Best practices for S3 ingestion, Redshift access, multimodal content, and retrieval at scale

Many Amazon Bedrock implementations start with what looks like a straightforward goal: connect structured and unstructured data sources, make them accessible to a foundation model, and enable natural language interaction. What becomes clear very quickly is that the quality, reliability, and cost of the solution depend heavily on decisions made at the data layer (not in the model or prompt design).

Cloud operators, founders, and IT leaders share how they're managing cloud storage costs. From data-driven access audits to lifecycle automation to the forgotten capacity reservations hiding outside the standard cost report, here's what's working... and what's not.
 

In today’s cloud-first world, designing infrastructure that is both secure and cost-efficient is a top priority for any organization building on AWS. Whether you're launching a startup, modernizing legacy systems, or scaling enterprise workloads, getting the architecture right from the start is critical.

Managing large datasets is not only an operational issue, but a financial one as well.

S3, Athena, and Glue, in particular, provide a reliable solution to handling ballooning costs.

Achieving long-term cost optimization as a business scales may mean moving to other AWS services.