Model deprecations hit without mercy: a production system breaks at 2am because a provider pulled a snapshot. An llm model sunset dates tracker is the difference between proactive migration and incident response. This guide lays out a concrete pipeline to monitor OpenAI, Anthropic, and Google model lifecycles and automate fallback.
Audit your live model dependencies
You cannot track what you do not call. Extract every distinct model identifier from your inference logs for the past 30 days. Most gateways emit structured JSON; parse it.
import json, collections
models = collections.Counter()
with open("inference_logs.jsonl") as f:
for line in f:
rec = json.loads(line)
# OpenAI-compatible schema uses "model" at top level
models[rec.get("model")] += 1
for mid, cnt in models.most_common():
print(f"{mid:40} {cnt}")Pinpoint snapshot IDs (gpt-4-0613, claude-3-opus-20240229) separately from aliases (gpt-4, claude-3-opus). Snapshots get hard sunset dates; aliases roll silently under you.
If you use a gateway with per-token usage metering, pull the same breakdown from its billing export. The goal is a single authoritative list of model IDs that carry production traffic.
Where providers publish sunset signals
No single API returns a clean deprecation_date field for all three providers. You stitch together metadata and announcements.
OpenAI
The /v1/models endpoint lists available models but omits retirement plans. Deprecations arrive via the API changelog and email. Snapshot models (dated suffixes) are typically supported for at least three months after a newer snapshot ships.
curl https://api.openai.com/v1/models \
-H "Authorization: Bearer $OPENAI_API_KEY" | jq '.data[] | {id: .id, created: .created}'Anthropic
Anthropic’s /v1/models requires an API key and returns IDs with creation timestamps. Their support policy guarantees a window (often 3 months for older versions) but the API does not expose it. Track the newsroom and the SDK release notes.
curl https://api.anthropic.com/v1/models \
-H "x-api-key: $ANTHROPIC_API_KEY" \
-H "anthropic-version: 2023-06-01" | jq '.models[] | {id: .id}'Vertex AI publishes model lifecycle states (GA, PREVIEW, DEPRECATED). Use the gcloud CLI to list foundation models and filter:
gcloud ai models list --region=us-central1 \
--filter="state:DEPRECATED" --format="value(MODEL_ID,STATE)"Gemini standalone API lacks deprecation metadata; rely on the Vertex state machine for sunset tracking.
Build the llm model sunset dates tracker
Create a local registry that merges provider metadata with manually curated announcement dates. SQLite is enough.
import sqlite3, datetime
con = sqlite3.connect("model_registry.db")
con.execute("""
CREATE TABLE IF NOT EXISTS models (
model_id TEXT PRIMARY KEY,
provider TEXT,
announced_deprecation TEXT,
effective_date TEXT,
status TEXT
)
""")
def upsert(model_id, provider, eff_date, status, announced=None):
con.execute(
"INSERT OR REPLACE INTO models VALUES (?,?,?,?,?)",
(model_id, provider, announced, eff_date, status)
)
# Example seed from a changelog scrape
upsert("gpt-4-0613", "openai", "2024-09-01", "sunset", "2024-06-15")
upsert("claude-3-opus-20240229", "anthropic", "2024-11-01", "sunset", "2024-08-20")
con.commit()Schedule a daily job that polls the endpoints above, diffs against the table, and inserts new rows when a provider marks a model deprecated. The llm model sunset dates tracker becomes useless if it only runs quarterly.
Scraping changelogs
Provider blogs are the earliest signal. Parse RSS with feedparser and regex for known model prefixes.
import feedparser, re
MODEL_RE = re.compile(r"(gpt-[\w\-]+|claude-[\w\-]+)")
feed = feedparser.parse("https://openai.com/blog/rss.xml")
for entry in feed.entries:
found = MODEL_RE.findall(entry.title + entry.summary)
if found:
print(entry.published, found)Do the same for Anthropic’s newsroom HTML or RSS if available. Store the announced_deprecation date even when the effective date is inferred from policy.
