How to route LLM requests by cost vs. latency

DigitalOcean has introduced its Inference Router, a tool designed to optimize LLM request handling by dynamically routing traffic based on cost, latency, or task-specific requirements. Instead of relying on a single model for all operations, developers can define policies that match workloads—such as real-time chat or background batch processing—to the most efficient model available. The system supports fallback models to ensure high availability and includes cache-aware routing to maintain performance. By implementing these routing strategies, teams can reduce infrastructure costs and improve response times without building custom routing logic. The router integrates directly into existing workflows, allowing developers to manage model pools and selection policies through simple configuration. This approach helps balance the trade-offs between model quality, speed, and expense, providing a more scalable architecture for production-grade AI applications.