AWS Vector Databases Decision Guide

Navigate the AWS vector database landscape and make informed decisions about semantic search, RAG, and AI-powered applications for your business needs.

Start Decision Tree Explore Services
🔍

Semantic Search

AI-powered search with meaning and context

🧠

RAG

Retrieval-Augmented Generation

⭐

Recommendations

Personalized content and product suggestions

AWS Vector Database Decision Tree

Answer a few questions to get personalized recommendations for your vector database and AI use cases.

What is your primary use case?

What type of RAG application are you building?

What are your security and compliance requirements?

What type of recommendations do you need?

What type of similarity matching do you need?

What scale of customer-facing RAG do you need?

What is your specialized domain?

AWS Vector Database Services

Comprehensive overview of AWS services for vector databases and AI applications.

🔍

Amazon OpenSearch Service

Fully managed search and analytics service with vector database capabilities

  • k-NN vector search
  • Hybrid search (text + vectors)
  • Real-time indexing
  • Multi-AZ deployment
  • Security & compliance
Semantic Search Enterprise Scalable
Learn More →
🧠

Amazon Bedrock Knowledge Bases

Fully managed RAG service with built-in vector storage and retrieval

  • Automatic embeddings
  • Managed vector storage
  • Built-in chunking
  • Foundation model integration
  • Serverless architecture
RAG Managed Serverless
Learn More →
🐘

Amazon Aurora PostgreSQL / RDS PostgreSQL

Relational databases with pgvector extension for hybrid applications

  • pgvector extension support
  • ACID compliance
  • Existing PostgreSQL skills
  • Hybrid data models
  • Aurora: Optimized Reads with NVMe caching
Relational Hybrid pgvector
Learn More →
⭐

Amazon Personalize

ML-powered recommendation service using collaborative filtering and deep learning

  • Real-time recommendations
  • AutoML capabilities
  • Multiple algorithms
  • A/B testing
  • Business metrics optimization
Recommendations AutoML Real-time
Learn More →
⚡

Amazon MemoryDB

Ultra-fast in-memory database with vector search capabilities (Valkey and Redis OSS compatible)

  • Sub-millisecond latency
  • Vector similarity search
  • In-memory performance
  • Valkey and Redis OSS compatibility
  • Multi-AZ durability
Ultra-fast In-memory Valkey
Learn More →
📄

Amazon DocumentDB

MongoDB-compatible document database with vector search

  • Vector search capability
  • MongoDB compatibility
  • Flexible schema
  • Automatic scaling
  • Point-in-time recovery
Document MongoDB Flexible
Learn More →

Common Vector Database Use Cases

Explore real-world applications and implementation patterns for vector databases on AWS.

🔍

Semantic Search

What: Search based on meaning and context, not just keywords

Examples:

  • Enterprise document search
  • E-commerce product discovery
  • Knowledge base search
  • Legal document research

Best for: Organizations with large text corpora

Cost considerations: $500-$5000/month depending on scale

Implementation Guide →
🧠

RAG (Retrieval-Augmented Generation)

What: Enhance LLMs with domain-specific knowledge

Examples:

  • Customer support chatbots
  • Technical documentation assistants
  • Legal research tools
  • Medical diagnosis support

Best for: Organizations needing AI with proprietary knowledge

Cost considerations: $1000-$10000/month including LLM costs

Learn More →
⭐

Recommendation Systems

What: Personalized content and product suggestions

Examples:

  • E-commerce product recommendations
  • Content streaming platforms
  • Social media feeds
  • News article suggestions

Best for: Consumer-facing applications with user engagement

Cost considerations: $200-$2000/month based on user volume

Get Started →
🎯

Similarity Matching

What: Find similar items based on features and characteristics

Examples:

  • Image similarity search
  • Fraud detection
  • Duplicate content detection
  • DNA sequence analysis

Best for: Applications requiring pattern recognition

Cost considerations: $300-$3000/month depending on data volume

Technical Docs →
🛡️

Anomaly Detection

What: Identify unusual patterns or outliers in data

Examples:

  • Network security monitoring
  • Financial fraud detection
  • IoT sensor anomalies
  • Quality control systems

Best for: Security and monitoring applications

Cost considerations: $400-$4000/month based on data throughput

Learn More →
🔬

Scientific Research

What: Analyze complex scientific data and find patterns

Examples:

  • Drug discovery
  • Genomic analysis
  • Climate modeling
  • Material science

Best for: Research institutions and pharmaceutical companies

Cost considerations: $1000-$20000/month for compute-intensive workloads

HPC Solutions →

Resources & Getting Started

Curated resources to help you implement vector databases and AI applications on AWS.

📚

Official Documentation

🛠️

Workshops & Tutorials

📝

Blogs & Articles

🎥

Videos & Training

💰

Cost Optimization

🏗️

Architecture Patterns