A comprehensive guide to choosing and implementing AWS native vector database services for RAG applications, covering OpenSearch, RDS PostgreSQL, Neptune Analytics, Bedrock Knowledge Bases, and Kendra with real-world decision frameworks.
The landscape of Retrieval-Augmented Generation (RAG) applications has evolved dramatically, with vector databases becoming the backbone of modern AI systems. As organizations are motivated to implement RAG solutions, the choice of vector database architecture can make or break the success of your application. AWS offers five native services for vector database functionality, each optimized for different use cases and scale requirements. Let’s dive in!
In 2024-2025, these services have made significant enhancements in my opinion. From OpenSearch’s fourfold latency improvements to pgvector 0.8.0’s enhanced query planning capabilities, and Bedrock Knowledge Bases’ new GraphRAG support, these options are far more powerful and production-ready than ever before.
The Five AWS Vector Database Powerhouses
Amazon OpenSearch Service: The Scale Champion
AWS OpenSearch Service Vector database capabilities blog Vector database capabilities revisited k-NN search documentation Cost optimization with quantization Disk-optimized vector engine Serverless vector search Choosing k-NN algorithms
Amazon OpenSearch Service stands as the most robust option for high-scale vector deployments. With OpenSearch 2.17 bringing significant improvements including fourfold latency improvements and 25% better performance through parallelization, it’s designed for organizations that need to handle billions of vectors with sub-second response times.
Key Capabilities:
- Massive Scale: Supports up to 16,000 dimensions with proven billion-scale deployments
- Multiple Algorithms: Three engine options (NMSLIB, FAISS, Lucene) with HNSW, IVF, and product quantization
- Advanced Optimization: Up to 32x compression with binary vectors and 70% cost reduction with disk-optimized ANN
- Serverless Option: Auto-scaling with pay-per-OCU pricing model
The service excels when you need maximum flexibility and can invest in the operational complexity. Recent disk-based vector search capabilities reduce memory requirements by up to 66% while maintaining query performance through intelligent rescoring.
Amazon RDS PostgreSQL with pgvector: The Integration Master
AWS RDS PostgreSQL | pgvector 0.8.0 announcement | pgvector 0.7.0 announcement | RAG workflows guidance
For organizations with existing PostgreSQL infrastructure, RDS with pgvector offers the smoothest integration path. The latest pgvector 0.8.0 release includes significant improvements to PostgreSQL’s query planner selection when filters are present, delivering better query performance and search result quality.
Recent Enhancements:
- Performance Boost: 30x faster HNSW index builds and improved filtering capabilities
- Extended Dimension Support: Up to 4,000 dimensions with halfvec and 64,000 with binary vectors
- Advanced Data Types: halfvec (50% memory reduction), sparsevec, and binary vector support
- Enhanced Filtering: Iterative index scans help prevent ‘overfiltering’ and ensure sufficient results
This option shines when you need to combine vector search with complex relational queries and maintain ACID compliance across your entire dataset.
Amazon Neptune Analytics: The Relationship Expert
AWS Neptune | AWS Neptune Analytics Documentation | Vector indexing guide | GraphRAG blog post
Neptune Analytics introduces a unique approach by combining vector similarity with graph relationships. With GraphRAG capabilities in preview, it provides more accurate and comprehensive responses by using RAG techniques combined with graphs.
Unique Features:
- Highest Dimension Support: Up to 65,535 dimensions, the highest among AWS services
- GraphRAG Capabilities: Multi-hop reasoning and relationship-aware retrieval
- Complex Query Support: Processes graph + vector queries efficiently
- Accuracy Improvements: Up to 35% better answer quality for interconnected queries
Choose Neptune Analytics when your data has rich relationships and you need to understand connections between entities, not just similarity.
Amazon Bedrock Knowledge Bases: The Rapid Deployment Solution
AWS Bedrock Knowledge Bases | AWS Bedrock Knowledge Bases Documentation | AWS Bedrock Knowledge Bases How it works guide | AWS Bedrock Knowledge Bases Launch announcement
Amazon Bedrock Knowledge Bases now supports custom connectors and ingestion of streaming data, allowing developers to add, update, or delete data through direct API calls. This represents the fastest path from concept to production RAG application.
