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Chapter DCost optimization labPage 6 of 8

Cost optimization lab

Add observability and tests

Cost optimization lab is production work only when one frozen failure can be reproduced, one measurable gate can stop a release, and one operator can safely reverse it.

~14 minObservability

Before you start

Why this matters

Read this incident aloud: duplicate shipping questions consume flagship-model tokens even though a cached small-model answer passes quality checks. In two minutes, write the earliest deterministic check that should fail, the telemetry signal you would inspect, and the action that must not happen automatically. Compare your answer with this chapter's boundary: cache keys exclude raw identity and sensitive text; regulated intents always use the approved model path.

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Instrument metrics, logs, and traces

Instrumentation should answer four questions from one correlation ID: what release ran, which stage failed, how long each stage took, and what decision was made. Emit structured events, not interpolated prose. A useful event includes event, request_id, trace_id, release, stage, outcome, latency_ms, and a small set of bounded labels. Record llm_cost_per_success_usd as a counter or ratio with stable dimensions; never use request IDs, user text, or error messages as metric labels.

Trace the transaction using a root span and child spans around meaningful boundaries. Add counts, versions, finish reasons, and safe IDs. Do not attach full prompts, retrieved passages, secrets, or tool arguments. Logs explain discrete decisions, metrics detect population changes, and traces reconstruct one request; none replaces the others. Sampling may reduce ordinary success traces, but always retain errors and policy denials under the approved retention policy.

Create an operational test that sends a synthetic request tagged probe=true, then checks that the metric increments, the trace has required child spans, and the log can be found by trace ID. An alert is useful only if it links to a runbook and has a clear action. For this lab, the release rule remains reduce cost by 25% while quality drops no more than 1 percentage point, and the first response is to promote routing by intent, retain a holdout, and automatically disable it if quality or retries breach budget.

For observability, test telemetry as a product interface. Assert required span names and log keys, cardinality-safe dimensions, redaction, and alert routing using synthetic events.

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Focused implementation artifact

def emit_decision(metrics, logger, *, trace_id, release, outcome, latency_ms):
    metrics.counter("llm_cost_per_success_usd").add(1, {"release": release, "outcome": outcome})
    logger.info("router_experiment_v6", extra={
        "trace_id": trace_id, "release": release,
        "outcome": outcome, "latency_ms": latency_ms,
        "payload": "[REDACTED]",
    })

def test_telemetry(fake_metrics, fake_logger):
    emit_decision(fake_metrics, fake_logger, trace_id="t-1", release="canary", outcome="deny", latency_ms=18)
    assert fake_logger.last["payload"] == "[REDACTED]"

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Verify the telemetry path

Create a synthetic request that reaches every normal stage and another that reproduces cost per call falls after routing, but retries increase and cost per successful answer rises. The success trace must contain a root span and ordered child spans; the failure trace must mark the first broken boundary and skip spans for actions that never ran. Assert context propagation rather than relying on visually similar timestamps.

Emit llm_cost_per_success_usd with release, outcome, and a bounded domain slice. Keep request IDs in logs and traces, never metric labels. A structured log should include trace ID, stage, decision, latency, and policy or model version. Redaction happens before export and is tested against nested secrets and identifiers. Sampling may drop ordinary successes, but it must retain errors and policy denials under the documented retention cap.

Test the alert using synthetic events until it opens the runbook with the correct query. The alert condition derives from cost per successful answer and reduce cost by 25% while quality drops no more than 1 percentage point; include window and minimum sample so one request cannot page an entire team. The first response is to promote routing by intent, retain a holdout, and automatically disable it if quality or retries breach budget.

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