AWS AI Services Complete Guide: Bedrock, SageMaker, AI Certification [2026]

AWS AI Services Complete Guide: Bedrock, SageMaker, AI Certification

Generative AI is redefining enterprise competitiveness—and AWS, as the world's largest cloud platform, has built a complete AI service ecosystem. From Amazon Bedrock that lets you directly use top large language models like Claude and Llama, to SageMaker that lets data scientists build their own models, to various specialized AI services like image recognition and speech-to-text, AWS offers a dazzling array of choices. But more choices also means it's easy to lose direction.

This article will help you understand the full picture of AWS AI services, so you know which service to use in which scenario and how to make the right technical choices in this AI wave.

AWS AI Services Overview

AWS AI and machine learning services can be divided into three levels, each corresponding to different user needs and technical capabilities:

Three-Tier AI Service Architecture

Application Layer: AWS has already trained models; you just call the API to use them. For example, use Rekognition to identify faces in images, use Transcribe to convert meeting recordings to text. These services are best for quickly integrating AI features without any machine learning background.

Platform Layer: This has been the hottest area since 2023. Amazon Bedrock lets you directly use top LLMs like Claude and Llama without deploying models yourself. SageMaker JumpStart provides fine-tuning capabilities for pre-trained models.

Foundation Layer: If you need to train your own models from scratch, SageMaker provides a complete MLOps platform, from data labeling, training, to deployment—an end-to-end service.

Complete AWS AI Services List

If you're not familiar with AWS services overall, we recommend reading the AWS Complete Guide first to understand basic concepts.

AWS Bedrock Generative AI

Amazon Bedrock is a major service launched by AWS in 2023, allowing enterprises to directly use foundation models from multiple top AI companies through simple API calls. This means you don't need to deploy and maintain complex GPU clusters yourself to add generative AI capabilities to your applications.

What is Bedrock?

Simply put, Bedrock is an "AI model marketplace" plus "enterprise-grade integration platform." Think of it as:

• Model Marketplace: One-stop access to Claude, Llama, Stable Diffusion, and other models
• Serverless Architecture: No infrastructure management, pay-per-use
• Enterprise Integration: Supports private VPC, IAM access control, data not used for model training

Compared to calling each AI vendor's API directly, Bedrock's biggest advantage is enterprise compliance—your data doesn't leave the AWS environment, won't be used to train models, and can fully integrate with existing AWS security and monitoring mechanisms.

Major Update in June 2026: OpenAI Models Officially Land on Bedrock

The old constraint of "leaving AWS to use GPT" is gone. As of June 1, 2026, OpenAI's GPT-5.5, GPT-5.4, and Codex are generally available on Amazon Bedrock (AWS What's New, 2026; OpenAI, 2026). For enterprises this is straightforward: OpenAI model costs go on your AWS bill, permissions run through IAM, and procurement and compliance follow the same process as every other AWS service.

The supporting updates shipped at the same time:

• Redesigned Bedrock console (2026-06-04): a new interface optimized for the OpenAI- and Anthropic-compatible APIs (AWS, 2026)
• CloudWatch metrics (2026-06-01): native monitoring metrics for the OpenAI/Anthropic-compatible API endpoints, so usage and latency flow into your existing dashboards (AWS, 2026)
• GovCloud follow-up (2026-06-03): GPT-5.4 is generally available in AWS GovCloud (US-West)

In the same week, Anthropic's then-latest Mythos-class model Claude Fable 5 also landed on Bedrock on launch day (2026-06-09) (AWS, 2026)—see our complete Claude Fable 5 guide; its successor, Fable 5.1, launched on 2026-09-01. Bedrock has gone from "the model marketplace without OpenAI" to the platform where both flagship camps sit side by side, which changes the platform-selection logic discussed below.

Supported Foundation Models

As of June 2026, Bedrock supports the following major model providers (Claude Opus 5.5, Claude Sonnet 5.5, and OpenAI's GPT-6 series were added from September 2026; check the Bedrock console for the full list):

The Claude family remains the most popular model lineup on Bedrock, excelling in complex task processing, code generation, and long text analysis. If you're unsure which model to choose, starting with Claude Sonnet is a safe choice; evaluate Opus or the Fable line for engineering-heavy workloads.

