"How portable is what you're building for us" is a fair question to ask any AI vendor or engineering partner, and the honest answer usually has more nuance than a simple yes or no. Some parts of an AI system are naturally portable. Some parts carry real switching costs no matter how the system is architected. Knowing which is which lets you make a genuinely informed decision instead of just hoping for the best.
What's actually portable by design
- Application code and business logic that doesn't touch the model directly. This is standard software, exactly as portable as any other codebase you own outright.
- Data and knowledge base content, assuming it's stored in an open, standard format rather than locked into a proprietary vendor-specific structure with no export path.
- Prompts and agent configuration, if they're written to be reasonably model-agnostic and version-controlled as part of your own codebase, rather than embedded in a vendor's proprietary tooling with no clean export.
What carries real, unavoidable switching cost
- Model-specific behavior tuning. A system tuned carefully against one specific model's particular quirks and strengths will behave somewhat differently on a different model, even with the same prompt. Some re-tuning is a real cost of switching that no architecture eliminates entirely.
- Fine-tuned models, if fine-tuning was part of the approach, are inherently tied to the base model and provider they were built on top of. This is a genuine, structural lock-in point worth being clear-eyed about before choosing that approach.
- Deep integration with a provider's proprietary tooling, such as agent frameworks or orchestration layers that only work within one vendor's ecosystem. This trades faster initial development for real migration cost later.
What actually determines how portable your system is
The honest answer is: mostly architecture decisions made early, not something you can retrofit cheaply later. The core idea is the same one that shows up in pricing volatility and rollback planning: an abstraction layer between your application and any specific model provider is what turns a forced migration into a configuration change instead of a rewrite.
The honest tradeoff, not a false promise
Full portability with zero switching cost isn't a realistic target for any real AI system, and a vendor who claims otherwise is oversimplifying. The realistic target is minimizing unnecessary lock-in (proprietary formats, vendor-specific tooling with no export path) while being honest that some genuine switching cost (from model-specific tuning and fine-tuning decisions) is often a reasonable tradeoff for better performance today. It isn't automatically a mistake to avoid at all costs.
How we approach this
We build with a clear, honest distinction between what's genuinely portable by design and what carries real switching cost, We architect for portability wherever it doesn't cost meaningful performance, so you own a system you can actually reason about, rather than one whose portability is an unverified claim.