AWS AI Services — The Complete Landscape
AWS offers 25+ AI/ML services spanning from low-level infrastructure to fully managed APIs. Choosing the right service depends on your use case, team expertise, customization needs, and operational preferences. This guide provides a decision framework to select the right AWS AI service for any scenario.
Generative AI Services
| Service | What It Does | Choose When |
|---|---|---|
| Amazon Bedrock | Access foundation models (Claude, Nova, Titan, Llama, Mistral) via API with managed RAG, Agents, and Guardrails | Building GenAI applications — chatbots, content generation, code, summarization |
| Amazon SageMaker AI | Full ML platform for training, fine-tuning, and deploying custom models | Need custom models, full MLOps, or high-volume dedicated inference |
| Amazon Q Business | Enterprise GenAI assistant connected to company data (40+ connectors) | Internal enterprise Q&A over company documents, Slack/Teams integration |
| Amazon Q Developer | AI coding assistant for IDE, CLI, and AWS Console | Code generation, debugging, code transformation, AWS console assistance |
| Amazon Nova | AWS’s own foundation models (Micro, Lite, Pro, Premier, Canvas, Reel) | Cost-optimized GenAI where you want AWS-native models |
| PartyRock | No-code playground for building GenAI apps | Learning, prototyping, demos without AWS account |
AI Application Services (No ML Expertise Required)
| Service | What It Does | Choose When |
|---|---|---|
| Amazon Rekognition | Image and video analysis — faces, objects, text, content moderation, custom labels | Face detection, content moderation, PPE detection, celebrity recognition |
| Amazon Textract | Extract text, tables, and forms from scanned documents | Invoice processing, ID verification, form digitization, medical records |
| Amazon Comprehend | NLP — sentiment, entities, key phrases, language detection, PII, custom classification | Sentiment analysis, content categorization, PII detection in text |
| Amazon Transcribe | Speech-to-text with speaker diarization, custom vocabulary, real-time streaming | Call transcription, meeting notes, subtitles, medical transcription |
| Amazon Polly | Text-to-speech with 60+ voices, SSML, Neural TTS, custom lexicons | Voice interfaces, accessibility, content narration, IVR systems |
| Amazon Translate | Neural machine translation — 75+ languages, real-time and batch | Website localization, multilingual support, document translation |
| Amazon Lex | Conversational interfaces (chatbots) with ASR + NLU, multi-turn dialogs | IVR bots, customer service chatbots, order-taking systems |
| Amazon Kendra | Intelligent enterprise search with ML-powered ranking | Enterprise document search, FAQ retrieval (being replaced by Q Business for GenAI) |
| Amazon Personalize | Real-time recommendations — products, content, search re-ranking | E-commerce recommendations, content personalization, user segmentation |
| Amazon Forecast | Time-series forecasting with AutoML | Demand planning, inventory optimization, financial forecasting, capacity planning |
Specialized AI Services
| Service | What It Does | Choose When |
|---|---|---|
| Amazon Comprehend Medical | Extract medical entities (conditions, medications, dosages) from clinical text | Healthcare — EHR processing, clinical trial matching, medical coding |
| Amazon Transcribe Medical | Speech-to-text optimized for medical terminology | Clinical documentation, physician dictation, telemedicine |
| Amazon HealthLake | FHIR-compliant health data lake with built-in NLP | Healthcare data aggregation, clinical analytics, population health |
| Amazon Fraud Detector | ML-based fraud detection for online payments, account creation, guest checkout | Online transaction fraud, new account fraud, loyalty program abuse |
| Amazon Lookout (Metrics/Vision/Equipment) | Anomaly detection for metrics, images, and industrial equipment | Manufacturing quality control, equipment predictive maintenance, business KPI monitoring |
| AWS DeepRacer | Reinforcement learning via autonomous racing | Learning reinforcement learning, team competitions, education |
AI Infrastructure
| Service | What It Does | Choose When |
|---|---|---|
| AWS Trainium / Trainium2 | Custom ML training chips — up to 50% cost savings vs GPU | Large-scale model training where cost optimization matters |
| AWS Inferentia / Inferentia2 | Custom ML inference chips — low latency, high throughput | High-volume inference at lowest cost-per-prediction |
| Amazon EC2 P5/P4 (NVIDIA) | GPU instances with NVIDIA H100/A100 | Custom training requiring CUDA ecosystem, multi-GPU workloads |
| SageMaker HyperPod | Managed distributed training clusters with auto-recovery | Training foundation models at scale, multi-node distributed training |
Common Scenario Decision Matrix
| Scenario | Best Service | Why |
|---|---|---|
| Customer support chatbot using company docs | Bedrock + Knowledge Bases | Managed RAG, no ML expertise needed |
| Employees asking questions about internal policies | Amazon Q Business | Enterprise-ready, 40+ data connectors, access controls |
| Extracting data from scanned invoices | Amazon Textract | Purpose-built for document extraction with tables and forms |
| Product recommendations on e-commerce site | Amazon Personalize | Real-time collaborative filtering, no ML expertise |
| Detecting inappropriate images in user uploads | Amazon Rekognition | Content moderation API with configurable confidence thresholds |
| Forecasting next quarter’s product demand | Amazon Forecast | AutoML for time-series, handles holidays/promotions |
| Transcribing customer call recordings | Amazon Transcribe + Comprehend | Speech-to-text + sentiment + entity extraction pipeline |
| Custom fraud detection model on proprietary data | Amazon SageMaker AI | Full training control, custom algorithms, VPC isolation |
| Multi-step AI agent that books flights + checks email | Bedrock Agents | Tool use, memory, code interpreter, Return of Control |
| Generating marketing copy with brand safety | Bedrock + Guardrails | GenAI generation + content safety enforcement |
Service Selection Framework
Ask these questions in order:
- Is there a purpose-built AI service? → Use it (Personalize for recs, Textract for docs, etc.) — these outperform general models for their specific task.