2024-2025 Enhancements:
- Multimodal Processing: Parse documents using either Amazon Bedrock Data Automation or foundation models, improving accuracy for documents with both images and text
- Structured Data Support: Natural language querying of data warehouses and data lakes through conversational interfaces
- Streaming Capabilities: Real-time data ingestion without intermediary storage
- Enhanced Data Sources: Web crawler, Confluence, SharePoint, and Salesforce connectors
- Built-in Evaluation: RAG evaluation tools in preview for quality assessment
This service eliminates months of development time while providing enterprise-grade features that most custom implementations lack.
Amazon Kendra: The Enterprise Search Specialist
AWS Kendra | AWS Kendra Features Overview | AWS Kendra Index Types Documentation | AWS Kendra GenAI Index announcement
Kendra delivers enterprise-grade semantic search without requiring vector expertise. Its GenAI Index optimizes specifically for RAG workloads with 43 native data connectors and advanced document understanding capabilities.
Enterprise Features:
- Zero Vector Expertise Required: Built-in semantic understanding and document processing
- Comprehensive Connectors: 43 pre-built connectors for enterprise data sources
- Advanced Access Control: Built-in security and compliance features
- Document Intelligence: Natural understanding of complex document structures
While limited to English, Kendra excels at complex document comprehension and provides the fastest time-to-value for traditional enterprise search scenarios.
Performance and Scale: Making the Right Choice
High-Throughput Scenarios (>100M Vectors)
For applications handling more than 100 million vectors, OpenSearch Service provides the most proven solution. The service can handle billions of vectors across thousands of dimensions, with serverless options that auto-scale for variable workloads.
Memory Planning for OpenSearch:
The memory requirements follow the formula: 1.1 * (4 * d + 8 * m) * num_vectors * (1 + replicas) bytes. For 1 billion vectors of 128 dimensions with default settings, you’d need approximately 1,408 GB of memory.
Recent SIMD optimizations deliver 87% latency reduction for inner product calculations, making it viable for real-time applications at massive scale.
Mid-Scale Deployments (<100M Vectors)
RDS PostgreSQL with pgvector works exceptionally well for sub-100M vector deployments, especially when leveraging Graviton3 instances for 40% better price-performance. HNSW indexes provide 15.5x faster queries than IVFFlat, but require the entire index in memory, making instance sizing critical.
Real-Time Latency Requirements
For sub-millisecond response requirements, consider:
- OpenSearch with warm indices and proper caching strategies
- MemoryDB with vector search capabilities (emerging option)
- Neptune Analytics for complex graph+vector queries (processes in seconds despite scale)
Cost Optimization: Maximizing Your ROI
Storage-Intensive Workloads
OpenSearch’s advanced quantization offers dramatic cost reductions: binary vectors provide 32x compression, product quantization delivers up to 64x compression, and disk-based search reduces costs by 66%.
Quantization Strategy:
Standard vectors (float32): 4 bytes per dimension
Binary quantization: 0.125 bytes per dimension (32x reduction)
Product quantization: Variable compression up to 64x
Variable Workload Patterns
For unpredictable traffic patterns, serverless options provide the best cost efficiency:
- OpenSearch Serverless: Pay per OCU-hour with automatic scaling
- Aurora Serverless v2: Scale to zero with 15-second resume times
- Bedrock Knowledge Bases: No infrastructure charges, only model usage(!)
Development and Testing
Optimize development costs with:
- Single-AZ deployments (50% cost savings)
- Reserved instances for predictable workloads (up to 70% savings)
- Spot instances for batch vector processing (up to 90% savings)
Technical Implementation Deep Dive
Vector Characteristics and Algorithm Selection
Dimensionality Limits by Service:
- OpenSearch: 16,000 dimensions (float32)
- Neptune Analytics: 65,535 dimensions (highest capacity)
- pgvector: 2,000 standard, 4,000 with halfvec, 64,000 with binary
- Bedrock/Kendra: Model-dependent (typically 1,536)
Algorithm Availability:
- HNSW: Available in OpenSearch (all engines), pgvector, Neptune Analytics
- IVF: OpenSearch FAISS engine only
- Product Quantization: OpenSearch FAISS exclusive
- Exact Search: All services support with performance trade-offs
Multi-Tenancy Strategies
Dedicated Isolation (Recommended for Compliance):
- Separate Bedrock Knowledge Bases per tenant
- OpenSearch index-per-tenant with routing
- PostgreSQL schema separation in Aurora
Shared Infrastructure with Filtering:
- OpenSearch metadata filtering integrated with k-NN queries
- pgvector WHERE clause optimization in version 0.8.0
- Kendra user context filtering
- Neptune graph-based access control
Winners by Use Case
Enterprise Document Search with Compliance
Winner: Amazon Kendra GenAI Index
Kendra provides built-in access control, 43 enterprise connectors, and requires no vector expertise. Starting cost around $810/month makes it economical for most enterprise scenarios.
