AWS Kendra vs AWS OpenSearch: Service Comparison Guide¶
Overview¶
This guide provides a comprehensive comparison between AWS Kendra and AWS OpenSearch to help you choose the right search solution for your needs. Both services offer powerful search capabilities but serve different use cases and technical requirements.
For details on OpenSearch: See the dedicated OpenSearch Introduction
Service Philosophy and Approach¶
AWS Kendra - Intelligent Enterprise Search¶
AWS Kendra is a fully managed enterprise search service that uses machine learning to deliver intelligent search capabilities. It's designed for organizations that want to provide natural language search across their enterprise content without extensive technical configuration.
Key characteristics:
- Managed AI/ML: Pre-trained models handle query understanding and relevance
- Natural language interface: Users can ask questions as they would to a person
- Enterprise-focused: Built specifically for organizational knowledge management
- Minimal configuration: Works out-of-the-box with many data sources
AWS OpenSearch - Flexible Search and Analytics Platform¶
AWS OpenSearch is a distributed search and analytics suite based on Elasticsearch and Kibana. It provides full control over search implementation and supports both traditional text search and modern vector search capabilities.
Key characteristics:
- Full control: Configure every aspect of search behavior and relevance
- Multi-purpose: Supports search, analytics, logging, and monitoring use cases
- Extensible architecture: Build custom search experiences and applications
- Vector search capable: Modern semantic search with embedding models
For detailed technical information about OpenSearch's search approaches, algorithms, and implementation patterns, see the OpenSearch Introduction.
Core Differences Summary¶
| Aspect | AWS Kendra | AWS OpenSearch |
|---|---|---|
| Primary Focus | Enterprise document search | General-purpose search & analytics |
| User Experience | Natural language queries | Structured queries with full customization |
| Setup Complexity | Low (managed service) | High (requires configuration and tuning) |
| Customization | Limited | Extensive |
| Use Cases | Knowledge bases, FAQ, document search | E-commerce, logging, custom search apps, vector search |
Detailed Feature Comparison¶
Search Capabilities¶
| Capability | AWS Kendra | AWS OpenSearch |
|---|---|---|
| Query Type | Natural language questions | Structured queries, full-text search |
| Semantic Understanding | Built-in ML models | Custom implementation required |
| Relevance Tuning | Automatic ML-based | Manual configuration required |
| Faceting/Filtering | Basic metadata filtering | Advanced filtering and aggregations |
| Auto-suggestions | Built-in query suggestions | Custom implementation required |
| Synonyms | Automatic detection | Manual configuration |
| Multi-language | Limited built-in support | Full multilingual support |
| Vector Search | Not supported | Full vector search capabilities |
| RAG Support | Limited to Q&A format | Full RAG implementation capabilities |
For detailed AWS Kendra features, see: AWS Kendra Features
For comprehensive OpenSearch vector search capabilities, algorithms, and implementation details, see: OpenSearch Technical Guide
For detailed RAG implementation patterns and architectures with OpenSearch, see: OpenSearch RAG Guide
Data Handling and Integration¶
| Feature | AWS Kendra | AWS OpenSearch |
|---|---|---|
| Supported Formats | 50+ document formats (PDF, Word, HTML, etc.) | Primarily JSON (custom preprocessing required) |
| Data Connectors | 40+ native connectors (SharePoint, S3, Salesforce, etc.) | Custom connectors required |
| Real-time Updates | Near real-time (minutes) | Real-time (seconds) |
| Document Size Limit | Up to 50MB per document | Up to 100MB per document (configurable) |
| Incremental Sync | Built-in via connectors | Custom implementation |
| API Integration | REST API, SDKs | REST API, multiple client libraries |
For current Kendra connectors, see: AWS Kendra Data Connectors
For OpenSearch APIs, see: OpenSearch API Reference
Scalability and Performance¶
| Aspect | AWS Kendra | AWS OpenSearch |
|---|---|---|
| Maximum Documents | Up to 5M documents per index | Virtually unlimited with proper architecture |
| Query Throughput (QPS) | Up to 8,000 queries/day (base tier) | Thousands of queries per second |
| Scaling | Automatic | Manual or automatic cluster scaling |
| Multi-region | Single region per index | Multi-region deployment supported |
| High Availability | Built-in | Configurable with replicas |
Security and Compliance¶
| Security Feature | AWS Kendra | AWS OpenSearch |
|---|---|---|
| Data Encryption | At rest and in transit | At rest and in transit |
| Access Control | IAM, Active Directory | IAM, fine-grained access control |
| VPC Support | Yes | Yes |
| Compliance | SOC, HIPAA eligible | SOC, HIPAA, FedRAMP |
| Audit Logging | CloudTrail integration | CloudTrail + detailed query logs |
For current compliance status, see: AWS Compliance Programs
Cost Comparison¶
⚠️ Pricing Disclaimer: AWS pricing changes frequently. Please refer to official AWS pricing pages for current rates and detailed cost calculations.
