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If the model or endpoint your app uses is retired, requests can fail after its shutdown date and the affected AI features may stop working until you migrate them. A provider can also retire a whole model-access service, removing its API and related tools. These lifecycle changes are not the same as the provider company shutting down.
What “shutdown” can mean for your app
A provider may retire one model, one endpoint, or a broader service. OpenAI defines “sunset” and “shut down” as the point when a model or endpoint is no longer accessible: OpenAI API deprecations. Amazon Bedrock warns that requests to an end-of-life model will fail and says migration is not automatic: Amazon Bedrock model lifecycle.
Retiring a single model does not necessarily affect a provider’s other products. A broader retirement can remove the access layer itself: GitHub’s announcement, for example, covered the Models playground, model catalog, inference API, and bring-your-own-key (BYOK) endpoints. That service’s announced retirement date was July 30, 2026; it is a concrete example, not a timetable for other providers: GitHub Models retirement announcement.
These events concern a model, endpoint, or named service. They do not establish that the provider company is closing, nor do the cited lifecycle notices quantify the odds of a provider going out of business.
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How much notice will you get?
There is no uniform notice period. Anthropic says it will provide at least 60 days’ notice for publicly released models with upcoming retirements. OpenAI cautions that preview models may receive much shorter notice. Google describes listed dates as the earliest possible shutdown dates and says it will communicate exact dates. Read the notice for the specific model and service rather than treating any one provider’s policy as an industry standard.
Sources: Anthropic model deprecations, OpenAI API deprecations, and Google Vertex AI model versions.
How to prepare and migrate before the deadline
- Inventory every dependency. Search source code, configuration, secrets management, and deployment settings for provider endpoints, model IDs, SDKs, and provider-specific features. Use usage exports where available; Anthropic documents an export that shows usage by API key and model: Anthropic model deprecations.
- Assign someone to watch lifecycle notices. Review provider deprecation pages, release notes, account emails, and console alerts on a regular schedule. Preview or experimental models may have shorter timelines, so track those separately.
- Choose a replacement against your requirements. Check region availability, modality, context and output behavior, tool or API compatibility, data handling, operational support, and price under your workload. A provider’s suggested successor is a candidate to test, not proof that it will behave equivalently in your app.
- Test using representative work. Compare task quality, failures, latency, tool calls, output format, and operational metrics on your own test cases. Both OpenAI and Anthropic advise evaluating replacements before retirement: OpenAI API deprecations and Anthropic model deprecations.
- Change and deploy deliberately. Update model identifiers and any provider-specific request or response handling. Stage the change, monitor it in production, and keep a rollback path while the old endpoint remains available. Bedrock explicitly states, “Migration will not happen automatically”: Amazon Bedrock model lifecycle.
- Verify data and contract terms early. Find out whether prompts, logs, fine-tuning artifacts, and application state can be exported, and how long they are retained. The lifecycle guidance cited here does not establish a general right to retrieve that data after a service ends.
How to compare replacement options
There is no universal best replacement. Evaluate candidates against the same workload and operational requirements rather than relying on a model name or a provider’s recommendation alone.
| What to compare | What to check |
|---|---|
| Task quality | Results on your application’s own evaluation set. |
| Integration | Request and response formats, tool calls, SDKs, and provider-specific features your app depends on. |
| Operations | Latency, availability, regional access, capacity, and required modalities. |
| Cost | Price under your actual workload, including expected input, output, and usage patterns. |
| Data and portability | Handling, retention, export terms, and access to fine-tuning assets. |
| Migration support | Available lifecycle guidance, support, and the code or configuration changes your app requires. |
Official guidance encourages testing replacements and describes lifecycle or regional details, but it does not identify one option as best for every app. A routing or abstraction layer may help organize integrations, but it cannot guarantee uninterrupted service or make models behaviorally identical.
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It depends on how the app is built. A 2026 study of analyzed open-source applications found that model identifiers were hard-coded in 94% of the applications it examined. In that sample, the median migration change was 6 added lines for prompt-only applications and nearly 700 added lines for fine-tuned applications. These are study results, not estimates for a particular app or a universal measure of effort. The study also found that 8% of the analyzed migrations switched providers; that figure does not show whether staying or switching is better for your team. 2026 study on LLM application migration
The practical implication is to check both the obvious model ID and the less visible dependencies: prompt formats, tool schemas, fine-tuning workflows, SDK behavior, and assumptions in your monitoring or deployment code. Changing a name may be enough for a simple integration, but it is not a safe assumption for a more customized one.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What happens to prompts, logs, and fine-tuned assets?
Do not assume data remains accessible or exportable after shutdown. The lifecycle pages cited here do not establish a general post-shutdown access guarantee for prompts, logs, fine-tuned weights, or other application data. The answer depends on the service’s terms and export procedures. Confirm what you can retrieve and test exports while the service is still available.
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