multi-model-routing

Multi-model routing distributes LLM requests to optimal providers based on cost and quality metrics. It evaluates provider pricing and response quality to route each task to the most cost-effective model available.

Provider fallbacks are supported to ensure continuous AI gateway operation. The system uses asynchronous model providers to chain fallback strategies, automatically switching to the next provider if the primary one fails.

Asynchronous model provider routing uses asyncio to handle concurrent LLM requests across multiple providers. This mechanism processes cost estimation and provider selection concurrently, ensuring high-quality responses without blocking operations.

Multi-model routing distributes tasks among various AI providers such as Claude Sonnet, GPT-4o, and Haiku. It selects the optimal provider by comparing cost and quality metrics to balance expense and response fidelity.

The routing system supports AWS integration through the boto3 dependency. This allows the asynchronous provider chains to interact with AWS services for cost estimation and model execution within your AI gateway.

Cost estimation is required for provider selection to optimize AI service routing. By evaluating cost and quality metrics before routing, the gateway ensures high-quality responses are delivered at a reasonable cost.