Database Services
Amazon DocumentDB (NoSQL document-oriented database)
Type: NoSQL (document-oriented), MongoDB-compatible.
Use Cases: Content management, catalogs, user profiles, flexible JSON data storage.
AI Context: Useful for storing semi-structured data (JSON) used by AI applications.
Vector DB Suitability: ❌ Not ideal
Amazon DynamoDB (NoSQL key-value store)
Type: Serverless NoSQL (key-value store), providing high availability and low latency.
Use Cases: High-traffic apps, session management, metadata storage.
AI Context: Suitable for real-time AI workloads like metadata management or recommendation engines.
Vector DB Suitability: ❌ Not ideal
Amazon ElastiCache (in-memory data store)
Type: Managed in-memory data store (Redis/Memcached).
Use Cases: Real-time caching, session storage, leaderboards, real-time analytics.
AI Context: Ideal for caching model inference outputs and providing rapid data retrieval.
Vector DB Suitability: ❌ Not ideal
Amazon MemoryDB (in-memory Redis-compatible database)
Type: Durable, in-memory Redis-compatible database.
Use Cases: Real-time transactional workloads requiring high performance and durability.
AI Context: Excellent for real-time AI applications with stringent speed and durability requirements.
Vector DB Suitability: ❌ Not ideal
Amazon Neptune (Managed graph database) ✅ Support VectorDB
Type: Managed graph database (supports Gremlin, SPARQL).
Use Cases: Fraud detection, recommendation systems, knowledge graphs, relationship analysis.
AI Context: Powerful for AI scenarios involving connected data, semantic searches, and graph analytics.
Vector DB Suitability: ✅ Possible (Graph-based vector search).
Amazon RDS (Managed relational databases) ✅ Support VectorDB
Type: Managed relational databases (MySQL, PostgreSQL, Oracle, SQL Server).
Use Cases: Traditional applications, structured transactional systems (ERP, CRM), structured data analytics.
AI Context: Ideal for structured, relational data usage in AI contexts.
Vector DB Suitability: ✅ Possible (via PostgreSQL with pgvector extension, moderate-scale recommended).
Amazon Aurora (Managed relational databases) ✅ Support VectorDB
Type: High-performance managed relational database compatible with MySQL and PostgreSQL.
Use Cases: Enterprise-grade applications, highly scalable transactional workloads, analytics.
AI Context: Good choice for structured relational data requiring high throughput, performance, and reliability in AI workloads.
Vector DB Suitability: ✅ Possible (using PostgreSQL-compatible Aurora with the pgvector extension), suitable for moderate vector-search scenarios but not optimized for extensive vector workloads.