What This Guide Covers
AWS offers the broadest portfolio of AI and ML services of any cloud provider — more than 30 AI/ML services spanning pre-built AI APIs, managed ML platforms, foundation model access, and specialised domain services. This guide provides the complete enterprise reference: every service explained with its use cases, pricing model, integration patterns, and when to use it versus alternatives. A service selection decision framework maps business problems to the right AWS AI service, eliminating the confusion of navigating a portfolio this large.
Three tiers structure the guide: Tier 1 — Pre-built AI APIs (Rekognition, Comprehend, Textract, Transcribe, Polly, Translate, Forecast, Personalize, Kendra — zero ML expertise required); Tier 2 — Managed ML Platform (SageMaker with all its sub-services — for custom model development); Tier 3 — Foundation Models (Bedrock — for LLM-powered applications, RAG, and AI agents).
Amazon Bedrock — Foundation Model Access and Agent Infrastructure
Bedrock provides API access to frontier foundation models from Anthropic (Claude 3.5/3.7), AI21 Labs (Jamba), Cohere (Command), Meta (Llama 3), Mistral, Stability AI (image generation), and Amazon Titan (text, embeddings, image). Bedrock Knowledge Bases delivers fully managed RAG: connect S3, Confluence, SharePoint, or web crawl sources, and Bedrock handles chunking, embedding generation, vector storage (OpenSearch Serverless), and retrieval automatically. Bedrock Agents provides managed AI agent infrastructure with Action Groups (Lambda-backed tool invocations), Knowledge Bases (RAG retrieval), Guardrails (output safety filtering), and CI/CD promotion via Prompt Flow evaluation.
Topics Covered in This Guide
- Pre-built AI APIs — Rekognition, Comprehend, Textract, Transcribe, Polly, Translate, Forecast, Personalize, Kendra — use cases, pricing, integration
- Amazon SageMaker — Studio, Feature Store, Pipelines, Training, Experiments, Model Monitor, Model Registry, Inference — MLOps end-to-end
- Amazon Bedrock — foundation model catalogue, Knowledge Bases (RAG), Agents, Guardrails, Prompt Flow, fine-tuning, model evaluation
- Amazon Q — Q Business for enterprise knowledge retrieval, Q Developer for AI-assisted coding, Q for QuickSight BI
- Service Selection Framework — decision matrix mapping 25 common business problems to the right AWS AI service tier
- Pricing & Cost Management — pricing models per service, cost estimation patterns, RI/CUD for SageMaker and Bedrock provisioned throughput
- Enterprise Integration Patterns — VPC endpoint access, IAM roles, CloudWatch monitoring, CI/CD for ML models and Bedrock agents
Frequently Asked Questions
Brief Summary
The definitive field reference covering every AWS AI and machine learning service in one place — from Bedrock foundation models and AgentCore’s eight production modules to the landmark $50 billion AWS–OpenAI strategic partnership announced in February 2026.
Every service is mapped to concrete enterprise use cases, complete with cost frameworks, implementation steps, and production architecture patterns ready to apply from day one.
Whether you are choosing between SageMaker and Bedrock, deploying AgentCore at scale, or evaluating the new co-created Stateful Runtime Environment, this guide hands you the exact blueprint.
Extended Summary
Imagine having a single, crystal-clear reference that instantly maps every AWS AI service, reveals the real architecture patterns deployed by Ericsson, PGA TOUR, and Toyota, and decodes the $50 billion AWS–OpenAI deal announced February 26, 2026 — the largest AI partnership in history — including exactly what it means for your enterprise stack.
You will trace the precise path of a real enterprise banking deployment — a customer query entering Bedrock Knowledge Bases, passing three Guardrails policy gates, being delegated to specialist Strands sub-agents, and resolving with a grounded, regulation-ready response in under two seconds, with a complete immutable audit trail.
The guide then delivers the first complete technical breakdown of the AWS–OpenAI Stateful Runtime Environment — the jointly co-created infrastructure launching mid-2026 that eliminates the stateless prompt-rehydration problem forever, dramatically reducing agent development time from months to hours.
Close with a battle-tested five-phase enterprise deployment roadmap, a 20-item service selection matrix, and a smart model-routing strategy — 60% Haiku, 30% Sonnet, 10% Opus — proven in production to cut per-session AI costs by 70–80%. The complete AWS AI guide for 2026: every service, every architectural pattern, every enterprise decision — in one place.