- Is it a generative AI use case? → Use Bedrock (simplest) or SageMaker (if you need custom training).
- Is it an enterprise internal use case? → Consider Q Business for document Q&A or Q Developer for coding.
- Do you need a custom ML model? → Use SageMaker with full training pipeline.
- Is it high-volume inference? → Consider Inferentia2 instances or SageMaker dedicated endpoints.
AWS Certification Exam Practice Questions
Question 1:
A retail company wants to add “Customers who bought this also bought” recommendations to their website. They have purchase history data but no ML team. Which service should they use?
- Amazon Bedrock with product catalog in Knowledge Bases
- Amazon Personalize with User-Personalization recipe
- Amazon SageMaker with a collaborative filtering algorithm
- Amazon Comprehend with custom classification
Show Answer
Answer: B – Amazon Personalize is purpose-built for recommendations with real-time personalization, collaborative filtering, and campaign management. It requires no ML expertise — you provide interaction data and it trains models automatically. Bedrock/GenAI is not optimal for recommendation engines. SageMaker would work but requires ML expertise.
Question 2:
A hospital needs to extract medication names, dosages, and conditions from physician notes in unstructured text. Which service is MOST appropriate?
- Amazon Comprehend with custom entity recognition
- Amazon Comprehend Medical
- Amazon Textract
- Amazon Bedrock with a medical prompt
Show Answer
Answer: B – Amazon Comprehend Medical is specifically designed to extract medical entities (medications, dosages, conditions, procedures, anatomy) from clinical text. It understands medical terminology, abbreviations, and context that general NLP services would miss. Textract is for scanned document extraction, not clinical NLP.
Question 3:
A company wants their 5,000 employees to ask questions about HR policies, benefits, and company procedures using natural language. The content is spread across SharePoint, Confluence, and internal wikis. Which service is MOST operationally efficient?
- Amazon Bedrock Knowledge Bases with S3 exports
- Amazon Q Business with native connectors
- Amazon Kendra with custom ranking
- Custom RAG built on SageMaker
Show Answer
Answer: B – Amazon Q Business is purpose-built for enterprise internal Q&A with native connectors to SharePoint, Confluence, and 40+ data sources. It handles access controls (respecting existing permissions), provides web UI and Slack/Teams integration, and requires no ML expertise. Bedrock Knowledge Bases would require exporting data to S3 first.
Question 4:
A media company needs to automatically generate subtitles for video content in multiple languages. Which combination of services should they use?
- Amazon Rekognition + Amazon Translate
- Amazon Transcribe + Amazon Translate
- Amazon Polly + Amazon Translate
- Amazon Bedrock + Amazon Translate
Show Answer
Answer: B – Amazon Transcribe converts speech to text (with timestamps for subtitles), then Amazon Translate converts the text into target languages. This is the standard pipeline for multilingual subtitle generation. Polly is text-TO-speech (opposite direction). Rekognition is for visual content, not audio.
Question 5:
A logistics company needs to predict delivery times 2 weeks in advance considering seasonality, promotions, weather, and historical patterns. Which service is designed for this?
- Amazon Bedrock with historical data in context
- Amazon Personalize with time-based recipes
- Amazon Forecast with related time series
- Amazon SageMaker with custom LSTM model
Show Answer
Answer: C – Amazon Forecast is purpose-built for time-series forecasting with AutoML. It natively handles seasonality, related time series (weather, promotions), holidays, and cold-start problems. It provides quantile forecasts (P10/P50/P90) for planning uncertainty. SageMaker LSTM would work but requires significant ML expertise.
Related AWS AI Guides
- Bedrock vs SageMaker
- RAG Architecture on AWS
- Prompt Engineering on AWS
- Responsible AI on AWS
- AWS AI & Generative AI Services Cheat Sheet
- Bedrock Agents, Knowledge Bases & Guardrails
Frequently Asked Questions
When should I use Bedrock vs purpose-built AI services?
Use purpose-built services (Personalize, Textract, Rekognition, Forecast) when one exists for your exact use case — they’re optimized and outperform general models. Use Bedrock for open-ended generative tasks (content creation, summarization, Q&A, code) where no purpose-built service exists.
Can Bedrock replace all other AI services?
No. While Bedrock’s foundation models can attempt many tasks, purpose-built services are more accurate, cheaper, and easier for their specific domains. Personalize outperforms Bedrock for recommendations, Textract outperforms it for document extraction, and Forecast outperforms it for time-series prediction.
What’s the difference between Q Business and Bedrock Knowledge Bases?
Q Business is a complete enterprise application (with UI, access controls, and data connectors) for employee Q&A. Bedrock Knowledge Bases is an API building block for developers to build custom RAG applications. Q Business uses Bedrock under the hood but adds enterprise-ready features.