having the relevant model input and output rates configured. Treat resulting totals as estimates when usage data is missing or pricing configuration is incomplete.
Separate gateway latency from model latency
Record end-to-end and provider duration
Use duration histograms for the full gateway request and for the upstream provider call. The difference can help identify time spent outside provider processing, such as gateway-side work or network delay, but it does not by itself identify the cause. Keep the two measurements distinct rather than reporting provider time as if it were total user-visible latency.
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Add streaming measurements
For streaming responses, measure time to first token (TTFT), inter-token latency (also called time per output token, or TPOT), and total stream duration. These answer different operational questions: TTFT describes how long a user waits for the first output; inter-token latency describes the pace of subsequent output; total duration captures how long the full response takes.
Review latency percentiles or histogram distributions, not only averages. Averages can obscure a slow tail that affects a smaller but important share of requests. Choose percentile views and alert thresholds to match your service objectives and observed baseline; the documentation examples do not establish a universal latency target.
Make errors useful for diagnosis
Capture error counts and classifications, then group them by the dimensions that help distinguish a provider incident from a gateway or workload issue. Provider, model, operation, and streaming mode are useful starting points. Preserve the gateway’s error type or equivalent classification as a structured attribute where available.
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When an error rate or latency distribution changes, use the affected metric slice to find representative requests. Follow the trace to see the request path and timing, then inspect its structured log and gateway or provider response classification. Metrics show whether a problem is widespread; traces and logs help explain what happened to individual requests.
Export and visualize the signals
Choose an export path your gateway supports
Export metrics and traces through the gateway’s supported interfaces to an OTLP-compatible collector, a monitoring backend, or a platform-native service. If using Prometheus exposition, configure scraping according to the product’s documentation. Protect telemetry endpoints with network controls or authentication: a metrics endpoint can expose operational details, and should not be made public without a deliberate security decision.
Build dashboards around request rate, token rate, error rate, latency distributions, TTFT, and cost where usage and rate data are available. Group by provider, model, operation, request mode, and consumer or team when those attributes are present. Google Cloud’s API Gateway documentation demonstrates monitoring traffic, latency, and errors and includes a sample log filter for requests above 300 milliseconds; that filter is an example, not a general performance threshold.
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Use each signal for its strength
- Metrics: aggregate rates, counts, and latency or token distributions over time.
- Traces: an individual request’s path and timing across gateway and provider operations.
- Logs: request-level event details and classifications used to investigate a specific case.
These signals complement each other. A dashboard can reveal a provider-specific rise in failures, while a linked trace and log can show how that failure appeared on a particular request. Logging and metrics may have gaps for some gateway error classes or test invocations, so do not assume every request will have a normal record in every backend.
Roll out monitoring in a practical order
- Define the request boundary and identifiers. Decide what starts and completes a gateway request, then propagate request or trace identifiers across the gateway and provider call where supported.
- Add bounded dimensions. Start with provider, model, operation, route, consumer or team, environment, and streaming mode, using only attributes the gateway can reliably populate.
- Instrument core counters and histograms. Add request and failure counters, input/output/total token counters when available, and separate duration histograms for gateway and provider time. Add TTFT and inter-token latency for streams.
- Export and secure telemetry. Configure the supported OTLP, Prometheus, or native-platform path, and restrict access to telemetry endpoints.
- Build dashboards and alerts. Show request and token rates, error rate, latency percentiles, streaming timings, and cost where the data supports it. Set alert conditions from your own objectives and baseline rather than copying an example filter.
- Test completeness and pivots. Verify that representative requests produce the expected attributes, metrics, traces, and logs—including streaming and passthrough cases—and that an aggregate spike can be followed to a request-level record.
Check feature maturity and coverage before relying on it
Telemetry capabilities differ by product and release. Kong’s AI OpenTelemetry metrics reference labels the feature Tech Preview and says it should not be used in production; its documentation also specifies version requirements and configuration flags that affect metric attributes. Review those requirements before building operational dependencies on the metrics.
Cloudflare documents exporting AI Gateway request spans through OTLP, with request, model, provider, token, cost, and custom metadata described in its integration documentation. Its analytics documentation covers requests, token usage, costs, errors, cached responses, time filtering, and GraphQL access. These are documented product capabilities, not independent performance measurements.
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Microsoft’s Azure API Management AI Gateway tier is in preview. Its current OTLP export covers token usage only; MCP request volume, latency, and errors can be viewed in the portal with Application Insights. Do not assume the preview’s OTLP stream contains the same coverage as its portal views.
Before adopting any implementation, compare signal coverage, attribution dimensions, export compatibility, version support and maturity, data completeness for streaming and passthrough traffic, and privacy controls such as payload capture, access, and retention. Recheck current product documentation as versions and preview coverage can change.
Quick Recap
Product documentation examples
- Kong Gen AI OpenTelemetry metrics reference documents request and provider latency, token use, TTFT/TPOT, configuration requirements, and the Tech Preview status.
- Kong Gateway OpenTelemetry metrics reference describes metric dimensions and attributes, minimum version, and configuration requirements.
- Kong guidance for monitoring AI LLM metrics covers Prometheus metrics, logging, token and cost visibility, and endpoint protection.
- Cloudflare OpenTelemetry integration describes AI request span export and associated metadata.
- Cloudflare AI Gateway analytics describes request, token, cost, error, and cache analytics.
- Microsoft’s Azure API Management AI Gateway preview documentation describes current token-use export and MCP monitoring coverage.
- Google Cloud API Gateway monitoring covers traffic, latency, errors, and example log queries.
- AWS API Gateway REST API monitoring describes combining CloudWatch metrics and logs with X-Ray traces, as well as potential logging and metrics gaps.
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