Bedrock Agent Features

Bedrock Agent lets you build AI assistants that can "take action," not just passively answer questions. Agents can:

• Call External APIs: Query inventory, place orders, send notifications
• Access Enterprise Data: Connect to company documents through Knowledge Bases
• Multi-step Reasoning: Automatically break down complex tasks and execute step by step

For example, an e-commerce customer service Agent can, upon receiving "I want to return" message, automatically query order status, determine if return conditions are met, and generate a return label—all without human intervention.

Knowledge Bases (RAG Architecture)

RAG (Retrieval-Augmented Generation) is currently the hottest enterprise AI application pattern. Bedrock Knowledge Bases lets you easily implement:

1. Upload Documents: Supports PDF, Word, HTML, Markdown
2. Automatic Vectorization: Uses Titan Embeddings or Cohere models
3. Store Vectors: Integrates with OpenSearch Serverless or Pinecone
4. Smart Retrieval: Automatically finds the most relevant content snippets during queries

This means you can build a customer service bot that "knows all company product manuals" or an enterprise assistant that "has read all internal documents."

Bedrock Pricing

Bedrock uses per-token pricing, with significant price differences between models:

(Claude rows are official standard prices as of June 2026, Anthropic Pricing, 2026; Bedrock regional endpoints carry a 10% premium over global endpoints. Claude Opus 5.5, Claude Sonnet 5.5, and the GPT-6 series, added in September 2026, are not listed here; Anthropic also launched Claude Haiku 5.5 on 2026-10-07, priced on its own API at $0.10/$0.50 for prompts up to 100K tokens and $0.50/$2.50 above that, and the Haiku 4.5 in this table is now Legacy but still available. See AWS Bedrock pricing for authoritative numbers on every model)

Cost Estimation Example: Suppose your customer service bot processes 1,000 conversations daily, averaging 1,000 input tokens + 500 output tokens per conversation, using Claude Sonnet 4.6:

• Daily cost: (1,000 × 1,000 × $3 / 1,000,000) + (1,000 × 500 × $15 / 1,000,000) = $3 + $7.5 = $10.5/day
• Monthly cost: Approximately $315/month

If budget is limited, consider Claude Haiku 4.5 (about $3.5/day under the same assumptions, roughly 67% less) or Llama 3.1 8B (about $0.33/day, roughly 97% less). Haiku 4.5 now has a successor, Haiku 5.5, with lower unit prices on Anthropic's own API; check the AWS pricing page for its availability and price on Bedrock.

Want to Implement AI in Your Enterprise? From Bedrock to Self-hosted LLM, Let Experienced Professionals Help You Avoid Pitfalls

Generative AI technology selection seems simple, but actual implementation involves cost control, data security, model selection, and various other issues. CloudInsight has helped multiple enterprises successfully implement AI solutions. Schedule a free AI implementation consultation, let us help you plan the most suitable AI architecture.

AWS SageMaker Machine Learning

If Bedrock is "using models others have trained," then SageMaker is "the complete platform for training your own models." For teams with proprietary data needing customized models, SageMaker is AWS's flagship ML service.

SageMaker Features

SageMaker provides a complete toolchain for the machine learning lifecycle:

SageMaker Studio

SageMaker Studio is an integrated ML development environment providing:

• Unified Interface: Complete development, training, deployment in one place
• Collaboration Features: Team members can share notebooks and experiment results
• Version Control: Track parameters and results of each experiment
• Automation: One-click model deployment to production

Compared to setting up your own Jupyter Server, Studio's advantage is eliminating operational burden and deep integration with other AWS services (like S3, IAM).

Bedrock vs SageMaker: Which to Choose?

This is the most frequently asked question. Simple decision framework:

Hybrid usage is also common: Use Bedrock for general conversation tasks, use SageMaker to train domain-specific classification or prediction models.

SageMaker Pricing

SageMaker pricing is more complex, mainly consisting of:

• Notebook Instances: ml.t3.medium about $0.05/hour
• Training Instances: From $0.08/hour (CPU) to $30+/hour (GPU) depending on specs
• Inference Endpoints: ml.t2.medium real-time inference about $0.05/hour
• Storage Costs: EBS storage, S3 data

For experimental projects, recommend using SageMaker Studio Lab (free version) or Spot Training (saves 70-90% training costs).

AWS Kiro (AI Development Assistant Tool)

Kiro is an AI IDE from AWS (preview in July 2025, generally available in November 2025), built around spec-driven development with AI agents.

