Your team just got approval for an AI project. Now comes the hard part: picking the provider.
OpenAI? Anthropic? Google?
Everyone has opinions. Benchmarks get pulled up. Cost comparisons get made. The pressure builds because this feels like a huge decision.
Architecturally, it should not be a permanent one.
Six months from now, there will almost certainly be better options. A competitor will launch something faster. Prices will shift. Capabilities will change.
If switching providers breaks your solution, you didn’t build an AI system. You built a dependency.
The Lock In Nobody Planned For
This pattern shows up in a lot of early AI implementations.
A team builds a customer service AI directly on OpenAI’s API. It works well. Workflows are built around OpenAI’s specific API structure. Prompts are tuned to GPT’s behavior. Errors are handled the way OpenAI returns them. The system rolls out company wide.
Later, another provider starts outperforming GPT for their specific use case. And at a lower cost for the volume they are running.
They want to switch. But doing so would require significant rework.
Their codebase calls OpenAI endpoints directly. Authentication is OpenAI specific. Error handling assumes OpenAI’s error codes. Prompts are designed around GPT’s response patterns.
Switching providers would mean rewriting large portions of the integration, rebuilding error handling, and retuning prompts for a different model’s behavior.
So they stay put. Paying more for a model that is no longer the best fit for their needs.
This is not because they picked the wrong provider. It is because they built the wrong way.
They treated the AI provider like fixed infrastructure instead of what it actually is: a replaceable service. Business logic and provider specific code became tightly coupled. Assumptions about one model’s quirks were baked into the system.
Now they are locked in. Not by contract. By code.
Meanwhile, competitors who built more flexible, model agnostic AI solutions can take advantage of better models and better pricing as the landscape shifts. That gap compounds over time.
What Model-Agnostic Actually Means
Another product team learned this the hard way.
They built a content generation tool directly on Anthropic’s API. It worked beautifully. Later, they needed to add a feature that GPT handled better.
They could not simply swap models for that feature. Their system assumed Claude’s API structure. Parameters were named differently. Response formats did not match. Error handling meant different things.
Supporting a second provider would have meant maintaining two separate integrations throughout the codebase.
Instead, they rebuilt the architecture.
They introduced an abstraction layer between their business logic and whichever provider was running underneath. That layer translated requests into provider specific formats, standardized responses for their defined use cases, and handled authentication and error differences.
Now, when they want to use a different provider, they update the abstraction layer. Their business logic does not care whether OpenAI, Anthropic, or Google is running underneath.
Recently, they switched providers for certain content types. Not because the original model got worse. Because another model got better at that specific task. The change was measured in hours rather than weeks.
That is what model-agnostic architecture looks like. Boring provider swaps. No rebuilds. No drama.
Where the Real Value Lives
Here is what most companies miss: the AI provider is not your asset. Your business logic is.
That customer service AI is not valuable because it uses a specific model. It is valuable because it understands:
- Which customer questions route to which teams
- How your product catalog and pricing rules work
- Your policies and exception handling
- Which responses resonate with your customers
- How to integrate with your ticketing system and CRM
That is the real value. The model is just the service performing language processing.
Many AI implementations bury that business logic inside provider specific code. Prompts are tuned to one model’s behavior. Exceptions are handled based on one provider’s error codes. Business rules and provider integration become inseparable.
Then switching providers means rebuilding everything.
Do it right and your business logic lives in its own layer. Your rules engine stays intact. Your integrations do not change. Your semantic understanding of your data persists. An abstraction layer translates between your logic and whichever AI service you are using today.
Switch providers and nothing fundamental breaks. Only the service underneath changes.
Why This Matters Right Now
Six months ago, GPT was the clear choice for many use cases. Today, the answer depends on the workload.
Some teams see stronger analytical performance from Claude. GPT-4o can be faster and more cost effective at scale. Gemini handles certain multimodal scenarios differently. Open source models continue to improve for teams that want to self host.
And this will keep changing.
The companies winning with AI are not the ones who guessed right last year. They are the ones who can change their answer as the landscape shifts.
Teams that built model-agnostic AI solutions from the beginning have already switched providers as better options emerged for specific tasks. Costs dropped materially. Capabilities improved. The switches themselves were uneventful.
Their competitors who built directly against a single provider’s API are still stuck. Paying more. Getting less. Unable to adapt.
That is the advantage of treating AI providers like interchangeable services.
The Questions That Actually Matter
When someone pitches you an AI solution, ask this:
What happens when we want to use a different AI provider?
If the answer is hesitation, you are looking at a dependency.
Ask where your business logic lives. If it makes direct API calls to a single provider, switching will be painful. If there is a clean abstraction layer, switching is manageable.
Ask them to show how the solution works with multiple providers. Teams that have already done this can explain it clearly. Teams that have not usually cannot.
If Swapping Providers Is Painful, Something Was Built Wrong
This sentence should worry you: “We can’t switch AI providers without rebuilding.”
It means you are locked into current pricing. You cannot take advantage of better options. You are vulnerable to provider changes. And you are paying the switching cost later instead of designing for flexibility now.
Some companies realize this after they are stuck and rebuild. It is expensive, but it restores control.
Smarter companies design for change from the beginning. They assume today’s best option will not stay best. They build accordingly.
How We Build AI That Survives Change
We start with your business logic, not with a provider.
We define your rules, processes, and exceptions first. That becomes the asset that is specific to you. Then we choose the provider that makes sense right now for the task at hand.
When better options emerge, you benefit immediately. Your logic persists. Your integrations persist. Only the service underneath changes.
Provider changes should be boring.
If they are not, someone built the wrong thing.
When you are ready to build AI that survives the next provider shift, we’re ready to help.
Get in touch with a P3 team member