Alternative: Bedrock Knowledge Bases + OpenSearch Serverless
- More customization flexibility
- Better multimodal support
- Requires additional setup complexity
High-Scale Consumer Applications
Winner: OpenSearch Service
Proven at billion-scale deployments with advanced caching, optimization features, and multiple algorithm choices.
Alternative: OpenSearch Serverless
- Eliminates capacity planning
- Higher per-query costs but automatic scaling
- Better for unpredictable traffic patterns
Existing PostgreSQL Environments
Winner: Aurora PostgreSQL with pgvector
Seamless integration with existing relational data, ACID compliance, and familiar tooling make this the obvious choice for PostgreSQL shops.
Considerations:
- Memory requirements for HNSW indexes
- Performance degradation beyond 1M vectors without proper optimization
- Recent 30x performance improvements and enhanced filtering capabilities address previous limitations
Knowledge Graph Applications
Winner: Neptune Analytics
Unique graph + vector capabilities provide 35% accuracy improvement for complex queries through relationship-aware retrieval.
Trade-offs:
- Single vector index limitation per graph
- Higher complexity than pure vector search
- Currently limited regional availability
Rapid Prototyping and MVPs
Winner: Bedrock Knowledge Bases
Days to production versus months, zero infrastructure management, and built-in evaluation tools make this the clear winner for getting started quickly.
Migration Path: Start with Bedrock for validation, then migrate to self-managed options only when you’ve validated specific custom requirements that the managed service cannot meet.
Recent Innovations Reshaping the Landscape
GraphRAG Revolution
The emergence of GraphRAG through Neptune Analytics and Bedrock Knowledge Bases integration fundamentally changes retrieval for relationship-rich data. Organizations with knowledge graphs or complex document relationships should prioritize these services for significantly improved accuracy.
Quantization Maturity
Quantization advances in pgvector 0.7+ and OpenSearch make billion-scale deployments economically viable, with 67x faster index builds removing previous bottlenecks.
Managed Services Production Readiness
Bedrock Knowledge Bases evolution positions it as production-ready for most RAG use cases, not just prototypes. The addition of custom chunking, reranking models, and evaluation frameworks addresses previous limitations that forced organizations toward custom solutions.
Migration Paths and Hybrid Architectures
Many organizations benefit from hybrid approaches rather than forcing all data into a single service:
Effective Hybrid vector database architectures:
- Kendra + OpenSearch: Enterprise documents in Kendra, user-generated content in OpenSearch
- pgvector + Bedrock: Structured data in PostgreSQL, unstructured in Knowledge Bases
- Neptune + OpenSearch: Graph relationships in Neptune, content vectors in OpenSearch
Migration Recommendations:
- Start with managed services for faster validation
- Migrate to self-managed for specific optimizations only after validating the need
- Maintain hybrid architectures for diverse data types rather than forcing uniformity
Common Anti-Patterns to Avoid
Over-Engineering Initial Deployments
Starting with complex custom solutions when Bedrock Knowledge Bases would suffice delays time-to-value without proportional benefits. The service now supports custom connectors and streaming data, addressing most customization needs. I def recommend to start with Bedrock Knowledge Bases and migrate to self-managed options.
Ignoring Quantization Opportunities
Running high-dimensional vectors without compression wastes 32-64x storage and memory costs. Modern quantization techniques maintain quality while reducing infrastructure requirements dramatically!
Single-Service Tunnel Vision
Forcing all data types into one service instead of leveraging each service’s strengths in a hybrid architecture often leads to suboptimal performance and unnecessary complexity.
Cheers! 🍺