Don't Build Your Application Around a Single LLM

When building an AI application, it's tempting to tightly integrate everything with one LLM provider.

It works initially.

But as the application grows, that decision can become an architectural constraint.

A better approach is to introduce an LLM abstraction layer between your application and the model providers.

                ┌── OpenAI
                │
Application → LLM Abstraction → Anthropic
                │
                ├── Gemini
                │
                └── Open-Source Models

Why does this matter?

🔹 Model availability You can switch models when a particular model is unavailable or deprecated.

🔹 Cost optimization Use an expensive model for complex reasoning and a cheaper model for simpler tasks.

🔹 Latency Different use cases have different latency requirements. Route requests accordingly.

🔹 Provider outages A fallback model can keep critical workflows running when a provider experiences an outage.

🔹 Model performance Different models perform better for different tasks. One model may be better for coding, another for summarization, another for reasoning.

🔹 Vendor lock-in Your business logic shouldn't become tightly coupled to one provider's API or model.

The key is to abstract capabilities, not just API calls.

For example:

generate_text()
generate_structured_output()
classify()
summarize()
extract()
embed()

Your application asks for a capability.

The abstraction layer decides which model should handle it.

This also makes it much easier to introduce:

• Model routing
• A/B testing
• Fallback strategies
• Cost-based routing
• Performance evaluation
• Model upgrades

Practical takeaway:

Design your AI application around capabilities, not around one specific model.

The model will change.

Your architecture shouldn't have to.

Tags: #Models, #AIEngineering

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