Building Provider-Agnostic AI to Avoid Lock-In

I build on Claude by default, and I say that plainly because most conversations about vendor lock-in start with a false choice. People assume you either commit hard to one provider and accept that you are trapped, or you stay provider-neutral and give up the good parts. Neither is true. You can pick the best tool for the work and still design so that moving is a project, not a rewrite.

That is the whole argument of this post. Being Claude-native is a preference I hold because it produces better work for the things I ship. It is not a cage. The cage is built later, quietly, through a hundred small decisions that couple your product to one API in ways nobody wrote down. Lock-in is rarely a decision. It is an accumulation.

So the goal is not to avoid depending on a provider. You will depend on one, and you should, because depth beats breadth when you are trying to move fast. The goal is to keep the seams clean enough that the day you need to switch, or add a second model, or negotiate from a position of strength, you can. Let me walk through what that actually looks like.

What LLM lock-in actually looks like

Lock-in with language models is not one thing. It is a set of couplings that each feel reasonable in isolation and add up to a product you cannot move.

The most obvious is prompt coupling. Prompts are not portable by nature. A prompt tuned for one model’s quirks, its formatting habits, the way it responds to system instructions, will not behave identically on another. If your prompts live scattered through your codebase as inline strings, tuned by trial and error against one model, you have written a large body of work that only runs in one place.

Then there is SDK coupling. You import one provider’s client library, you call its methods, you shape your code around its request and response objects, its streaming format, its error types. That library becomes load-bearing. Ripping it out touches every file that ever talked to the model.

Next are the proprietary features, and these are the seductive ones. Provider-specific tool-calling formats. A particular structured-output mode. Prompt caching that works one way here and another way there. Fine-tunes trained on one provider’s base model that do not transfer. Each of these is genuinely useful, which is exactly why they create the deepest coupling. You adopt them because they make your product better, and in doing so you tie a knot.

The quietest one is data gravity. Your embeddings were generated by one provider’s model and sit in a vector index. Your retrieval system was tuned against those vectors. Your evaluation history, your logs, your captured examples all assume one model’s behavior. Moving is not just swapping an API key. It is re-embedding a corpus and re-tuning retrieval, and that cost is what keeps most teams put even when they have good reasons to leave.

Why total provider-neutrality is a trap too

Here is where a lot of well-meaning engineering goes wrong. The reaction to lock-in is to build for the lowest common denominator, to only use features that every provider supports, to abstract so aggressively that no provider-specific capability ever touches your code. That feels safe. It is a mistake.

When you build to the common denominator, you deliberately leave the best features on the table. The whole reason a provider builds prompt caching, or a strong tool-calling format, or a fast structured-output mode is to make hard things easier. If your architecture forbids using any of them because they are not universal, you have paid the full price of a frontier model and thrown away the parts that justified it.

You also slow yourself down, which is the cost nobody counts. A neutral abstraction has to be designed, maintained, and reasoned about. Every new capability a provider ships has to be evaluated against your lowest-common-denominator interface and usually rejected, because adopting it would break the neutrality you spent so much effort building. You end up with a product that is portable and mediocre, which is the worst of both worlds. Portable to what? Another mediocre implementation.

Some coupling is a fair price for capability. I will happily use a provider-specific feature if it makes the product meaningfully better, as long as I know where that coupling lives and what it would cost to undo. That is the difference between coupling you chose and coupling that happened to you. Aim for portability by design, not neutrality by default.

The abstraction layer that isolates provider calls

The single most useful structural decision is to put a thin layer between your application and any model provider. Not a heavy framework. A boundary. Your application code asks for a completion, or a classification, or a structured extraction, in terms that describe the task. The layer behind that boundary decides which provider and model serves it, and translates to and from that provider’s specific API.

The point is that provider-specific code lives in exactly one place. When you want to add a second provider, you implement the same interface again behind the boundary. When you want to route different tasks to different models, you do it inside the boundary without touching the callers. This is the foundation of a multi-model architecture, and it is what turns “switch providers” from a rewrite into a configuration change.

The mistake people make here is over-abstracting the boundary until it only exposes what every provider shares. Do not do that. Let the layer pass through provider-specific features when a task needs them. The boundary’s job is isolation, not amputation. It should let you reach the good features while keeping the reach in one auditable place, so you always know what you are coupled to and can price the cost of leaving. A clean boundary is also just good LLM application architecture regardless of the lock-in question, because it makes testing, logging, and swapping components far easier.

Treat prompts and eval sets as assets you own

Your prompts are intellectual property. Treat them like it. That means they do not live as inline strings scattered through application logic. They live as versioned, named assets you can read, diff, and move. When a prompt is a first-class object rather than a literal buried in a function call, porting it to a new model becomes a tuning exercise instead of an archaeology project.

Your evaluation sets matter even more, and most teams underinvest here badly. An eval set is a collection of representative inputs paired with what good output looks like, plus a way to score model responses against that standard. If you own a real one, switching providers stops being scary, because you can run a candidate model through your evals and see, in concrete terms, where it is better, worse, or the same. Without evals, a provider switch is a leap of faith and everyone knows it, so nobody takes it.

The strategic point is that prompts and evals are the parts of your AI system that are genuinely yours. The model is rented. The infrastructure is rented. But the accumulated knowledge of what your product needs to say and how you measure whether it said it well, that is the durable asset. Building a serious evaluation practice is one of the highest-value things you can do, and I have written more about how it fits into shipping real software in building AI-native products.

Own your data, retrieval, and embeddings strategy

Data gravity is the coupling that quietly decides whether you can ever move. Address it directly.

