Generative 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.

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 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 SageMaker automates ML training, but instance costs add up fast. This guide covers spot instances, instance selection, distributed training, and production patterns to reduce SageMaker costs by 50-70%.

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.

Amazon Q for Business is a generative AI assistant for enterprise search and document retrieval. This guide covers setup with SharePoint and S3 data sources, user management, and production deployment patterns.

Build multi-step AI pipelines visually with Amazon Bedrock Flows. We compare it to Step Functions and custom Lambda orchestration with a decision matrix for enterprise teams.

Amazon Bedrock Data Automation replaces fragmented Textract + Comprehend + Lambda pipelines with a managed intelligent document processing service. Production guide.

On June 16, 2026, S3 Vectors raised the QueryVectors limit to 10,000 results per query and cut data-processed charges up to 80% on indexes over 10M vectors. Architecture, pagination, and cost comparison vs OpenSearch and MemoryDB.

Deploying GenAI without guardrails is a compliance incident waiting to happen. Here is how to build a production-grade AI governance layer on AWS using Amazon Bedrock Guardrails, least-privilege IAM, and continuous evaluation.