Building AI Applications on AWS: How to Balance Performance, Scalability, and Cloud Costs
An AI feature that performs well during testing can become expensive or frustratingly slow once customers start using it. Every additional request consumes resources, and a model that delivers excellent results may still be unsuitable if its response times or operating costs don’t match your product.
Amazon Bedrock makes it possible to build applications using foundation models without managing the underlying model-serving infrastructure. However, choosing a model is only one part of the process. You also need to consider how requests are processed, how your application handles growing demand, and what each customer interaction costs.
For startups and SaaS companies, these decisions can determine whether an AI feature remains financially sustainable as usage increases.
Start With Your Application’s Performance Requirements
Before choosing a model or infrastructure configuration, define what your AI application needs to accomplish.
A customer support assistant and a document-processing system might use similar foundation models, but their...
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