Load Balancing

Load balancing for AI distributes incoming inference requests across multiple instances of a model or across different backend services. This ensures that no single instance is overwhelmed while others sit idle, optimising both performance and resource utilisation. AI workloads present unique load balancing challenges. Requests vary dramatically in processing time (a simple classification might take milliseconds, while a long text generation might take seconds). Some requests require specific hardware (GPUs with enough memory for large models). Streaming responses require persistent connections that differ from typical HTTP request-response patterns. Load balancing strategies for AI include round-robin (distributing requests evenly), least-connections (routing to the instance handling the fewest requests), weighted distribution (sending more traffic to more capable instances), and content-based routing (directing different request types to different model variants).

Load balancing directly impacts the reliability and performance of AI services. Without proper load balancing, an AI application may deliver inconsistent response times, experience outages during traffic spikes, or waste expensive GPU resources on idle instances. For production AI systems, load balancing enables high availability — if one instance fails, traffic is automatically redirected to healthy instances. This is critical for customer-facing applications where downtime directly impacts revenue and satisfaction. Load balancing across multiple AI providers or models enables cost optimisation and resilience. Requests can be routed to the most cost-effective provider for each query type, and if one provider experiences issues, traffic automatically shifts to alternatives.