What is a primary advantage of using a Gated Recurrent Unit (GRU) in Recurrent Neural Networks for business applications?

GRUs simplify the gating mechanism by combining the reset and update gates into one operation, which reduces the number of parameters compared with LSTMs. This makes training faster and less memory‑intensive, so businesses can deploy real‑time forecasting models on smaller servers or in cloud environments with lower cost. Because GRUs still capture long‑term dependencies, they can accurately predict trends like monthly sales or inventory demand without the heavy computation of a full LSTM. For instance, a retailer can use a GRU‑based model to forecast next‑quarter revenue in minutes, enabling quicker pricing or stocking decisions. The result is a leaner model that delivers comparable accuracy while cutting inference time and hardware requirements.