Normalize and diff against usage
Join the registry with your audit counter. Flag any model with an effective date inside 45 days.
import sqlite3, collections, datetime
con = sqlite3.connect("model_registry.db")
cur = con.execute("SELECT model_id, effective_date, status FROM models")
registry = {row[0]: row for row in cur}
used = collections.Counter({"gpt-4-0613": 1200, "claude-3-opus-20240229": 300})
for mid, cnt in used.items():
if mid in registry and registry[mid][2] == "sunset":
days_left = (datetime.date.fromisoformat(registry[mid][1]) - datetime.date.today()).days
if days_left < 45:
print(f"ACTION: {mid} sunsets in {days_left}d, {cnt} calls/month")Alert with lead time, not after the fact
Wire the diff output to a Slack webhook or PagerDuty. Set three thresholds: 30, 14, and 7 days. Avoid a single “imminent” alert; context switching is cheaper earlier.
import os, requests
def alert(msg):
requests.post(os.environ["SLACK_WEBHOOK"], json={"text": msg})
if days_left <= 30:
alert(f"Model {mid} deprecates {registry[mid][1]} – migrate now")Test the alert path by injecting a fake sunset date two days out. A tracker that has never fired in staging will fail in production.
Routing and fallback strategy
When a snapshot sunsets, requests start failing with 404. If you front your calls with a gateway, automatic fallback can mask transient provider errors but not a permanent removal. A gateway such as n4n.ai honors client routing directives and provides automatic fallback when a provider is rate-limited or degraded, yet you still must update your routing table to point at the replacement model ID.
Define a fallback chain in your client config:
{
"route": {
"primary": "gpt-4-0613",
"fallback": ["gpt-4-0125-preview", "gpt-4-turbo"]
}
}The chain buys time, but the primary must be swapped before the deadline. Fallback chains also hide the deprecation if you are not watching logs, so emit a metric when a fallback triggers.
Migration playbook
- Pin the new model in a staging environment.
- Replay a week of production prompts through it; compare outputs on a fixed eval set.
- Shift 5% of traffic via weighted routing.
- Watch latency and error rates for 48 hours.
- Flip primary, keep old ID in fallback for two weeks.
Do not trust benchmark scores alone. Tool-calling schemas and system prompt handling differ across snapshots. Run a differential test:
def diff_calls(old_model, new_model, prompts):
for p in prompts[:100]:
a = complete(old_model, p)
b = complete(new_model, p)
if a.tool_calls != b.tool_calls:
yield p, a, bCommon pitfalls
Alias drift. Calling gpt-4 means the provider chooses the underlying snapshot. Your tracker may show no sunset, but the behavior changes underneath you. Lock snapshots for reproducible systems.
Timezone ambiguity. Announced dates are often UTC, but some dashboards render local time. Store all dates as ISO-8601 UTC and convert at display.
Single-source trust. Relying only on /v1/models will miss deprecations because none of the three expose a sunset field reliably. The llm model sunset dates tracker must combine API state, mailing lists, and changelog scrapers.
Ignoring fine-tuned models. If you host a fine-tune on a base that sunsets, your custom model dies too. Track base model IDs referenced in training jobs.
Alert fatigue. If every preview model triggers a page, engineers mute it. Restrict alerts to models present in your usage audit.
Scrape fragility. RSS formats change. Wrap parsers in try/except and alert on zero entries returned for 3 consecutive days.
Operationalize the tracker
Run the registry update in CI, not just on a laptop. A failed scrape should alert the on-call, because silence is how you miss a deprecation. Keep the SQLite file in a backed-up bucket; treat it as production config.
The llm model sunset dates tracker is not a one-time script. It is a living component of your LLMOps stack, as critical as your retry logic. Build it before the next provider email, not after the 2am page.