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Chapter DCost optimization labPage 2 of 8

Cost optimization lab

Set up interfaces and contracts

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 minSetup

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.

1Learn the idea

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Build typed boundaries and fixtures

Code begins at the boundary, not at the provider SDK. Parse CostSample with intent, model, input_tokens, output_tokens, cache_hit, latency_ms, quality_pass, and price_version before any model or tool call, and return OptimizationReport with cost_per_success, cache_rate, routed_share, quality_delta, and projected_savings even when the provider times out. Typed records make malformed data a normal error path instead of an exception discovered after a side effect. Inject the model client, clock, action adapter, and telemetry sink so tests can replace each one. That design also prevents a local test from accidentally reaching production.

The important implementation choice is ordering. Authenticate and normalize first; make the controlled call second; validate the response third; commit any state or action last. For this system, cache keys exclude raw identity and sensitive text; regulated intents always use the approved model path. A convenient function that mixes parsing, generation, action, and logging cannot prove that ordering. Keep each stage named so a trace can show which boundary rejected the request.

Use deterministic identifiers derived from stable business inputs when retries are possible. Record the release, fixture version, and policy version with the result. Never derive authorization from generated text. The code path should make the safe behavior easier than bypassing it: callers receive a decision object, not an unrestricted provider response.

For setup, define dataclasses or schemas, dependency protocols, and a fixture loader. Validate fixture uniqueness at startup and fail closed if required policy fields are absent. The contract should make illegal states difficult to construct.

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Focused implementation artifact

from dataclasses import dataclass

@dataclass(frozen=True)
class LabInput:
    payload: dict
    release: str
    trace_id: str

@dataclass(frozen=True)
class LabResult:
    passed: bool
    reasons: tuple[str, ...]
    metrics: dict[str, float]

def load_fixture() -> LabInput:
    payload = {"intent":"shipping_faq","model":"flagship","input_tokens":2100,"output_tokens":180,"cache_hit":false,"latency_ms":2400,"quality_pass":true,"price_version":"2026-07"}
    assert payload, "fixture must not be empty"
    return LabInput(payload, "candidate", "trace-test-001")

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Exercise the contract

Construct the fixture through the typed input, then deliberately remove one required field and add one unknown field. Decide whether each is rejected at parse time or represented explicitly. A permissive dictionary that silently drops data is not a contract. Record validation errors with a fixture ID and trace ID, but not the rejected payload.

Next, substitute fake adapters for model, storage, clock, telemetry, and external actions. Each fake should record calls, support a deterministic response, and default to denying network or side effects. Use those records to prove that a malformed input causes zero provider calls. The contract's primary result should carry enough data to compute cost per successful answer and emit llm_cost_per_success_usd without reopening raw input.

Recreate cost per call falls after routing, but retries increase and cost per successful answer rises as a fixture-construction test. If the type system cannot express the expectation, add an explicit policy field rather than hiding it in a comment. Keep the boundary enforceable in code. On violation, promote routing by intent, retain a holdout, and automatically disable it if quality or retries breach budget.

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