AI Gateway Observability Guide

AI gateway observability explains how applications, agents, gateways, model providers, and tools behave as one production system. Traditional API signals such as request rate, error rate, and latency remain necessary, but they are not sufficient. Teams also need token usage, model and provider routing, streaming latency, retries, fallback, estimated cost, guardrail decisions, and agent tool activity.

The goal is not to log every prompt and response. That can expose sensitive data and create unnecessary storage risk. The goal is to collect enough structured evidence to operate AI traffic, investigate failures, allocate usage, and improve policy.

For the complete architecture and terminology, start with the AI Gateway Guide.

What Makes AI Traffic Different to Observe?

AI requests differ from conventional API calls in several ways:

Latency includes queueing, model startup, first token, and full generation time.
Streaming can succeed initially and fail before completion.
Token volume varies significantly between requests.
One logical task may trigger multiple models, retries, and tool calls.
Provider errors, model behavior, and gateway behavior can look similar to users.
Cost depends on model and usage, not only request count.
Prompts, responses, retrieved context, and tool results may contain sensitive data.

Observability must preserve these distinctions. A single average-latency chart hides whether users wait for the first token, generation is slow, or fallback adds extra attempts.

AI Gateway Signal Model

flowchart LR
  app[Application or Agent] --> gateway[AI Gateway]
  gateway --> providerA[Model Provider A]
  gateway --> providerB[Model Provider B]
  gateway --> mcp[MCP Gateway or Tool]
  gateway --> metrics[Metrics]
  gateway --> logs[Structured Logs]
  gateway --> traces[Distributed Traces]
  gateway --> usage[Token and Cost Records]
  metrics --> operations[Operations and Alerts]
  logs --> investigation[Investigation and Audit]
  traces --> investigation
  usage --> finance[Usage and Budget Analysis]

Metrics support fast aggregation and alerting. Logs preserve structured request and decision context. Traces explain a multi-step path. Usage records support token and cost allocation. Audit records capture administrative or policy changes. Each signal has a different retention and access model.

Core Metrics

Traffic and Availability

Request rate by application, route, model, and provider.
Success and error rates.
Active requests and streaming connections.
Timeouts, client disconnects, and cancellations.
Gateway-level and provider-level failures.

Separate gateway rejection, network failure, provider quota, provider error, and application cancellation. Grouping all of them as HTTP errors makes incident response slower.

Latency

Measure several latency stages where the integration exposes them:

Gateway processing time.
Provider connection and queue time.
Time to first token (TTFT): how long an interactive user waits before output begins.
Inter-token or generation rate.
Time to last token or full response duration.
Tool and MCP call duration.
Retry and fallback delay.

Use percentiles rather than averages for user-facing latency. Segment by model, provider, region, workload, and streaming mode when those dimensions are operationally meaningful.

Token Usage

Input tokens.
Output tokens.
Total tokens.
Tokens per request and per successful task.
Token usage by consumer, team, application, model, and provider.
Rejected or incomplete request usage when available.

Token counts may come from local estimation, provider responses, or both. Record the source because different tokenizers or missing provider usage data can produce discrepancies.

Routing and Resilience

Selected model and provider.
Routing rule or policy outcome.
Retry attempts.
Fallback attempts and destination.
Health-check status.
Provider quota responses.
Requests served by a non-default model.

A successful final response can hide failed earlier attempts. Without attempt-level visibility, teams may see rising cost and latency without understanding that fallback is responsible.

Rate Limits and Budgets

Allowed and denied requests.
Limit dimension and scope.
Tokens consumed within each window.
Remaining quota where it can be calculated reliably.
Estimated spend and budget thresholds.

See AI Gateway Rate Limiting for enforcement design. Observability should distinguish a gateway policy denial from a provider-side quota denial.

Structured Logging Without Unnecessary Data Exposure

A useful AI gateway log can include:

Request or trace identifier generated or accepted according to a documented trust model.
Authenticated consumer, team, or application identifier.
Route, workload, model, and provider.
Request start, first-token time, and completion time.
Input, output, and total token counts with their source.
Retry and fallback decisions.
Rate-limit or guardrail decision code.
Provider and gateway status categories.
Estimated cost fields when calculation inputs are known.

Prompts and responses require stronger caution. Full content may contain personal data, credentials, proprietary code, retrieved documents, or tool results. Logging everything by default can create a second sensitive-data store.

Prefer:

Metadata and classification over full content.
Configurable sampling for diagnostic sessions.
Redaction before storage where the mechanism is verified.
Separate access controls and retention for content-bearing logs.
Hashes or stable identifiers only when their privacy implications are understood.
Explicit consent and policy for replay or evaluation datasets.

Do not describe a log field as a trusted user identity if the caller can supply it without verification.

Distributed Tracing for AI Requests

A trace can show one user action across the gateway, provider attempts, retrieval, tools, and application services.

Useful spans include:

Application request.
AI gateway policy and routing decision.
Provider attempt.
Retry or fallback attempt.
Retrieval or embedding request.
Agent tool or MCP call.
Response streaming and completion.

Span attributes should remain bounded. Prompt text, full tool output, and user identifiers should not be added casually. High-cardinality values can also increase cost and reduce observability-system performance.