Kiro Core Features

• Specs: Expand a single prompt into user stories with acceptance criteria, a technical design document, and a sequenced task list
• Task execution: AI agents implement tasks one by one, with code diffs and execution history you can review
• Hooks: Saving, creating, or deleting files can automatically trigger agents to run tasks in the background

Kiro's difference from general AI coding tools is spec-driven development: requirements, design, and tasks are written down first, then AI agents implement against them, so decisions stay documented.

Use Cases

• Taking prototypes to production: use Specs to expand requirements into user stories, a design document, and a task list, then let AI agents implement them one by one
• Team collaboration: requirements, design, and tasks stay documented, which makes review and handover easier
• Automating routine checks: use Hooks to update test files on save, refresh README files when API endpoints change, and scan for leaked credentials before you commit

Kiro became generally available in November 2025 and can be downloaded from kiro.dev.

Other AWS AI Services

Besides Bedrock and SageMaker, AWS has a series of specialized AI services. These services have pre-trained models; you just need to call the API to use them.

Amazon Rekognition Image Recognition

Rekognition provides AI analysis capabilities for images and videos:

• Object Detection: Identify objects in images (cars, animals, buildings, etc.)
• Face Analysis: Detect faces, match faces, analyze expressions and age
• Text Detection (OCR): Extract text from images
• Content Moderation: Detect inappropriate content (violence, adult content)

Use Cases:

• E-commerce: Automatically tag product images
• Media: Video content moderation
• Security: Facial access control systems

Pricing: Image analysis about $0.001/image (first 1 million), facial matching (CompareFaces) is also $0.001/image for the first 1 million.

Amazon Comprehend Natural Language Processing

Comprehend provides text analysis features:

• Sentiment Analysis: Determine if text is positive, negative, or neutral
• Entity Recognition (NER): Extract names, places, organizations, dates, etc.
• Key Phrase Extraction: Automatically identify text highlights
• Language Detection: Supports 100+ languages
• Topic Modeling: Discover hidden topics from large document sets

Use Cases:

• Customer Service: Automatically classify complaint types and emotions
• Marketing: Social media sentiment monitoring
• Legal: Contract key information extraction

Amazon Transcribe Speech-to-Text

Transcribe provides high-accuracy speech recognition:

• Real-time Transcription: Streaming audio to text in real-time
• Batch Processing: Upload audio files for batch transcription
• Speaker Identification: Distinguish different speakers
• Custom Vocabulary: Improve accuracy for specialized terminology
• Content Filtering: Automatically filter sensitive words

Use Cases:

• Meeting Notes: Automatically generate meeting transcripts
• Customer Service Quality: Call recording analysis
• Video Subtitles: Automatically generate subtitles

Pricing: About $0.006/minute for standard batch transcription in US East (about $0.010/minute for streaming).

Amazon Lex Conversational AI

Lex is a chatbot building service using the same conversational technology as Alexa:

• Intent Recognition: Understand what users want to do
• Slot Filling: Collect information needed to complete tasks
• Multi-turn Conversation: Support complex conversation flows
• Lambda Integration: Call backend services during conversation

Difference from Bedrock: Lex is suitable for "task-oriented" structured conversations (like reservations, queries), Bedrock is suitable for "open-ended" natural conversations. Both can also be combined—Lex handles intent recognition, Bedrock handles complex response generation.

AWS AI Certification Introduction

If you want to validate your AWS AI expertise, AWS now offers several AI-related certifications (AI Practitioner, Machine Learning Engineer – Associate, Generative AI Developer – Professional, and the beta AI Business Strategist); the entry-level and ML engineering ones are covered below.

AWS Certified AI Practitioner

This is a new entry-level AI certification launched in 2024, suitable for those wanting to understand AI/ML fundamentals.

Exam Domain Weights:

• AI and ML Fundamentals: 20%
• Generative AI Fundamentals: 24%
• Foundation Model Applications: 28%
• Responsible AI Guidelines: 14%
• AI Solution Security: 14%

This certification's feature is "no programming required"—it tests conceptual understanding rather than implementation ability, very suitable for business or management personnel wanting to enter the AI field.

AWS Certified Machine Learning Engineer - Associate

The former Machine Learning – Specialty (MLS-C01) was retired on March 31, 2026. AWS's machine learning technical certification is now the Associate-level Machine Learning Engineer.

Recommend having actual SageMaker usage experience before attempting.

