Analytics

AWS Service / Feature	Description	Example Use Case
AWS Data Exchange	Managed service to find, subscribe to, and import third-party datasets directly into AWS data lakes and analytics workflows.	Subscribing to third-party financial or demographic datasets to enrich AI model training data.
Amazon EMR	Big data platform for processing massive datasets using open-source frameworks like Apache Spark, Hive, and Presto.	Running distributed ETL and preprocessing pipelines across terabytes of raw telemetry data before training models.
AWS Glue	Serverless data integration service that automates ETL workflows, data preparation, and metadata cataloging.	Automatically crawling S3 buckets to build a unified catalog and preparing structured data for ML training.
AWS Glue DataBrew	Visual data preparation tool allowing analysts to clean, normalize, and transform datasets without writing code.	Applying pre-built data cleaning recipes to handle missing values and deduplicate data prior to ML modeling.
AWS Lake Formation	Centralized service to quickly set up, secure, and govern data lakes with fine-grained row- and column-level access control.	Enforcing column-level permissions so data science teams can query data lakes without viewing PII.
Amazon OpenSearch Service	Managed search engine with vector database capabilities for high-performance vector similarity search.	Serving as the vector store for RAG applications to execute fast semantic search over document embeddings.
Amazon Quick	AI-powered assistant for business intelligence, research, business insights, automation, and dashboard generation.	Enabling business analysts to generate visualizations and ask natural language questions about sales data.
Amazon Redshift	Enterprise cloud data warehouse for fast, SQL-based analytical querying across petabytes of structured data.	Querying historical customer transactions to extract features for predictive churn models.

Cloud Financial Management

AWS Service / Feature	Description	Example Use Case
AWS Budgets	Cost management tool to set custom spending limits and receive automated alerts when thresholds are breached.	Setting an alert to trigger when monthly Amazon Bedrock token spend exceeds budget limits.
AWS Cost Explorer	Visual analytics tool to review, analyze, and forecast AWS spending patterns and usage trends over time.	Tracking daily spending trends across SageMaker endpoints and Bedrock models to detect cost anomalies.

Compute

AWS Service / Feature	Description	Example Use Case
Amazon EC2	Scalable virtual compute instances providing access to specialized GPU (g5, p5) and AWS silicon (trn1, inf2).	Hosting self-managed deep learning model training jobs or custom open-source inference runtimes.
AWS Lambda	Serverless compute service that runs code in response to events without provisioning or managing servers.	Serving as the backend execution tool for AI agents to invoke external APIs or database queries.

Containers

AWS Service / Feature	Description	Example Use Case
Amazon Elastic Container Service (Amazon ECS)	Fully managed container orchestration service for running Docker containers at scale.	Deploying containerized AI microservices and web APIs on serverless AWS Fargate compute.
Amazon Elastic Kubernetes Service (Amazon EKS)	Managed Kubernetes service for running containerized applications and distributed ML workloads.	Orchestrating multi-node distributed deep learning training clusters using open-source Kubernetes tools.

Database

AWS Service / Feature	Description	Example Use Case
Amazon Aurora	Cloud-native relational database (PostgreSQL/MySQL compatible) supporting pgvector for vector storage.	Storing transactional application data alongside vector embeddings for unified SQL and semantic search.
Amazon DocumentDB (with MongoDB compatibility)	Managed NoSQL document database supporting JSON data models and vector search capabilities.	Storing unstructured JSON user profiles and executing vector similarity search over text fields.
Amazon DynamoDB	Serverless, fully managed NoSQL key-value database delivering single-digit millisecond performance.	Storing real-time conversation history, memory state, and session contexts for AI agents.
Amazon ElastiCache	In-memory caching service (Redis/Memcached) for accelerating database reads and managing fast session state.	Caching frequent LLM prompt completions or vector search results to reduce response latency and token costs.
Amazon Neptune	Managed graph database service engineered to store and query highly connected datasets and knowledge graphs.	Querying entity relationships in knowledge graphs to enrich context for graph-augmented RAG applications.
Amazon RDS	Managed relational database supporting engines like PostgreSQL with pgvector extension capabilities.	Storing structured enterprise data while performing vector similarity search via pgvector.

Developer Tools

AWS Service / Feature	Description	Example Use Case
Kiro	AI-assisted development workspace tool designed to accelerate code generation and software development workflows.	Generating boilerplate software code and streamlining developer workflows within coding environments.
Strands Agents	Developer framework and runtime for building, securing, and orchestrating resilient multi-agent software workflows.	Coordinating specialized AI agents that collaboratively write code, execute unit tests, and review security flaws.
Amazon Q	Generative AI-powered assistant for work that helps developers, IT teams, and business users answer questions and generate code.	Asking architectural questions about AWS services or generating unit tests in an IDE via Amazon Q Developer.

