AWS Generative AI Services

According to AWS documentation, Amazon Bedrock does not use your prompts and completions to train AWS or third-party base models, and does not share them with model providers. Data is encrypted in transit and at rest, and you can use AWS PrivateLink to keep traffic off the public internet. Model invocation logging is optional and under your control. We review these settings with your security team during design.

Bedrock Agents provide a managed, configuration-driven way to build agents with action groups and knowledge bases, suitable for many straightforward use cases. Amazon Bedrock AgentCore is a set of services for deploying and operating agents built with any framework or model, offering runtime isolation, memory, tool gateways, identity, policy controls and observability. We often recommend AgentCore for complex or multi-agent systems that need more control.

There is no single best model. Larger models handle complex reasoning and long documents better, while smaller models are faster and cheaper for classification, extraction or routing. We compare several Bedrock models on your evaluation set, measuring quality, latency and cost per task. Many solutions combine models, using a capable model for hard requests and a smaller one for routine work.

We ground answers in your content and require citations, limit the assistant’s scope, and configure Bedrock Guardrails for content filtering, denied topics, personal data redaction and contextual grounding checks. Agents receive narrowly scoped permissions and approval steps for consequential actions. Before launch we evaluate quality and safety on a test set, and in production we monitor outputs and user feedback.

Costs consist mainly of model inference, priced per token or through provisioned throughput, plus vector storage, retrieval, compute, logging and monitoring. Token usage depends on traffic, prompt length and retrieved context. We estimate cost per transaction during the prototype and optimize with prompt caching, intelligent prompt routing, batch inference for offline jobs and smaller models where they meet quality targets.

A focused prototype measured against an evaluation set typically takes four to six weeks. Production hardening, including security review, integration, user testing and monitoring, usually adds several more weeks depending on complexity and approval processes. Multi-tool agents take longer. Throughout the project we send weekly written updates and demo working software every sprint.