Project
A managed AWS Bedrock service that stores and indexes data for retrieval-augmented generation, letting generative AI apps query relevant context.
JFrog has documented an on-behalf-of exchange so agent calls reach Artifactory as the signed-in user. The price is the Gateway's cached tool search, which per-user discovery gives up.
Reality
TwelveLabs Marengo Embed 3.0 is generally available as an embedding model in Amazon Bedrock Knowledge Bases. The managed ingest does frames, transcription and vectors, and its 4-second segmentation default sets your retrieval granularity.
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AWS's comparison of the three customer-managed backends credits OpenSearch with in-memory speed and S3 Vectors with sub-second queries at up to 90 percent lower storage cost. The Aurora spec sets a ceiling your embedding model has to fit under.
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Agentic retrieval turns one question into an unknown number of lookups, and each is a place isolation can fail, which is why the interesting line in AWS's design is the one still leaving per-user isolation with your application.
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A small Bedrock model sits between the retriever and the answer call and strips text the query does not need. Whether that pays comes down to two numbers AWS cannot supply for you.
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A new Bedrock AgentCore pattern from AWS treats the agent as an orchestrator with no say over access, pushing per-user enforcement down to DynamoDB, Knowledge Bases and Salesforce.
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AWS published the build pattern most agent demos skip: isolation and cost attribution scoped per customer, not per application. Axonius chose silo and kept its existing tenant plumbing.
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