Machine Learning

AWS Service / Feature	Description	Example Use Case
Amazon Augmented AI (Amazon A2I)	Managed service that coordinates human-in-the-loop review workflows for low-confidence ML predictions or AI responses.	Automatically routing low-confidence document extraction or sensitive LLM outputs to human operators for review.
Amazon Bedrock	Serverless API platform offering access to leading foundation models (FMs) from third-party providers and Amazon.	Building generative AI applications, text summarizers, and RAG systems without managing infrastructure.
Amazon Bedrock AgentCore	Managed runtime framework for building, executing, securing, and orchestrating multi-agent enterprise workflows.	Running production AI agents with built-in identity management, session isolation, and tool execution guardrails.
Amazon Comprehend	Natural language processing (NLP) service that extracts insights, sentiment, entities, and topics from unstructured text.	Analyzing customer review text to determine overall sentiment and categorize recurring product issues.
Amazon Lex	Service for building conversational AI interfaces (chatbots and voicebots) using natural language understanding (NLU).	Building automated voice and text chatbots for customer support routing and bank account inquiries.
Amazon Nova	Family of state-of-the-art foundation models delivering frontier intelligence, multimodal capabilities, and price-performance.	Generating multimodal assets and executing complex reasoning tasks at low latency and cost.
Amazon Personalize	Managed ML service that generates real-time personalized product recommendations, ranking, and content feeds.	Delivering tailored product recommendations on an e-commerce website based on past user behavior.
Amazon Polly	Text-to-speech (TTS) service that converts written text into natural, lifelike spoken audio across multiple languages.	Converting written news articles or blog posts into spoken audio for mobile app listeners.
Amazon Rekognition	Computer vision service that automates image and video analysis for facial recognition, object detection, and moderation.	Automatically scanning user-uploaded profile photos to detect and filter inappropriate content.
Amazon SageMaker AI	Comprehensive ML platform for building, training, fine-tuning, hosting, and monitoring custom models at scale.	Training, evaluating, and hosting a custom XGBoost model to predict customer credit risk.
Amazon SageMaker JumpStart	ML hub within SageMaker offering pre-built solution templates and one-click deployment/fine-tuning of open FMs.	Fine-tuning an open-weight foundation model on private company data with one-click deployment.
Amazon Textract	ML service that automatically extracts text, handwriting, tables, and structured form data from scanned documents and PDFs.	Extracting structured line items, invoice totals, and dates from scanned paper receipts.
Amazon Transcribe	Automatic speech recognition (ASR) service that converts spoken audio recordings into time-stamped text transcriptions.	Transcribing customer service phone calls to perform automated text analysis and quality auditing.
Amazon Translate	Neural machine translation service providing fast, accurate, and customizable language translation.	Translating customer support articles into multiple languages in real time for international users.
AWS Transform	Automated code modernization service using generative AI to refactor, upgrade, and migrate legacy software applications.	Refactoring and upgrading legacy Java application codebases to modern long-term support (LTS) versions.

Management and Governance

AWS Service / Feature	Description	Example Use Case
AWS CloudTrail	Governance service that logs and records all user activity and API calls across AWS infrastructure for auditing.	Auditing all administrative and model invocation API calls (InvokeModel) for compliance tracking.
Amazon CloudWatch	Monitoring and observability service for collecting metrics, monitoring logs, and triggering automated alerts.	Tracking model inference latency, token usage metrics, and capturing operational invocation logs.
AWS Config	Service that continuously monitors and audits AWS resource configurations against defined security rules.	Automatically detecting and flagging unencrypted S3 buckets or public endpoints that drift from security baselines.
AWS Trusted Advisor	Automated tool that checks AWS environments against best practices for security, performance, and cost optimization.	Identifying idle SageMaker instances or unattached storage volumes to eliminate wasteful spending.
AWS Well-Architected Tool	Assessment tool that reviews workloads against AWS best practices (including the Machine Learning Lens).	Evaluating a generative AI application architecture against the security and cost pillars of the ML Lens.

Networking and Content Delivery

AWS Service / Feature	Description	Example Use Case
Amazon CloudFront	Global content delivery network (CDN) that caches content and API responses for fast, low-latency delivery.	Accelerating static web frontend assets and caching public API responses for an AI web portal.
Amazon VPC	Isolated virtual network environment in the AWS Cloud to securely host resources and private endpoints.	Routing Bedrock API calls privately over AWS PrivateLink without exposing traffic to the public internet.

Security, Identity, and Compliance

AWS Service / Feature	Description	Example Use Case
AWS Artifact	Compliance portal providing on-demand access to AWS security reports (SOC, ISO) and regulatory agreements.	Downloading AWS ISO/SOC compliance reports or signing a Business Associate Addendum (BAA) for HIPAA compliance.
AWS Identity and Access Management (IAM)	Access management service to securely control authentication and authorization for AWS resources.	Defining granular roles and least-privilege policies to grant applications access to invoke specific Bedrock models.
Amazon Inspector	Automated vulnerability management service that scans compute instances, container images, and Lambda functions for flaws.	Automatically scanning container images hosting ML inference code for software vulnerabilities before deployment.
AWS Key Management Service (AWS KMS)	Managed service to create and control cryptographic keys used to encrypt data at rest.	Encrypting S3 buckets, vector database indices, and SageMaker model artifacts with customer-managed keys.
Amazon Macie	Data security service that uses ML to discover, classify, and protect sensitive Personally Identifiable Information (PII) in S3.	Scanning S3 data lakes to identify and redact sensitive customer PII before indexing data for RAG.
AWS Secrets Manager	Service that helps store, rotate, and manage API keys, database passwords, and OAuth tokens securely.	Storing external API keys safely so AI agents can retrieve them during automated function calling.

Storage

AWS Service / Feature	Description	Example Use Case
Amazon S3	Highly durable object storage designed to store unstructured data, training files, model artifacts, and documents.	Storing raw PDF files, image datasets, and model weight artifacts used in Bedrock Knowledge Bases.
Amazon S3 Glacier	Ultra-low-cost storage class designed for long-term data archiving and regulatory data retention.	Archiving historical AI audit logs and training dataset snapshots for multi-year compliance retention.