Strategy Pattern

The Strategy Pattern defines a family of interchangeable implementations behind a shared contract. A caller delegates an operation to the selected strategy rather than embedding implementation-specific logic throughout the application. In a generative AI application, that contract might accept messages, generation settings, and request metadata, then return generated content, usage information, and a normalized completion status. Each strategy handles a supported model integration while the business workflow remains independent of the selected implementation.

A practical cloud architecture exposes a stable inference endpoint through an API gateway. A compute component receives the request, evaluates routing policy, and selects a strategy from a registry of approved implementations. External configuration maps workload names, tenant tiers, or task types to strategy identifiers and model settings. For example, summarization requests might use a lower-cost model, while complex reasoning requests use a model that has passed a separate quality evaluation. Changing that mapping switches subsequent eligible requests without modifying application code.

On AWS, Amazon API Gateway can provide the client-facing endpoint, AWS Lambda can execute the selection and invocation logic, and AWS AppConfig can distribute routing configuration. The application must retrieve or refresh configuration at runtime; merely storing configuration externally does not make selection dynamic. Configuration should identify approved strategies, model identifiers, supported Regions, timeouts, and generation settings. Secrets belong in a secrets-management system rather than in routing documents.

The strategy interface needs explicit capability boundaries. Models differ in context limits, tool calling, structured output, image support, streaming behavior, and safety controls. A request requiring tool use must not be routed to a strategy that cannot support it. Provider-specific adapters can translate payloads and errors beneath the shared contract. Amazon Bedrock Converse provides a consistent messaging interface for supported models, but it does not make every model equally capable or cover arbitrary external provider APIs.

Configuration-driven switching has limits. Switching between already implemented, authorized, and tested strategies can avoid a code deployment. Adding a provider with a different authentication mechanism or request format usually requires a new integration, tests, and deployment. The pattern is therefore not a promise that any future model can be adopted through a configuration change alone. It also differs from simple load balancing: selecting a strategy can change behavior and capabilities, not merely the destination serving an identical implementation.

Routing policy must preserve security and compliance requirements. Filter candidates by approved provider, data residency, model access permissions, and required safeguards before optimizing for cost or latency. A fallback must satisfy the same constraints as the primary strategy. Do not silently send sensitive prompts to an unapproved provider after a timeout. Bound retries and fallback attempts because another invocation can increase cost, repeat tool side effects, or generate a materially different answer.

The main trade-offs are integration complexity, abstraction maintenance, and possible loss of provider-specific features. Validate configuration before deployment, retain a last-known-good version, and roll out changes gradually. Track model quality, latency, error rates, token usage, and estimated cost by strategy and configuration version without logging sensitive prompt content unnecessarily. Contract tests check interface compatibility, while representative evaluations determine whether a replacement model is suitable for the actual task.