AWS AI Services Comparison: Bedrock vs SageMaker vs Kendra vs OpenSearch

AWS offers more than two dozen AI and machine learning services. For enterprise teams evaluating their options, the sheer breadth of the portfolio creates a real decision problem: Which services overlap? Which ones complement each other? When should you choose a managed service over building on a general-purpose platform?

This guide compares the most commonly evaluated AWS AI services - Amazon Bedrock, SageMaker, Kendra, OpenSearch, Comprehend, Textract, and Rekognition - with clear guidance on when each is the right choice and how they fit together in production architectures.

Amazon Bedrock: Managed foundation models

Bedrock is AWS's fully managed service for accessing foundation models from Anthropic, Meta, Amazon, Stability AI, and others. It provides a unified API for model invocation, fine-tuning, and RAG through Knowledge Bases.

Best for

• Generative AI applications - chatbots, content generation, summarization, code generation
• RAG pipelines with built-in Knowledge Bases
• Multi-model strategies where you want to switch between providers without changing code
• Teams that want managed infrastructure with no ML ops overhead

Limitations

• You cannot bring your own model architecture - only models available through Bedrock are supported
• Fine-tuning options are limited compared to SageMaker's full training capabilities
• Less control over inference infrastructure - you cannot tune instance types, batching, or GPU allocation

Bedrock is the right starting point for most enterprise GenAI use cases. It minimizes operational burden while providing enterprise-grade security and compliance controls. Our AWS AI consulting practice frequently recommends Bedrock as the foundation, layering in other services where specific capabilities are needed.

Amazon SageMaker: Full ML platform

SageMaker is AWS's comprehensive machine learning platform. It covers the entire ML lifecycle - data preparation, model training, tuning, deployment, and monitoring. Where Bedrock provides managed access to existing models, SageMaker gives you full control to build, train, and deploy your own.

Best for

• Custom model training on your proprietary data
• Fine-tuning open-source models (Llama, Mistral, Falcon) with full control over hyperparameters
• Deploying models to dedicated inference endpoints with custom instance types
• Traditional ML workloads - classification, regression, forecasting, anomaly detection
• Teams with ML engineering expertise who need maximum flexibility

Limitations

• Higher operational complexity - requires ML engineering skills for training, deployment, and monitoring
• Cost management requires careful attention to instance selection and endpoint scaling
• Longer time-to-value compared to Bedrock for standard GenAI use cases

When to use SageMaker over Bedrock

Choose SageMaker when you need to train models on proprietary data that cannot leave your environment, when you require custom model architectures not available through Bedrock, when you need fine-grained control over inference infrastructure for latency-sensitive workloads, or when your use case involves traditional ML (not generative AI). Many production architectures use both - Bedrock for generative AI tasks and SageMaker for specialized models that augment the GenAI pipeline.

Amazon Kendra: Enterprise search

Kendra is a fully managed intelligent search service. It uses natural language understanding to return precise answers from unstructured data sources, not just keyword matches. Kendra connects to over 40 data sources out of the box, including S3, SharePoint, Salesforce, ServiceNow, and databases.

Best for

• Enterprise search across diverse data sources - documents, wikis, ticketing systems, CRMs
• FAQ-style question answering where users expect direct answers, not document links
• Use cases that require access control inheritance from source systems (SharePoint permissions, Salesforce visibility rules)

Kendra vs Bedrock Knowledge Bases

This is the most common comparison question enterprises ask. Bedrock Knowledge Bases provide RAG-optimized retrieval designed to feed context into foundation models. Kendra provides standalone intelligent search with built-in NLU ranking. If your primary goal is powering a GenAI application with document retrieval, Bedrock Knowledge Bases are typically the better fit. If you need a standalone search experience with rich connector support and fine-grained access control, Kendra is the stronger choice. In some architectures, Kendra serves as the retrieval layer for a Bedrock-powered GenAI application, combining Kendra's connector ecosystem with Bedrock's generation capabilities.

Amazon OpenSearch: Vector and full-text search

OpenSearch Service (and OpenSearch Serverless) provides distributed search and analytics. For AI workloads, its key capability is vector search - storing and querying high-dimensional embeddings for similarity search. It also provides full-text search, making it ideal for hybrid retrieval patterns.