Trace propagation may cross organizations or providers. Verify which headers leave the gateway and whether the destination supports or preserves them.

Observing Streaming Responses

A streaming request can have several outcomes:

Failed before headers or the first token.
Started successfully, then terminated early.
Completed normally.
Client disconnected while the provider continued.
Gateway timed out while an upstream operation remained active.

Record at least start, first token, terminal status, generated usage, and cancellation source when available. A 200 at stream start is not sufficient evidence that the user received a complete answer.

Dashboards should separate time to first token from full duration. Users may tolerate a long answer if it starts quickly, while a low average full duration can still hide poor interactive response.

Provider and Model Comparison

Observability supports model and provider selection when teams compare equivalent workloads:

TTFT and full latency.
Success and provider error rates.
Retry and fallback frequency.
Input and output tokens.
Estimated cost per successful task.
Quality or evaluation results collected outside the gateway.

Gateway telemetry cannot determine answer quality by itself. Quality requires a defined evaluation method, dataset, or user signal. Avoid treating lower latency or cost as proof of better model output.

AI Agent and MCP Observability

Agents create trees of activity rather than one request. Track:

Agent run and parent request identifiers.
Number of model turns.
Tool and MCP calls.
Selected tool or server.
Tool latency, error, and timeout.
Model and token usage by step.
Loop or maximum-step termination.
Policy decisions for tool access.

The gateway can observe network-visible calls, but it may not know the agent's internal plan or whether a result was semantically correct. Combine gateway telemetry with agent-runtime events using a shared correlation model whose trust boundaries are documented.

See How API Gateways Enhance MCP Servers and AI Agent Control Plane for related architecture.

Dashboards by Audience

Platform Operations

Availability and latency by provider and model.
Streaming concurrency.
Retry, fallback, and quota failures.
Gateway and upstream resource health.

Application Teams

Usage and errors by application or route.
TTFT and completion latency.
Token usage per workflow.
Limit and policy decisions.

FinOps and Engineering Leadership

Usage and estimated cost by team, application, model, and provider.
Budget trend and forecast.
Cost per successful task or product workflow.
Unused reserved capacity or repeated fallback cost.

Security and Governance

Access and policy denials.
Guardrail decision categories.
Administrative policy changes.
Tool and MCP access patterns.
Retention and evidence coverage.

Each audience should see the minimum information needed for its responsibility. Cost reporting rarely needs prompt content; security investigation may require restricted diagnostic access.

Alerts That Lead to Action

Useful alerts connect a signal to an owner and response:

Provider errors or quota denials exceed a threshold.
TTFT or full latency regresses for a model.
Retry or fallback frequency increases.
Token usage changes sharply for an application.
A team approaches its quota or budget.
Streaming disconnects increase.
An agent exceeds normal model-turn or tool-call behavior.
Telemetry stops arriving from a gateway cluster.

Avoid alerting on every individual model error. Use service objectives, rates, and workload criticality to reduce noise.

Incident Investigation Workflow

When an AI request fails or becomes slow:

Identify the application, route, time window, and correlation identifier.
Determine whether the gateway accepted or rejected the request.
Inspect routing, rate-limit, and policy decisions.
Separate gateway latency from provider TTFT and generation time.
Review every retry and fallback attempt.
Check provider quota and health signals.
Inspect agent or MCP child calls when present.
Confirm whether the client disconnected or the stream ended early.
Review content only through an approved, restricted process when metadata is insufficient.
Record remediation and update policy or alerts if needed.

Production Checklist

Define one identifier model for applications, consumers, requests, agent runs, and attempts.
Distinguish client-supplied correlation from authenticated identity.
Measure TTFT and completion time separately.
Record input and output tokens and the source of the count.
Preserve retry and fallback attempts, not only final status.
Track model and provider routing decisions.
Minimize prompt, response, retrieved-context, and tool-result logging.
Define retention and access by signal type.
Bound metric cardinality.
Test streaming completion, cancellation, timeout, and provider failure.
Reconcile estimated usage with provider reporting.
Assign owners and runbooks to alerts.

AISIX AI Gateway and API7 Gateway Telemetry

AISIX AI Gateway documents gateway metrics and usage records for requests, tokens, limits, models, providers, and policy decisions. Its traffic control documentation describes the policy and usage scopes that observability should preserve. The product experience also presents request volume, latency, errors, spend, and model health across configured environments.

API7 Gateway and Apache APISIX use a different observability model built around gateway logs, metrics, traces, and AI plugins. Keep those signals separate from AISIX-specific dashboards and policy records when documenting an architecture.

Neither product description implies that every signal in this guide is available by default. Verify exact fields, integrations, metric cardinality, token accounting, retention, and deployment requirements against current documentation.

For general API and service telemetry, use the Observability solution. AI-specific dashboards should build on that foundation without assuming model quality can be inferred from gateway telemetry alone.

Next Steps

Review the complete AI Gateway Guide.
Design enforcement with AI Gateway Rate Limiting.
Review Load Balancing Multiple LLM Backends.
Evaluate AISIX AI Gateway for AI-native observability and operations.

AI Gateway Observability: Metrics, Logs, Traces, Tokens, Cost, and Agent Traffic