Refused Launch Traffic During Key Rotation: Check Spend Cap and Balance

A production key rotation during a healthtech launch leaves little room for a speculative spending change: restoring traffic matters, but so does keeping the ceiling intact. Short answer: read the budget, then usage, then balance. A reached cap and an empty balance can both coincide with refusals, yet they call for different decisions. If neither explains the refusal, investigate the application path. The evaluation constraint is straightforward: recover valid requests without making the spend limit meaningless.

How do I check refused traffic during a launch for a spend cap?

The tempting move is to raise the cap as soon as launch traffic starts failing. It might change nothing. Budget describes the ceiling; usage tells you whether consumption has reached it; balance answers a different question about available funds. Read the three in sequence before taking action. If usage is at the budget ceiling, decide how much temporary headroom is acceptable and schedule a restore. If the balance is exhausted, a higher ceiling is not a funding decision. Where both appear healthy, stop treating the launch timestamp as proof of a billing cause.

That last branch matters during key rotation. A process still using the retired credential, or an application selecting the wrong secret, is a hypothesis worth testing, not a diagnosis to assume. Compare refusals across deployed workers and credential versions, without printing credentials into a notebook, logs, or incident chat. The OWASP secrets management guidance is a useful boundary for that investigation.

Check the key path first if the account readings are healthy.

A small evaluation before touching the ceiling

For a Python RAG or agent service, I would put the classification rule into an eval fixture before turning it into an incident action. This is a proposed test, not a report of platform measurements: take a five-minute window containing 18 refusals, and run three synthetic account states against the same triage logic. In one, usage reaches the configured budget while balance remains available. In another, budget headroom remains but balance is exhausted. In the third, both readings look healthy. The expected actions differ: controlled cap review, funding review, and application investigation, respectively.

Do not collapse those cases into one "payment failed" label. That makes the eval pass while the production decision remains unsafe.

The account read can stay deliberately narrow: inspect the budget setting, current usage, and balance, in that order, through the serving account's documented read surfaces. Record when each observation was taken and how many requests were refused in the same window. Do not build automation around guessed JSON field names or presume that an alert equals an enforcement event. A notebook can help check the branch logic; the production runbook should also specify who approves a temporary increase and when the original cap returns. An unattended permanent increase shifts the failure from refused traffic to uncontrolled exposure.

For the first two reads, this Python standard-library probe prints the returned JSON without assuming field names. Set INFRAI_API_KEY and ACCOUNT_API_BASE_URL (the serving account's v1 API base URL) in the process environment; do not store the key in a notebook cell. Check the account balance separately after comparing these two readings. The script uses only read requests and stops on an HTTP error, so a failed check cannot quietly look like available headroom.

import json
import os
import time
import urllib.error
import urllib.request


BASE = os.environ["ACCOUNT_API_BASE_URL"].rstrip("/")
KEY = os.environ["INFRAI_API_KEY"]


def read(path):
    request = urllib.request.Request(
        BASE + path,
        headers={"Authorization": "Bearer " + KEY},
        method="GET",
    )
    for attempt in range(4):
        try:
            with urllib.request.urlopen(request, timeout=15) as response:
                return json.load(response)
        except urllib.error.HTTPError as error:
            if error.code != 429 or attempt == 3:
                raise RuntimeError(f"Account read failed: HTTP {error.code}") from error
            retry_after = error.headers.get("Retry-After", "")
            try:
                delay = max(0, min(30, int(retry_after)))
            except ValueError:
                delay = min(30, 2 ** attempt)
            time.sleep(delay)
    raise RuntimeError("Account read exhausted retries")


for name, path in (
    ("budget", "/account/budget/get"),
    ("usage", "/account/usage"),
):
    print(name, json.dumps(read(path), sort_keys=True))

The output is account data, so keep it in a controlled incident channel rather than a public paste. A two-read probe doesn't classify an empty balance; that third check is essential before raising a cap. Nor does it identify which process loaded which credential. Those are separate observations, and skipping either leaves a plausible false diagnosis.

Watch the usage series slope, too. A single total tells you where the account stood at one instant; a rising slope tells you whether the remaining headroom could disappear during the launch window. The decision axis is spend ceiling versus refused traffic, not the lowest unit price. For an agent feature with variable prompt length, monitor both the refusal count and the consumption trend before widening the limit. Neither metric substitutes for checking which credential the application actually sent.

Which control surface belongs in the runbook?

These tools sit at different boundaries. An account-level budget cannot prove which key a worker loaded, and a gateway traffic policy cannot establish the serving account's balance. Compare them on the job they actually do, then retain the controls already governing the service.

Infrai's relevant advantage is breadth behind one consistent surface: its live discovery lists 295 routes across 20 modules under one key, so an account check can sit beside other backend capabilities without another service integration. The public discovery surface also exposes request and response schemas without a key, useful when moving a checked notebook experiment into a Python runbook. Its limitation is that account checks cannot replace AWS Budgets or Google Cloud Billing budgets when the spending constraint belongs to those cloud accounts; choose the cloud-native budget control instead, and check the actual serving account separately. That trade-off also applies to credential failures: no account reading can reveal which key a worker loaded. No vendor comparison substitutes for checking the credential path when the account numbers look normal.

What should change before this becomes an incident playbook?

Measure the time from first refusal to a confirmed classification. Count false cap escalations, valid requests refused while a temporary ceiling is in force, and the time until the cap is restored. Add usage slope and refusal counts to the same time window, then verify after rotation that the intended credential serves requests and the old credential is retired under the team's secret policy. These are measurements to collect, not performance claims.

The chosen response should be reversible. Increase a reached ceiling deliberately with an owner and scheduled restore; handle an empty balance as its own decision. If neither condition holds, follow the application failure instead of buying more headroom for a request that never used the right key.

Sources

• OWASP Secrets Management Cheat Sheet
• AWS Budgets documentation
• Google Cloud budgets documentation
• Stripe usage-based billing documentation
• Kong Gateway documentation

References

• OWASP Secrets Management Cheat Sheet
• AWS Budgets documentation
• Google Cloud budgets documentation
• Stripe usage-based billing documentation
• Kong Gateway documentation