# Model-Agnostic Architecture

Source: https://customlabs.io/glossary/model-agnostic-architecture/
Updated: 2026-09-13

Foundations

# Model-Agnostic Architecture

A model-agnostic architecture lets an application swap or route between LLM providers without a rewrite.

It protects against price changes, deprecations, and capability shifts from any single vendor.

It also lets different workloads route to whichever model is cheapest or best suited.

It is a deliberate design decision, not a default most teams start with.

Most teams retrofit it only after their first vendor lock-in scare.

[Insight: Model-agnostic by design](https://customlabs.io/insights/model-agnostic-by-design/)[Case study: Model-agnostic routing cut inference spend](https://customlabs.io/case-studies/model-agnostic-routing-cut-inference-spend/)

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## Related terms

[Inference Cost Inference cost is what it costs to run a trained model on a request.](https://customlabs.io/glossary/inference-cost/)[Model Deprecation Model deprecation is a provider retiring a model version on its own schedule.](https://customlabs.io/glossary/model-deprecation/)

## More in Foundations

[Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) retrieves relevant passages at query time and feeds them into an LLM's context.](https://customlabs.io/glossary/retrieval-augmented-generation/)[Embeddings Embeddings are numeric vectors that place similar content close together in vector space.](https://customlabs.io/glossary/embeddings/)[Context Window The context window is the maximum text, measured in tokens, a model can consider at once.](https://customlabs.io/glossary/context-window/)[Token A token is the basic unit of text an LLM reads and writes.](https://customlabs.io/glossary/token/)