Start with the raw data. Your source documents, your captured examples, your logs of what users actually asked and what worked, all of that should live in systems you control, in formats you can re-process. If your only copy of your knowledge is embedded into one provider’s vectors, you do not own your knowledge. You own a derivative of it that only that provider can regenerate.

Then treat embeddings as replaceable rather than permanent. Embeddings tie you to whichever model produced them, because vectors from different models are not comparable. The defense is not to avoid embeddings. It is to keep the source material and the embedding pipeline clean enough that you can re-embed the whole corpus with a different model when you need to. Re-embedding is real work, but it is bounded and repeatable work if you kept the inputs. It is impossible work if you did not.

Your retrieval layer should sit behind its own interface too, so that the vector store and the embedding model are implementation details rather than facts of life. When retrieval is a component you can reconfigure, switching embedding providers becomes a migration you schedule rather than a wall you hit.

Contract, pricing, and availability risk

So far this has read like an engineering post, but the strongest reasons to keep an exit are commercial, not technical.

Pricing changes. A provider can reprice, deprecate a model you built on, or move a capability behind a higher tier. If you have no alternative, you absorb whatever the new terms are, because your only counter-move is a rewrite you cannot afford. If you can credibly move, you negotiate from a real position, and even if you never leave, the option itself changes the conversation.

Rate limits and availability are the operational version of the same risk. Providers have outages. Providers throttle. Providers occasionally restrict access to models or regions with little notice. If your product goes down every time one provider hiccups, you have made a reliability decision without meaning to. The ability to fail over is not paranoia. It is the same discipline you already apply to databases and payment processors, applied to a dependency that happens to be newer.

There is also plain concentration risk. Betting the whole company on one vendor’s roadmap, terms, and uptime is a bet, and it should be a conscious one. I am comfortable being Claude-native because the work is better, but I am not comfortable being unable to move if the terms of that relationship change. Those are two different comfort levels, and keeping them separate is the point. If you want the fuller commercial framing, I go deeper in choosing an LLM for your business.

Keep a fallback provider you actually test

Almost everyone who worries about lock-in ends up with a fallback provider on paper. Far fewer have one that works. A fallback you have never run in production is not a fallback. It is a hope with a config entry.

The difference is testing. Your fallback path should be exercised regularly against the same evals as your primary, so you know exactly how quality shifts when it kicks in. It should be wired into your abstraction layer so that failing over is a switch, not a scramble. Ideally some real traffic flows through it periodically, so the path stays warm and you catch drift before an outage forces you to discover it live.

This costs something. Maintaining a tested second path is real ongoing work, and I will not pretend it is free. But the cost is bounded and known, which is the whole point. You are trading a small, predictable maintenance cost for the removal of a large, unpredictable one. That is a good trade for anything you actually depend on. If cost is the concern, note that a tested fallback also opens the door to routing cheaper work to cheaper models, which ties directly into LLM cost optimization.

How Claude-native and portable coexist

I want to close the loop on the tension I opened with, because the resolution is simple once the pieces are in place.

Being Claude-native means I default to the best tool for the work I do, and I use its strong features without apology when they make the product better. Being portable means every one of those choices sits behind a clean boundary, my prompts and evals are assets I own, my data can be re-embedded, and I have a fallback I have actually run. Those two things do not fight. One is about doing the best work today. The other is about not being hostage to that choice tomorrow.

The mental model I use is that depth and optionality live at different layers. At the capability layer, go deep. Use the best model, use its best features, tune hard against it. At the architecture layer, stay clean. Keep the seams visible, keep the couplings priced, keep the exit real. When those layers are separated, you get to be fast and free at the same time, which is the only combination worth aiming for.

A practical checklist for your lock-in exposure

Run your own product through these questions and be honest about the answers.

• Do all your provider calls go through one boundary, or is client code scattered across the codebase?
• Are your prompts versioned, named assets, or inline strings tuned by trial and error?
• Do you own a real evaluation set you could run a candidate model against this week?
• Do you keep source data in a form you could re-embed, or only as vectors one provider produced?
• Is your retrieval layer swappable, or fused to one vector store and embedding model?
• Which provider-specific features are you using, and do you know what each would cost to replace?
• Do you have a fallback provider, and when did you last actually run it under load?
• If your primary provider repriced tomorrow, is your response “negotiate” or “absorb it”?

You will not answer all of these perfectly, and you should not try to. The point is to know where your couplings are, decide which ones are worth their price, and remove the ones that are pure accident. That is the difference between lock-in you chose and lock-in that chose you.

The short version

• Lock-in is an accumulation of small couplings: provider-specific prompts, SDK dependencies, proprietary features, non-transferable fine-tunes, and data gravity from embeddings.
• Total provider-neutrality is its own trap. Building to the lowest common denominator throws away the best features and slows you down.
• Aim for portability by design, not neutrality by default. Some coupling is a fair price for capability, as long as you know where it lives.
• Isolate provider calls behind one clean boundary so switching becomes configuration, not a rewrite.
• Treat prompts and eval sets as assets you own. They are the durable part of an AI system.
• Own your data and keep embeddings replaceable, so re-embedding is bounded work rather than impossible work.
• The strongest reasons to keep an exit are commercial: pricing, rate limits, availability, and concentration risk.
• Keep a fallback provider you actually test. An untested fallback is a hope, not a plan.
• Being Claude-native and being portable coexist. Go deep at the capability layer, stay clean at the architecture layer.

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I am Deepanshu Grover, a Growth Product Manager and AI builder in Paris. If you want AI in your product without betting the company on one vendor, connect on LinkedIn or get in touch.