Preparation Resources

• AWS Skill Builder: Official free learning platform with complete courses
• Udemy Courses: Stephane Maarek's AI Practitioner course is well-reviewed
• Practice Exams: Tutorials Dojo, Whizlabs provide high-quality questions

For more AWS certification information, see AWS Certification Complete Guide.

AWS AI vs Azure AI vs GCP AI

All three major cloud platforms have complete AI services. How to choose?

Generative AI Platform Comparison

Platform Advantages

AWS Bedrock Advantages:

• Most model choices (8+ model providers)
• Claude and OpenAI flagship lineups on one platform (GPT-5.5/GPT-5.4/Codex GA since June 2026, AWS, 2026)—the era of "Azure is the only way to GPT" is over
• Deep AWS service integration
• Good enterprise compliance (HIPAA, SOC 2)

Azure OpenAI Advantages:

• Earliest and most complete GPT lineup, paired with the Microsoft Foundry toolchain
• Microsoft ecosystem integration (Teams, Office 365)
• Easy adoption for existing Microsoft environments

Google Gemini Enterprise Agent Platform (formerly Vertex AI) Advantages:

• Gemini model strong multimodal capabilities
• Good BigQuery ML integration
• TPU hardware advantage, lower training costs

Selection Recommendations

For a deeper comparison of the three major clouds, see AWS vs Azure vs GCP Complete Comparison.

FAQ

Q1: What's the Difference Between Bedrock and ChatGPT API?

Bedrock is a "model marketplace" where you can use models from multiple vendors—and since June 2026 that includes OpenAI's GPT-5.5, GPT-5.4, and Codex, so you can switch between Claude and GPT on one platform. Calling the OpenAI API directly limits you to OpenAI models. Additionally, Bedrock data is not used for training and costs roll into your AWS bill, offering better enterprise compliance.

Q2: Do I Need GPUs to Use Bedrock?

No. Bedrock is a Serverless service; AWS manages all infrastructure. You just call the API and pay per token.

Q3: Is SageMaker Suitable for Small Teams?

It depends. If you need to train your own models, SageMaker provides complete tools. But if you just want to use AI features, Bedrock is more friendly for small teams as it doesn't require ML expertise.

Q4: Does AWS AI Services Have Free Tier?

Limited free tier:

• Rekognition: 1,000 images/month each for Group 1 and Group 2 APIs (legacy accounts created before 2025-07-15 only, first 12 months); new accounts use up to US$200 in credits instead, with a 6-month free plan
• Transcribe: 60 minutes/month (legacy accounts created before 2025-07-15 only, first 12 months)
• Comprehend: 50,000 units (about 5 million characters) per API per month, for 12 months from first use
• Bedrock: No dedicated free tier, but new accounts can use their sign-up credits (up to $200 USD) to try it

Q5: Is AI Practitioner Certification Worth Taking?

If you're a PM, sales, or non-technical person wanting to understand AI, this certification is a good entry choice. But if you're an engineer, recommend directly challenging ML Engineer – Associate or focusing on practical experience.

Q6: Do Bedrock Models Handle Chinese Content Well?

Claude and Llama both support Chinese well, including Traditional Chinese. But for specialized domains (legal, medical), recommend supplementing domain knowledge with Knowledge Bases.

Next Steps

AWS AI service selection depends on your specific needs:

• Want to quickly integrate AI features → Start with Bedrock, try one model
• Want to build enterprise knowledge base → Use Bedrock Knowledge Bases
• Want to train your own models → Evaluate if SageMaker meets your needs
• Want to validate AI expertise → Choose AI Practitioner or ML Engineer – Associate based on background

Regardless of which path you choose, the key is to start doing. Open a Bedrock project in AWS Console, run a few prompts, and you'll understand these services' value faster than reading ten articles.

To understand AI service costs, read AWS Pricing Complete Guide for more accurate cost estimation.

Need Professional Advice for AI Implementation? Schedule a Free Consultation

Generative AI technology evolves rapidly. From model selection, architecture design to cost control, each step has its expertise. The CloudInsight team has rich AI project experience and can help you avoid common implementation pitfalls to build truly deployable AI solutions. Schedule a free AI consultation now.

Further Reading

• AWS Complete Guide: Comprehensive Learning Resources from Beginner to Advanced
• AWS Certification Complete Guide: 12 Certifications Introduction and Preparation Tips
• AWS Pricing Complete Guide: Pricing Models, Calculator Tutorial, Cost-Saving Tips
• AWS vs Azure vs GCP 2026 Complete Comparison: Features, Pricing, Selection Guide

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