A model endpoint is safe to change only when its registered callers have proved the new contract and stopped relying on the old one.
Migrate inference clients with compatibility tests and usage evidence
Inventory who actually calls the endpoint
List each client, owner, environment, API version, request shape, response fields read and retry policy. Add traffic evidence from the gateway because a repository search misses scheduled jobs and old mobile builds. A consumer can depend on a route enum even if it ignores the score. Consumer inventory supplies the owner and retirement process; this migration adds concrete protocol assertions and a per-client cutover state.
Build a compatibility matrix
Replay redacted request fixtures from each caller against the old and candidate endpoint. Assert stable interpretation, not byte-for-byte equality when scores legitimately change. Test missing optional fields, unknown response fields, 4xx validation, 5xx retry, timeout, idempotency and fallback. If a caller assumes a route enum is exhaustive, a new value can crash it despite a valid schema. The API contract names these visible behaviors, while test fixtures make them repeatable in CI.
Move callers in measured stages
Deploy a compatible adapter or explicit v2 route, send a small registered client cohort to it and record version in request traces. Watch rejected requests, fallback, latency and caller retries by client ID. Distinguish “no old traffic observed” from “all old clients migrated”; intermittent batch jobs need a window spanning their schedule. Do not retire v1 while an unowned client still sends production traffic. A staged gateway switch should have a reversible route pointer and a response sample that proves which contract served.
Retire with an evidence trail
Record client sign-offs, last observed v1 call, scheduled-job coverage, rollback route and expiry for the adapter. Retest old and new versions after a model promotion, because a changed model can alter a response policy without changing an API schema. The applied migration runs a mobile reviewer and a nightly reconciliation job through different upgrade clocks. A safe deprecation is a measured state transition, not a date written in a ticket.
Implementation
def retire_contract(registered, observed, required_window_days=35):
blockers = []
for client_id, record in registered.items():
traffic = observed.get(client_id, {})
if not record["signed_off"]:
blockers.append((client_id, "unsigned"))
elif traffic.get("v1_calls", 0) > 0:
blockers.append((client_id, "old-traffic"))
elif traffic.get("observed_days", 0) < required_window_days:
blockers.append((client_id, "short-window"))
return {"state": "hold" if blockers else "retire", "blockers": blockers}
clients = {"review-ui": {"signed_off": True},
"nightly-ledger": {"signed_off": True}}
usage = {"review-ui": {"v1_calls": 0, "observed_days": 47},
"nightly-ledger": {"v1_calls": 1, "observed_days": 47}}
assert retire_contract(clients, usage)["state"] == "hold"
assert retire_contract(clients, {**usage, "nightly-ledger":
{"v1_calls": 0, "observed_days": 47}})["state"] == "retire"
Performance and operating cost
The retirement check is O(c) time and O(c) output space for c clients. Contract replay costs more: fixture maintenance, deployed-environment access and at least one full cycle of rare callers. The example counts observed days; production evidence also needs traffic completeness, known caller IDs and a schedule long enough to cover infrequent jobs.
Common Mistakes
- Retiring after a short quiet interval that misses monthly jobs.
- Assuming schema validation proves semantic response compatibility.
- Migrating a client without watching retry and fallback rates.
- Ignoring an unknown caller because it has no owner entry.
Read next
- Inference API contracts: version the decision, not only the payload
- Project: migrate receipt inference callers without breaking decisions
- Model retirement: find consumers before removing a version
- ML tests: separate code, data, model and service failures
- Promotion evidence: bind evaluation, contract and rollback to one digest
Continue the workflow: Dual-label evaluation: compare models across a target migration.
Continue the workflow: Privacy release gates: reduce exposed detail and retest model utility.
