Overview

In today’s fast-evolving AI landscape, new models emerge daily, pricing structures change, and performance improvements happen constantly. Building applications tightly coupled to a single LLM provider creates technical debt and limits your ability to adapt.

This guide explores an architecture pattern that allows you to easily switch between different LLM providers like OpenAI (ChatGPT), Anthropic (Claude), and Google (Gemini) without rewriting your entire codebase. You’ll learn how to design flexible systems that are testable, maintainable, and future-proof.

The Problem: Tight Coupling

Like many developers, I typically start with direct integration:

Diagram showing an application directly calling a single LLM provider, illustrating tight coupling to one AI service

This approach works initially, but creates several problems:

  • Vendor lock-in - You’re dependent on one AI company’s availability and pricing
  • Difficult migrations - Changing providers means rewriting code throughout your application
  • Hard to test - Testing requires real API calls, making unit tests slow and expensive
  • No fallback options - If the AI service goes down, your application breaks
  • Limited flexibility - Can’t easily compare different models or use the best one for each task

The Solution: Interface-Based Architecture

The key is creating a common interface that all LLM providers must implement. This abstraction layer decouples your application logic from specific provider implementations.

Step 1: Define a Common Interface

First, define a shared contract that all LLM providers must follow:

Diagram of a shared LLM interface defining a common contract for multiple AI providers

This interface typically includes methods like generate(), stream(), and embeddings() that every provider must implement.

Step 2: Implement Provider-Specific Adapters

Create concrete implementations for each AI provider:

Diagram showing multiple LLM provider implementations (OpenAI, Anthropic, Google) conforming to a shared interface

Each adapter handles provider-specific details like authentication, request formatting, and error handling while exposing the same interface.

Step 3: Build Your Application Service

Your application service depends only on the interface, not concrete implementations:

Architecture diagram of an application service using interchangeable LLM providers through a common abstraction

This means you can swap providers by changing which adapter you inject, without modifying your business logic.

Step 4: Add Configuration-Based Selection

Keep provider settings in a configuration file for maximum flexibility:

Configuration-based LLM selection using a settings file to switch providers without changing application code

Now you can change providers by simply updating a config file - no code changes required.

Testing Benefits: This architecture simplifies testing significantly. You can create mock provider classes to simulate API responses, enabling thorough unit testing without making live API calls or incurring costs.

Advanced Patterns

Once you have the basic swappable architecture in place, you can implement sophisticated patterns that leverage multiple providers simultaneously.

Task-Based Provider Selection

Route different types of tasks to the most appropriate provider:

Task-based LLM selection where different AI providers are chosen based on the type of task

For example: use Claude for complex reasoning, GPT-4 for creative writing, and Gemini for multimodal tasks. Your application selects the optimal provider based on the task type.

Multi-LLM Consensus

Get responses from multiple providers and synthesize them for higher-quality results:

Multi-LLM consensus pattern combining responses from several AI providers to produce a final answer

This pattern works well for critical decisions where you want multiple “opinions” before proceeding.

Fallback Chain

Implement automatic failover when a provider is unavailable:

Fallback chain architecture where requests move to another LLM provider if the primary one fails

Your application tries providers in order until one succeeds, ensuring high availability even during outages.

A/B Testing Providers

Compare different providers in production to make data-driven decisions:

A/B testing setup comparing responses from different LLM providers to evaluate quality and performance

Track metrics like response quality, latency, and cost to optimize your provider selection over time.

Benefits of This Architecture

Flexibility and Control:

  • Easy switching - Change AI models without modifying application code
  • Multi-provider strategy - Use different providers for different tasks based on their strengths
  • Backup plans - Automatic failover if one provider is down or rate-limited
  • Cost optimization - Choose providers based on price-performance ratio for each use case

Quality and Reliability:

  • A/B testing - Compare responses from different providers to find the best match
  • Consensus patterns - Combine multiple models for higher-quality outputs
  • Gradual migrations - Test new providers in production with minimal risk

Development Experience:

  • Testability - Mock providers for fast, free unit testing
  • Future-proof - Easily add new providers as they emerge
  • Reduced technical debt - Clean abstractions prevent vendor lock-in

Summary

In today’s rapidly evolving AI landscape, building applications that can easily switch between different LLM providers isn’t just a nice-to-have - it’s essential for maintaining competitive advantage and operational flexibility.

Key takeaways:

  • Use interfaces/abstractions to decouple your application from specific providers
  • Implement adapters for each provider while maintaining a common contract
  • Leverage configuration to change providers without code changes
  • Build advanced patterns like fallbacks, consensus, and A/B testing for production resilience

Start with the basic interface pattern, then add advanced patterns as your needs grow. Your future self will thank you when the next breakthrough model launches or pricing changes force a migration.

Looking for more architecture content? Check the architecture tag for related posts.