Inference

The inference step is where the magic happens – user inputs are actually run through the AI models, either running locally or in the cloud, to generate outputs. Seamlessly orchestrating this prediction stage requires some key technical capabilities.

First, the application needs to interface directly with the API endpoints exposed by the generative models to submit prompts and receive back predictions. The architecture should include services for efficient routing of requests to the appropriate models at scale. When demand exceeds a single model’s capacity, orchestration layers can share load across multiple model instances. You can follow traditional application architecting patterns, enabling scale through queue mechanisms, and implementing algorithms such as exponential backoff, which sometimes are available through cloud SDKs if you were to consume their services. It is always a good idea to evaluate common API consumption patterns and explore the tradeoffs...