# AI API Deprecations: Shutdown Dates & Migration (`a_a2ad/ai-api-deprecation-tracker`) Actor

Query 93 dated lifecycle decisions across selected OpenAI, Anthropic, Azure Foundry and Amazon Bedrock sources. Exact model IDs, shutdown dates, versions, region rules, replacements and conflicting official claims. A bounded snapshot, not a live model inventory.

- **URL**: https://apify.com/a_a2ad/ai-api-deprecation-tracker.md
- **Developed by:** [Manabu Omae](https://apify.com/a_a2ad) (community)
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$5.00 / 1,000 complete lifecycle decisions

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## AI API Deprecations: Shutdown Dates & Migration

Find the announced lifecycle conditions for an exact model or API service before planning a migration. This Actor returns **93 complete decision records** from selected official OpenAI, Anthropic, Microsoft Foundry and Amazon Bedrock documentation, with provider, version and region scope, related lifecycle stages, replacement guidance, evidence and unresolved conflicts together.

**Snapshot observed: 6 November 2026. Main coverage window: 6 November 2026 through 4 February 2027.** The snapshot also includes explicitly marked lower-bound and retired control records needed to interpret the schedules. It is a saved document snapshot, with no live endpoint check, scheduled refresh or update SLA. The name “tracker” refers to structured tracking information in this dated release.

### Start with one model

Pass this Actor input from the Console, REST API, SDK or an Apify MCP client:

```json
{
  "model_ids": ["gpt-5.5-cyber-preview"],
  "provider_scopes": ["openai_direct"],
  "limit": 1
}
```

This snapshot returns one scoped decision containing OpenAI's announced **1 February 2027** model shutdown, its source, announcement date and replacement-selection limitation. The event date has day precision and an unknown timezone. The document was observed on 6 November; the run does not test whether an endpoint accepts requests now.

Start the Actor once for the requested input. If your REST or MCP client returns a Run ID before completion, poll that same Run ID and read its default Dataset when it succeeds. Do not start a duplicate Run merely to retrieve its results. Reuse saved records by `record_id` and `record_revision_sha256` when the same snapshot is sufficient.

For an agent workflow, use the exact model ID and provider route, inspect every returned record's version/region/deployment scope and date semantics, then use the provider evidence to plan the appropriate migration. A named replacement is a provider recommendation, not a tested compatible substitute. Some transitions require an API migration as well as a model-name change.

**$0.005 per complete decision ($5 per 1,000 results).** This example returns one billable item. Set the run's maximum total charge to at least $0.005 for one result. No-match returns no Dataset items and incurs no Dataset-item charge; it does not prove that a model is safe, supported, active or free of deadlines.

### What is covered

| Route enum | Records | Recorded source scope and limits |
|---|---:|---|
| `openai_direct` | 31 | Selected upcoming deprecation entries relevant to the fixed window, including four service or training workflows. Later schedules outside the window are not an all-model inventory. |
| `anthropic_direct` | 4 | Sonnet 4.5's announced shutdown and three lower-bound controls. A minimum availability date is not an announced shutdown. |
| `azure_foundry` | 47 | Opened retirement-table and lifecycle-stage entries relevant to the window. Model versions, seller routes and deployment footnotes are retained. |
| `amazon_bedrock` | 11 | Relevant scheduled end-of-life entries in the opened Legacy table, an extended-access transition and a regional retired control. Newer model cards were not exhaustively inventoried. |

Gemini's current schedule was not verified and is not a selectable provider route in this release. Sources do not establish complete coverage of every provider, active model, region or deadline. A model missing from the results may be outside the covered dates, source sections or supplied filters. **No match is never evidence that continued use is safe.** This statement also applies when the Dataset is empty.

### Input and selection

| Field | Meaning |
|---|---|
| `model_ids` | Up to 100 exact model IDs or aliases listed in `policy_scope.asset_ids`. Matching is case-sensitive after Unicode NFKC normalization and outer whitespace trimming; there is no prefix or fuzzy match. Each value is 1–256 printable characters. |
| `provider_scopes` | Up to four entries from the exact route enums above. OpenAI direct API and an Azure deployment are different routes. |
| `record_ids` | Up to 100 exact IDs from previous results, useful for retrieving a particular version or regional decision. |
| `query` | At most 200 characters and 20 whitespace-separated terms. All terms must occur in the saved title, scope, rules or uncertainty. Unicode-normalized, case-insensitive text search. |
| `limit` | Integer 1–100, default **1**. Omitted filters return a deterministic catalogue-order sample; they do not imply complete coverage. |

Values within an ID list are alternatives. Different filter fields combine with AND. Duplicate IDs or aliases for the same decision produce one item. An omitted array does not filter; an explicit empty array selects nothing. Unknown IDs return no matches. Unsupported fields, invalid provider names and malformed values fail before Dataset output.

One ID may legitimately match several records: Azure `gpt-chat-latest` has distinct model versions, and Bedrock model scopes can differ by region. Use a sufficient `limit`, inspect `selection.truncated_by_limit`, then choose exact `record_ids` if only one scoped decision is needed. The metadata distinguishes matched records from returned records and reports requested model IDs not matched under all supplied filters. It does not represent provider-wide inventory completeness.

Retrieve all four recorded Azure versions for a saved model alias:

```json
{"model_ids":["gpt-chat-latest"],"provider_scopes":["azure_foundry"],"limit":100}
```

Retrieve a service workflow by text:

```json
{"provider_scopes":["openai_direct"],"query":"self-serve fine-tuning","limit":1}
```

Service/workflow records have empty `asset_ids`; service labels are not model IDs and do not match `model_ids`. Their events distinguish, for example, stopping new training jobs from shutting down fine-tuned inference.

