| | | | | | | | Today's edition is sponsored by AWS Marketplace, on a choice a lot of you are making right now: which vector database to build on. | | Three AI patterns, one building block: | | RAG grounds your model in your data, not its training set. Agentic memory carries context across sessions. Semantic search matches intent, not keywords.
| | Each one runs on a vector database, and each puts different demands on it. So the right pick depends on your architecture, not on a feature list. | | AWS Marketplace put together an evaluation guide to help you choose: | | Architecture diagrams: each pattern mapped with Amazon Bedrock and Amazon Bedrock AgentCore. Evaluation framework: hybrid search, scaling, hosting trade-offs, embedding pipeline fit, and metadata filtering. Free trials: test vector databases in AWS Marketplace against your own workload.
| | Get the evaluation guide → | | | Why it's worth a look: |
Tools in AWS Marketplace are designed to plug into your AWS workflows. Evaluate fast with free trials and developer-tier pricing, avoid upfront license fees, and pay only for what you use, billed through your AWS account. | | If you're picking a vector database this quarter, start with the architecture. | | Get the evaluation guide → | | | Louis, Laura, and the ☕️ Techpresso team. |
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