Why write an exit plan before there's a problem?

Vendor lock-in is cheapest to avoid before you are deep in it. Designing an abstraction layer and confirming export rights up front means a switch is an engineering task rather than a crisis, whether the trigger is price, an outage, or a policy change.

What is an API abstraction layer and why does the plan emphasise it?

It is a thin internal interface your code calls instead of the provider's SDK directly. With it, swapping providers means changing one adapter rather than every call site. Building it early is the single highest-leverage step for portability.

Why does the plan include data deletion confirmation?

Leaving a provider is not complete until your data and any fine-tunes are deleted and you have written confirmation. Under GDPR and most contracts you have a right to this, and it limits residual risk and cost.

Does the same plan work for any provider pair?

The structure does. Prompt formats, tokenisation, tool-calling schemas, and fine-tune portability differ between providers, so the model-migration section always needs provider-specific detail you fill in.

Is fine-tuned model migration always possible?

Rarely directly. Fine-tunes are usually provider-locked, so migration typically means re-tuning on the new provider from your retained training data. Keeping that data and its documentation is why the export step comes first.

AI Vendor Exit Plan Template

AI vendor exit plan template

Depending on one AI provider with no way out is a business risk: prices change, models get deprecated, terms shift, and outages happen. An exit plan turns “we’re stuck” into “we switch in N weeks.” This generator produces a structured, dated migration plan covering the parts teams forget — export rights, an API abstraction layer, parallel running, cutover, and written confirmation that your data was deleted.

How it works

You enter your current provider, the target provider, and a timeline in weeks. The tool lays out the standard phases of a clean exit: secure data and model export rights, build or verify an abstraction layer so provider calls are centralised, port prompts and tool schemas to the new provider, run both in parallel and compare outputs, cut over behind a flag, then confirm deletion of data and fine-tunes at the old provider. Each phase is placed on a proportional timeline and rendered as Markdown you can copy straight into a runbook and assign owners.

Tips and notes

Build the abstraction layer first. It is what makes every future switch cheap; without it, migration touches every call site.
Keep your training data. Fine-tunes rarely port — you re-tune on the new provider from data you must already hold.
Don’t skip deletion confirmation. The exit isn’t done until you have it in writing; it’s both a GDPR right and a risk reducer.
Run in parallel before cutover. Comparing outputs on real traffic catches quality regressions the docs won’t warn you about.