Best for

• Vector storage and similarity search for RAG pipelines
• Hybrid search combining semantic (vector) and lexical (keyword) retrieval
• High-volume, low-latency search workloads with custom relevance tuning
• Teams that need full control over indexing, sharding, and query optimization

OpenSearch vs Bedrock Knowledge Bases

Bedrock Knowledge Bases use OpenSearch Serverless under the hood for vector storage. The difference is control vs. convenience. Knowledge Bases manage the index lifecycle, chunking, embedding, and sync for you. Direct OpenSearch gives you full control over index configuration, custom analyzers, sharding strategy, and query DSL. Choose Knowledge Bases when the managed defaults work for your use case. Choose direct OpenSearch when you need custom index configurations, complex query patterns, or integration with existing OpenSearch infrastructure.

Amazon Comprehend, Textract, and Rekognition: Purpose-built AI

AWS offers several purpose-built AI services that handle specific tasks without requiring model training or prompt engineering. These services are often overlooked in the GenAI era, but they remain the right choice for their target use cases.

Amazon Comprehend

Natural language processing service for sentiment analysis, entity extraction, language detection, topic modeling, and PII detection. Use Comprehend when you need these specific NLP capabilities at scale with predictable pricing. Comprehend is often more cost-effective than calling a foundation model for tasks like PII detection or sentiment classification, and it runs with deterministic behavior - the same input always produces the same output.

Amazon Textract

Document intelligence service that extracts text, tables, forms, and structured data from scanned documents and images. Textract excels at processing invoices, receipts, tax forms, and identity documents with specialized pre-trained models. For document processing pipelines, Textract handles the extraction layer while a foundation model on Bedrock handles reasoning about the extracted content.

Amazon Rekognition

Computer vision service for image and video analysis - object detection, facial analysis, text in images, content moderation, and custom label detection. Rekognition handles visual AI tasks that foundation models are only beginning to address reliably. For production image analysis workloads, Rekognition's purpose-built models typically outperform general-purpose vision models on specific tasks like content moderation and PPE detection.

How these services fit together

The most effective enterprise AI architectures on AWS combine multiple services, using each where it excels. A document processing pipeline might use Textract to extract structured data from scanned documents, Comprehend to detect PII before storage, OpenSearch for indexing and retrieval, and Bedrock for natural language interaction with the processed data. An agentic AI system might use Bedrock Agents for reasoning and orchestration, SageMaker for a custom classification model that the agent calls as a tool, and Kendra for enterprise-wide knowledge retrieval.

The common mistake is treating service selection as an either/or decision. In practice, production systems are composites. The architecture challenge is designing clean interfaces between services so each can be upgraded or replaced independently as the AWS AI portfolio continues to evolve.

Decision framework

When evaluating AWS AI services for a new project, work through these questions in order:

• Is there a purpose-built service for your task? If Comprehend, Textract, or Rekognition handle your specific need, start there. They are cheaper, faster, and more deterministic than general-purpose models.
• Do you need generative AI? If yes, Bedrock is the default choice. It provides managed access to the best foundation models with enterprise security built in.
• Do you need custom model training? If Bedrock's fine-tuning options are insufficient, SageMaker gives you full control over the training and deployment lifecycle.
• What is your retrieval strategy? For GenAI-integrated retrieval, use Bedrock Knowledge Bases. For standalone enterprise search, use Kendra. For custom search with full control, use OpenSearch directly.
• What are your operational constraints? Teams with limited ML engineering capacity should lean toward managed services (Bedrock, Kendra, Comprehend). Teams with deep ML expertise can extract more value from SageMaker and OpenSearch.

Getting it right the first time

The cost of choosing the wrong AWS AI service is not just the initial implementation. It is the rearchitecting, data migration, and retraining that follow when the wrong choice hits production constraints. Investing in architecture evaluation upfront - understanding your specific requirements, testing candidate services against your actual data, and designing for composability - saves months of rework later.

Our AWS AI consulting team helps enterprises navigate these decisions. We evaluate your use cases against the full AWS AI portfolio, design architectures that combine services effectively, and implement production-grade systems that meet your security, cost, and performance requirements. For generative AI consulting that extends beyond AWS, we bring the same service-selection rigor to multi-cloud and hybrid environments.