Batch inference is designed for scenarios with high volumes of AI requests that don't require immediate responses, such as processing support tickets or document extractions. This method can significantly reduce costs—up to 50% lower than real-time requests—by allowing multiple requests to be processed asynchronously. DigitalOcean's batch inference integrates smoothly with OpenAI and Anthropic models, facilitating efficient handling of non-interactive workloads without affecting real-time traffic limits.