What is model-agnostic architecture?
A design approach that does not tie AI applications to a single model provider, so different models can be swapped or combined based on needs and cost.
In detail
AI models evolve quickly, and their prices and capabilities change a great deal. Model-agnostic architecture separates business logic from any specific model and calls models through a unified interface, so switching models doesn't mean rewriting the whole application, and different tasks can use different models.
For example
Routine classification tasks use a low-cost small model, complex analysis tasks call a more capable large model, and the same application selects automatically by task.
How companies use it
When building AI applications, leave room to switch models, and build a set of test cases to validate results before any switch.
Common misconceptions
Over-engineering to “avoid lock-in”. Early on, choose one suitable model and deliver results, while keeping the option to change later.
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