To retrieve the entire **current saved corpus**:

```json
{"limit":100}
```

This returns 93 records, costing $0.465 at the stated rate. A $0.50 run cap accommodates this batch. It retrieves the saved selection described above, not all AI models or a fresh scan.

### Output and interpretation

Each Dataset item contains `record_id`, `decision_key`, `title`, `policy_scope`, complete `rule_groups`, `sources`, `rights`, `needs_confirmation` and `confirmation_reasons`. The envelope adds `coverage_window`, `source_coverage`, selection metadata, a data version, a canonical record hash and separate snapshot and delivery times.

Read each `lifecycle_event` within its rule group and exact source scope:

- `date_kind` and `date_semantics` distinguish announced dates, earliest bounds and conflicting official dates. A lower-bound control keeps `effective_date` and `announced_shutdown_date` null; its earliest date remains separate.
- Conflicting official assertions remain together. The Azure `gpt-realtime-mini` record preserves both published candidate dates and leaves the resolved effective date unknown. The product does not choose the earlier or later date for you.
- `timezone: null` means the source did not establish a timezone; no timestamp or midnight deadline is invented.
- Deployment exceptions remain material. For Azure `gpt-4o`, the extension does not revive already retired Regional Standard deployments, and automatic upgrade behavior does not apply to every deployment type.
- Unknown replacements, migration behavior or source coverage stay unknown. A false `needs_confirmation` flag means there is no recorded additional confirmation reason; it is not an availability or compatibility guarantee.

All related stages and competing claims for the same provider/asset/version/scope are delivered in one complete decision. Shared aliases and snapshots of the same shutdown are not separate charges. Different versions or regional scopes can be distinct decisions. Cross-provider evidence that affects a decision is included without charging a separate row for the evidence itself.

`retrieved_at_jst` is delivery time. `snapshot_observed_at_jst` marks the end of a documented observation window, not an exact per-page fetch time. Source observation precision and the window are retained. `real_time_lookup`, `live_endpoint_state_verified`, `availability_guarantee` and `replacement_compatibility_guarantee` are false.

### Pricing and budget behavior

The price is **$0.005 per complete decision**, including its related stages, conditions, sources and unknowns. There is no Actor-start fee; platform usage is included. Repeated retrieval is charged again. Empty results produce no Dataset-item charges.

The full selected batch must fit the remaining run allowance before output is emitted. If it does not, the run fails with `BATCH_BUDGET_EXCEEDED` without emitting a partial paid batch. The Actor uses the standard Dataset item event once for each returned decision; it does not separately charge for aliases, source claims or fields. Use `record_id` plus `record_revision_sha256` to cache unchanged records where appropriate.

### Evidence and corrections

Official documents remain freely accessible. This product provides hosted, independently structured access to selected factual relationships and their evidence limits. Source links and locators are included in every decision:

- [OpenAI API deprecations](https://developers.openai.com/api/docs/deprecations)
- [Anthropic model deprecations](https://platform.claude.com/docs/en/about-claude/model-deprecations)
- [Microsoft Foundry model retirement schedule](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule)
- [Amazon Bedrock Legacy lifecycle](https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html)

No source articles, images, private account data or vendor licences are redistributed or claimed. This product is not affiliated with the providers. It does not call model endpoints, change deployments or execute migrations. For a factual error, open an issue with the record ID and the conflicting official evidence.

# Actor input Schema

## `model_ids` (type: `array`):

Optional case-sensitive exact model IDs/aliases recorded in policy_scope.asset_ids. NFKC normalization and outer whitespace trimming only. Up to 100 values, 256 characters each; no prefix or fuzzy matching. Duplicate aliases of one scoped decision return one item. Omit for no model filter; \[] selects nothing. Services are selected with query, provider_scopes or record_ids.

## `provider_scopes` (type: `array`):

Optional exact enums: openai_direct, anthropic_direct, azure_foundry, amazon_bedrock. Up to four entries. Omit for all four recorded routes; \[] selects nothing. A route is not a claim of all models on that platform.

## `record_ids` (type: `array`):

Optional exact record IDs from an earlier result. Up to 100; duplicates return one item. Omit for no record filter; \[] selects nothing. Combine filters with AND.

## `query` (type: `string`):

All whitespace-separated terms must occur in the saved title, scope, rules or uncertainty. Unicode-normalized, case-insensitive text search; maximum 200 characters and 20 terms. This also finds service/workflow names. This is not live web search.

## `limit` (type: `integer`):

1–100 complete decision records. Default 1 is a deterministic sample, not full coverage. Several versions or regions may match one model ID. Read selection.truncated_by_limit and the provider/version/region scope. At $0.005 per result, all current 93 records cost $0.465.

## Actor input object example

```json
{
  "limit": 1
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "limit": 1
};

// Run the Actor and wait for it to finish
const run = await client.actor("a_a2ad/ai-api-deprecation-tracker").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "limit": 1 }

# Run the Actor and wait for it to finish
run = client.actor("a_a2ad/ai-api-deprecation-tracker").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "limit": 1
}' |
apify call a_a2ad/ai-api-deprecation-tracker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,a_a2ad/ai-api-deprecation-tracker"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/S8Tb8YQ3ONN65TYco/builds/g09Ju5q1hXO8AjnAg/openapi.json