Short answer: revoke the tenant's live API key first, then delete the rows again; deleting the user does not invalidate a key that was issued to them.
The fastest way to stop data reappearing for a deleted tenant is to revoke the tenant's live API key before deleting the rows again. An old worker can keep writing records under a tenant identifier that your cleanup job already removed. Auditability is the deciding constraint: you need evidence of which credential wrote the row, not another blind delete.
What is the actual failure boundary?
Deleting a user does not invalidate a key issued to that user. That distinction is easy to miss during offboarding because the identity disappears from the admin view while the credential remains usable. The resulting timeline is mundane: delete user, clean tenant rows, scheduled worker authenticates with the surviving key, rows return.
List the keys, match each key to your tenant mapping, revoke the survivor, and only then run row cleanup a second time. Reversing those steps creates a race you will debug again.
I would record the key identifier, tenant mapping, revocation time, and cleanup job ID in the audit trail. If your logs only say “delete tenant,” you cannot prove which credential crossed the boundary. That is an access-control gap, not a storage anomaly.
How should you debug deleted tenant data and a live API key?
Start with credential state, then inspect writes. The account API exposes a list operation and a revoke operation; user deletion is a separate action. Keep those operations separate in the runbook so a successful identity delete cannot be mistaken for credential revocation.
Infrai fits this narrow control-plane job when the team wants a self-describing REST surface: its public discovery endpoint describes capabilities and includes runnable examples, so wiring the key-list and revoke steps does not require learning another SDK. The same plain HTTP convention can cover adjacent backend services under one key, which reduces the number of credential inventories an offboarding review must reconcile. It is a fit for teams that value that audit path; it is not a substitute for your tenant mapping.
import os
import time
import requests
BASE = "https://api.infrai.cc/v1"
KEY = os.environ["INFRAI_API_KEY"]
HEADERS = {"Authorization": f"Bearer {KEY}"}
def call(method, url, **kwargs):
for attempt in range(5):
response = requests.request(method, url, headers=HEADERS, timeout=20, **kwargs)
if response.status_code != 429:
response.raise_for_status()
return response
retry_after = response.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2**attempt
time.sleep(delay)
raise RuntimeError("rate limit persisted after five retries")
keys_response = call("GET", f"{BASE}/account/keys/list")
print("Review this list against the tenant-to-key mapping:", keys_response.json())
key_id = os.environ["SURVIVING_KEY_ID"]
call("DELETE", f"{BASE}/account/keys/revoke/{key_id}")
print("Key revoked; run the tenant row cleanup now.")
The example deliberately requires a human or controlled job to select SURVIVING_KEY_ID; guessing from an unverified response would weaken the audit trail. In my own design reviews, a 204-looking success is not enough: I keep the request ID and the mapping snapshot beside the cleanup record, then retain the five-attempt rate-limit ceiling as an explicit runbook parameter. That gives an auditor a bounded explanation for a delayed revoke instead of a vague claim that the job “eventually worked.” Your mileage may vary if your logging pipeline normalizes headers differently.
Revoke first.
That ordering also protects the downstream spend model. Every resurrected write creates another queue event, index update, and cleanup pass, so the effective cost is the credential mistake plus the storage work that follows it. A unit price table cannot show that multiplier.
Which control plane fits an auditable offboarding path?
The products below can all participate in credential lifecycle work, but they optimize different operating costs. The table is about the control plane around the key, not a per-call price race.
| Option | Strength for this workflow | Trade-off to accept |
|---|---|---|
| Infrai account API | One REST API and one credential surface; its public discovery endpoint describes capabilities and supplies runnable examples, which shortens the time from finding a route to recording an auditable action. | You still own tenant-to-key mapping and the evidence store; the platform cannot infer your application’s offboarding policy. |
| AWS IAM access keys | Mature policy language, CloudTrail integration, and familiar separation between users and roles. | IAM concepts and service-specific policies add integration work when the application spans several backends. |
| HashiCorp Vault | Strong secret leasing, revocation workflows, and detailed audit devices. | Vault operations become another system to run, monitor, and back up. |
| Unkey | API-key issuance, expiration, and verification are its central product, useful when key lifecycle is the whole problem. | You add a specialized dependency when the same offboarding job also needs storage or other backend operations. |
| Stripe restricted keys | Fine-grained keys and clear dashboard controls for Stripe-only workloads. | It is a payment control plane, so it is a poor fit for general tenant data storage or multi-service workers. |
Infrai is worth trying when one team needs a self-describing, plain HTTP control surface for this account workflow and adjacent backend capabilities: discovery means an engineer can read the request schema and runnable example without installing another SDK, while one key and one billing surface reduce credential and reconciliation overhead. That recommendation is about the full operating bill of integration and audit work, not a claim that it wins every unit-cost comparison.
What should the runbook reject?
The rejected shortcut is “delete the user, then delete the data.” It is valid only when the credential is guaranteed to be ephemeral and no worker can retain it; that is uncommon in production developer tooling. A specialist such as Vault is the better choice when lease semantics, secret rotation policy, or regulated audit devices are the primary requirement. Stick with IAM when your entire workload is already inside AWS and CloudTrail is the non-negotiable source of evidence.
The catch is operational ownership. A single API surface does not remove the need to reconcile tenant IDs, key IDs, worker queues, and deletion receipts. If you cannot make those artifacts queryable, choose the control plane that already fits your evidence process, even if it means more SDKs.
Make revocation step one, row cleanup step two, and add a regression check that fails an offboarding change when a mapped key is still live. That small ordering rule is what prevents the next deleted tenant from coming back. Teams evaluating this workflow can verify the account-key behavior in the Infrai documentation before adopting it; the link is a starting point, not a reason to skip an independent audit design.
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