AWS Kendra Pricing Structure¶
Current pricing information: AWS Kendra Pricing
- Index-based pricing: Monthly fee per search index
- Query-based charges: Per query pricing model
- Developer Edition: Lower-cost tier with reduced capacity
- Enterprise Edition: Full features and higher capacity
Typical cost factors:
- Base index fee (varies by edition)
- Per-query charges
- Additional capacity units
- Data source connectors (some may have additional costs)
AWS OpenSearch Pricing Structure¶
Current pricing information: AWS OpenSearch Pricing
- Instance-based pricing: Hourly rates for compute instances
- Storage charges: Separate EBS storage costs
- Data transfer: Standard AWS data transfer rates
- Reserved instances: Available for cost optimization
Typical cost factors:
- Instance hours (master, data, and UltraWarm nodes)
- Storage volume and type
- Data transfer costs
- Optional features (fine-grained access control, etc.)
Cost Optimization Considerations¶
Kendra cost optimization:
- Choose appropriate edition for your needs
- Optimize query patterns to reduce query charges
- Use incremental updates efficiently
- Monitor usage with AWS Cost Explorer
OpenSearch cost optimization:
- Right-size instances for your workload
- Use Reserved Instances for predictable workloads
- Implement data lifecycle policies
- Consider UltraWarm storage for infrequently accessed data
- Optimize shard and replica configuration
Real-World Use Case Examples¶
When AWS Kendra Excels¶
Enterprise Knowledge Base
- Scenario: Company with 50,000 employees needs to search across HR policies, procedures, and documentation
- Why Kendra: Employees can ask natural questions like "What's the remote work policy?" without training
- Benefits: Quick deployment, built-in connectors to existing systems, automatic relevance tuning
Customer Support FAQ
- Scenario: Customer service needs intelligent search across support documents
- Why Kendra: Natural language understanding improves answer accuracy
- Benefits: Reduced support ticket volume, faster resolution times
Legal and Compliance Search
- Scenario: Law firm needs to search across case documents and legal precedents
- Why Kendra: Understanding of legal terminology and document context
- Benefits: Faster case research, improved document discovery
When AWS OpenSearch Excels¶
RAG-Powered AI Applications
- Scenario: Company building an AI assistant that needs to answer questions using company knowledge base
- Why OpenSearch: Full RAG implementation with vector search, hybrid retrieval, and custom reranking
- Benefits: Accurate AI responses with source attribution, scalable knowledge integration
- Implementation: See OpenSearch RAG Guide for detailed patterns
E-commerce Product Search
- Scenario: Online retailer with millions of products needs advanced search with filters, facets, and recommendations
- Why OpenSearch: Full control over ranking, faceting, and custom scoring
- Benefits: Optimized conversion rates, personalized search experiences
Application Monitoring and Logging
- Scenario: Technology company needs to search and analyze application logs
- Why OpenSearch: Real-time ingestion, powerful analytics, custom dashboards
- Benefits: Faster incident resolution, proactive monitoring
Content Discovery Platform
- Scenario: Media company needs semantic search across articles, videos, and podcasts
- Why OpenSearch: Vector search enables content similarity and recommendation
- Benefits: Improved content discovery, user engagement
Multi-tenant SaaS Application
- Scenario: Software platform needs to provide search functionality to multiple clients
- Why OpenSearch: Flexible architecture supports multi-tenancy and customization
- Benefits: Scalable solution, client-specific search experiences
Decision Framework¶
Choose AWS Kendra When:¶
- Primary use case is enterprise document search
- Users need natural language query interface
- Quick deployment with minimal technical resources
- Built-in connectors match your data sources
- Willing to pay premium for managed AI/ML capabilities
- Limited search customization requirements
Choose AWS OpenSearch When:¶
- Need full control over search relevance and ranking
- Building custom search applications
- Require vector search and semantic capabilities
- Implementing RAG (Retrieval-Augmented Generation) systems
- Have technical team capable of managing search infrastructure
- Multiple use cases beyond search (analytics, logging)
- Cost optimization is important for high query volumes
- Need real-time search and analytics capabilities
- Building AI-powered applications requiring knowledge retrieval
Hybrid Approach Considerations¶
Some organizations use both services:
- Kendra for internal enterprise search and simple Q&A systems
- OpenSearch for customer-facing applications, analytics, and advanced RAG implementations
This approach maximizes the strengths of each service while serving different organizational needs.
RAG Implementation Comparison¶
| RAG Capability | AWS Kendra | AWS OpenSearch |
|---|---|---|
| Setup Complexity | Low - built-in Q&A format | High - requires custom implementation |
| Customization | Limited to predefined patterns | Full control over retrieval and generation |
| Vector Search | Not available | Advanced HNSW and IVF algorithms |
| Hybrid Retrieval | Basic metadata filtering | Sophisticated text + vector combination |
| Reranking | Built-in ML models | Custom reranking pipelines |
| Multi-modal RAG | Text documents only | Text, images, and multimedia content |
| Production Scale | Managed scaling | Requires infrastructure management |
| Cost for RAG | Higher per-query costs | More cost-effective at scale |
For comprehensive RAG implementation guidance with OpenSearch, see: OpenSearch RAG Guide
Migration Considerations¶
Key Planning Factors¶
Data Architecture
- Evaluate current data formats and sources
- Plan for data transformation requirements
- Consider ongoing data synchronization needs
User Experience
- Assess user expectations and training requirements
- Plan for query interface changes
- Design for gradual migration if needed
Technical Implementation
- Evaluate existing integrations and APIs
- Plan for infrastructure changes
- Consider development resource requirements
Cost Impact
- Model costs under different usage scenarios
- Factor in migration and development costs
- Plan for ongoing operational expenses