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Load balancing distributes traffic across multiple servers, preventing any single server from becoming a bottleneck. When a request arrives, the load balancer decides which server should handle it (using algorithms like round-robin, least connections, or hash-based). This enables handling more traffic and provides resilience: if one server fails, others handle traffic.
Load balancers can be software (running on servers) or hardware (dedicated appliances). They can operate at layer 4 (TCP/UDP, transport level) or layer 7 (HTTP, application level). Layer 7 balancing is smarter: it understands HTTP and can route based on request content. This enables advanced routing strategies like geo-location, session affinity, or request type.
Load balancing is essential for scalable systems. Without it, you're limited by single-server capacity. With load balancing, you scale by adding more servers. Modern cloud platforms (AWS ELB, Google Cloud Load Balancing, Azure Load Balancer) provide managed load balancing, abstracts away operational complexity.
Groovy Web implements load balancing across our AI infrastructure, ensuring even distribution of inference requests. Load balancing enables scaling to handle traffic spikes in our AI-First products.
Our AI-First engineers build production systems using Load Balancing technology. Talk to us.
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