Performance optimization in scalable web-based AI applications involves maximizing hardware utilization with TensorFlow.js by managing tensor lifecycles, selecting appropriate backends like WebGL or WebGPU, batching inputs, profiling memory and execution, and applying model quantization and pruning for efficient inference and reduced resource consumption.