A shared AI interface can make a provider change look pleasantly small in the code. Change a setting, supply credentials and send the request somewhere else.
The response may still behave differently.
Two chefs can wear the same apron and have very different views on chili. A common SDK offers a consistent way to place the order; it does not make the result identical.
Laravel’s AI SDK, introduced in beta in February 2026, provides a timely example of provider abstraction.1 For a business using AI inside an application, that abstraction can be useful. A dependable handover also needs an inventory of what can move, a check of what will change and a controlled switch.
Define the portable part of the feature
Begin with the business function, independent of the current provider’s response format. That makes the intended behavior easier to preserve.
Give the application its own result format
Imagine a hypothetical system that extracts details from incoming equipment inquiries. The rest of the application expects a draft containing the event date, location, requested items and unresolved questions.
Define that result in the application’s own terms. Provider-specific responses can then be mapped into it. This keeps vendor details from spreading into every screen and business rule.
The mapping should preserve uncertainty and failure states. An absent date should remain absent, rather than quietly becoming an invented value to satisfy a required field.
Structured output can help enforce shape where supported. It still needs validation against the application’s requirements and an appropriate response when generation is refused or incomplete.
Inventory the parts that have a longer memory
A text-generation request may be relatively easy to redirect. Stored conversations, uploaded files and provider-managed search resources may require additional work.
Embeddings deserve particular attention. Vectors produced by different embedding models are not automatically compatible, even when their dimensions happen to match. A change may require generating a new index from the original material and assessing retrieval quality again.
Record which assets are stored with the provider, which originals the business retains and what can be recreated. Portability becomes more practical when that list exists before anyone needs to use it.
We build AI agents with clear jobs and sensible limits. See how.
Test the behavior that matters to the business
The candidate provider needs to handle the actual feature, including its awkward cases. A convincing general demonstration is a useful introduction, rather than an acceptance test.
Compare outputs on representative tasks
Use a set of appropriately protected examples with agreed expected behavior. For the inquiry extractor, include ordinary requests, missing dates, conflicting details and messages containing irrelevant instructions.
Check whether the application can use each result correctly. Also examine whether uncertainty is expressed sensibly and whether important information has been omitted.
A useful comparison records:
- Correctly extracted and incorrectly inferred details.
- Missing information that is appropriately flagged.
- Valid output structure and usable failure responses.
- Any consequential changes in tool selection or arguments.
- The relevant service limits and operational requirements.
Repeat important cases where variation would affect the decision. Keep the model versions, instructions and settings recorded so the comparison can be understood later.
If a second model grades the output, calibrate its judgments against human review. It can help with assessment, but it should not be the sole witness to its colleague’s brilliance.
Review instructions, tools and data permissions
Providers can differ in supported features and how instructions or tool calls are represented. Check those mappings explicitly.
A backup provider also needs to be approved for the information the application would send. Automatic failover is still a change in where the request goes. Its configuration should match the business’s actual data-handling decisions.
Retest authorization around tools independently of the model. The application should enforce access before exposing records or performing actions, whichever provider is currently answering.
Check out this blog: An AI agent needs a better finish line than “Done”.
Switch through a route you can observe
Start in a test environment. Where useful, compare candidate outputs against recorded inputs without allowing duplicate external actions. Keep the comparison separate from the live workflow.
For a controlled live rollout, define what will be monitored and what would trigger a return to the previous arrangement. Preserve the settings and necessary resources for that return, while accounting for any state created during the trial.
Staff handling the feature should know what has changed and how to report a questionable result. A different model may introduce a new kind of error even when the overall quality looks similar.
The useful outcome is a provider boundary the team understands: clean application interfaces, known dependencies and evidence that the alternative can do the intended job.
That gives the business real options. The key swap can remain a small technical step. It just should not be asked to carry the whole moving van.