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.

What is the AI Vendor Exit Plan Template?

Generate a vendor exit plan template for migrating away from an AI provider — covering data export rights, model migration, API abstraction layer design, transition timeline, and data deletion confirmation requirements. It runs free in your browser on Gera Tools, with nothing uploaded.

AI Vendor Exit Plan Template

Name: AI Vendor Exit Plan Template
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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.

When to write an exit plan

The right time to write an exit plan is before you need one. The common triggers that force a switch — a price increase, a model deprecation notice, a provider outage, a policy change that affects your use case — give you limited time and maximum urgency. Writing the plan when things are calm means you already know:

What data you would need to export and whether you have the rights
How hard it would be to port your prompts and tool schemas
Whether your codebase is abstracted enough to change providers in weeks rather than months
What the likely quality delta is between providers for your specific use case

Teams that have done this discovery at leisure switch in weeks. Teams that have not switch in quarters, under pressure, with regressions.

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.

The abstraction layer: the single highest-leverage step

An API abstraction layer is a thin internal interface — one class, one module, one service — that your application calls instead of the provider’s SDK directly. All provider-specific logic lives in one adapter that implements that interface. To switch providers, you write a new adapter for the new provider and change one configuration value. Without it, every call site in your codebase needs to change.

The cost of building an abstraction layer is low when you set it up at the start of a project. The cost of retrofitting one to switch providers urgently is very high. This is the step most teams skip when things are working well and regret most deeply when they need to move.

What actually differs between providers

Prompt portability varies more than most teams expect. Common differences that require prompt revision when switching:

System prompt handling — some providers treat the system message differently or handle its interaction with user messages in subtly different ways
Tool / function calling schemas — the JSON schema for tool definitions varies between providers; not always directly portable
Context window and truncation — different effective context lengths can cause different behaviours on long inputs
Response formatting and verbosity — models from different providers may need different instructions to hit the same output format
Fine-tune portability — fine-tuned weights are provider-specific and cannot be transferred; you need to re-train from your original dataset

Building a robust evaluation set before the switch lets you measure quality on the new provider against your baseline before committing.

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.
Test on your real workload, not demos. Providers perform differently on different task types; your specific prompts and use cases are the only reliable benchmark.