amazon-bedrock

Build production-ready AI agents on Bedrock with tool use, multi-step workflows, and supervisor patterns. From single agents to multi-agent orchestration.

Multi-agent supervisor pattern on Bedrock: architecture, implementation, and production deployment for scalable AI workflows.

AWS Nova models vs Claude: pricing comparison, performance benchmarks, and decision framework for choosing the right Bedrock model for your enterprise AI.

Choose between AWS Bedrock and OpenAI API for enterprise generative AI. Compare pricing, compliance, latency, and feature trade-offs.

Compare fine-tuning and RAG (retrieval-augmented generation) for customizing LLMs on Bedrock. Cost, latency, and accuracy trade-offs.

Production guide for HIPAA-compliant generative AI on AWS Bedrock — BAA scope, eligible models, Guardrails for PHI redaction, Knowledge Bases for RAG over clinical data, VPC isolation, and the audit evidence package OCR investigators expect.

Build SaaS with AI: multi-tenant architecture on Bedrock, cost isolation, and tenant data security.

The 20 AWS services reshaping enterprise architecture in 2024–2026: AI agents, vector storage, generative BI, distributed SQL, and security automation explained.

Amazon Bedrock Agents Classic automate workflows by giving foundation models the ability to call tools (APIs, Lambda, databases). This guide covers building agents with tool definitions, testing in the console, handling errors, and scaling to production.

Amazon Bedrock Knowledge Bases automate the RAG (Retrieval-Augmented Generation) pipeline — semantic search, chunking, embedding, and context injection into Claude or other foundation models. This guide covers setup, data ingestion, cost optimization, and production patterns.

Amazon Bedrock Guardrails protect foundation models from harmful outputs — filtering on prompt injection, jailbreaks, toxicity, and PII. This guide covers setup, testing, cost optimization, and production safety patterns for GenAI applications.

Bedrock billing is not a single line item — it is a composition of model invocation costs, Knowledge Base retrieval, Agent orchestration, Guardrails evaluation, and cross-region inference profile routing. Each component has its own pricing model and its own set of cost traps.