FinOps & i18n: Architecting a Cost-Effective Multi-Language AI Agent

Day 86. How to support 5 languages without doubling your LLM inference costs using DynamoDB caching.

Scaling a fintech application on a global scale presents a distinctive challenge when Generative AI is incorporated. While the translation of static user interface text incurs minimal costs, the on-the-fly translation of dynamic AI analyses can significantly erode profit margins.

Today, I enhanced my Serverless Financial Agent to accommodate multiple languages, including English, Spanish, French, German, and Italian. The frontend component identifies the language of the browser (navigator languages) and records the user’s preferred language in a DynamoDB database.

The Translation Caching Strategy mandates that altering the UI language should not initiate a comprehensive and costly financial recalibration. To address this issue, I devised a focused translation pathway. When a user transitions between languages, the React application transmits solely the current AI-generated message to the backend.

The backend system invokes Amazon Nova to translate the text a single time and subsequently caches the output in a DynamoDB table utilizing a SHA-256 hash derived from the target language and source text. Should the user swiftly alternate between languages, the backend provides an instantaneous cached response, thereby circumventing the entire LLM process.

Native AI Output for newly formed inquiries or the routine automated email reports entails the backend directly incorporating the preferred language into the Amazon Nova prompt (invoke_nova_ai()). The AI autonomously produces financial recommendations in the designated language while meticulously maintaining the integrity of merchant names and currency codes.

It is imperative to avoid reliance on unrefined LLM calls for repetitive translation endeavors. The implementation of rigorous cryptographic caching mechanisms is essential to safeguard your cloud expenditure.