Vector database comparison
AWS provides multiple approaches to implementing vector search capabilities, ranging from individual vector databases to Amazon Bedrock Knowledge Bases, which is a fully managed service. When evaluating these options, organizations must consider various aspects including architecture, scalability, integration capabilities, performance characteristics, and security features.
Individual vector databases
The following table provides an overview of key features of several AWS individual vector database solutions, focusing on their architectures, scaling capabilities, data source integrations, and performance characteristics.
Feature |
Amazon Kendra |
Amazon OpenSearch Service |
Amazon RDS for PostgreSQLwith pgvector |
Amazon DocumentDB |
Amazon MemoryDB |
Amazon Neptune Analytics |
Amazon S3 Vectors |
|---|---|---|---|---|---|---|---|
Primary use case |
Enterprise search and RAG |
Distributed search and analytics |
Relational DB with vector support |
Document DB with vector search |
Real-time in-memory vector search |
Graph analytics with vector search |
Cost-optimized vector storage |
Architecture |
Fully managed |
Distributed cluster |
Relational database |
Document-oriented |
In-memory database |
Graph analytics engine |
Serverless object storage |
Data model |
Document-based |
JSON documents |
Relational tables |
JSON documents |
Key-value with JSON |
Property graph |
Object storage |
Vector dimensions |
Managed automatically |
Up to 16,000 |
Configurable |
Up to 2,000 (indexed); 16,000 (unindexed) |
Up to 32,768 |
Configurable |
Up to 4,096 |
Indexing methods |
Automatic |
HNSW, IVF |
HNSW, IVFFlat |
HNSW, IVFFlat |
HNSW |
Native graph and vector |
Automatic |
Distance metrics |
Automatic |
Cosine, Euclidean, dot product |
Cosine, Euclidean, inner product |
Cosine, Euclidean, dot product |
Cosine, Euclidean, inner product |
Cosine, Euclidean |
Cosine, Euclidean |
Query latency |
Sub-second |
Sub-10 ms (GPU-accelerated) |
10-100 ms |
Millisecond |
Sub-millisecond |
Sub-second |
Sub-100 ms |
Scaling model |
Automatic |
Horizontal (add nodes) |
Vertical and read replicas |
Horizontal (add instances) |
Vertical and replicas |
Automatic |
Automatic (serverless) |
Maximum vectors |
Managed |
Billions (cluster-dependent) |
Millions (instance-dependent) |
Millions per collection |
Millions per database |
Billions |
2 billion per index; 10,000 indexes per bucket |
Throughput |
High |
Very high (thousands of QPS) |
Medium |
High |
Very high (millions of requests per day) |
High |
Medium (optimized for infrequent queries) |
Data durability |
99.999999999% (11 9s) |
Configurable with replicas |
99.99% (Multi-AZ) |
99.99% (Multi-AZ) |
99.99% (Multi-AZ) |
99.99% |
99.999999999% (11 9s) |
Consistency model |
Eventual |
Eventual (configurable) |
Strong (ACID) |
Eventual |
Strong |
Strong |
Strong |
Additional capabilities |
40 or more data connectors, NLP |
Full-text search, analytics, dashboards |
SQL queries, ACID transactions |
MongoDB API compatibility |
Redis API compatibility, caching |
Graph algorithms, traversals |
Amazon S3 integration, lifecycle policies |
Pricing model |
Pay per query and storage |
Instance hours and storage |
Instance hours and storage |
Instance hours and storage |
Instance hours and storage |
Capacity units and storage |
Storage, queries, and data transfer |
Cost optimization |
Usage-based |
Reserved instances, auto-scaling |
Reserved instances, Aurora Serverless |
Reserved instances |
Reserved instances |
Auto-scaling |
Up to 90% savings vs specialized DBs |
Best for |
Enterprise search with minimal setup |
High-throughput, low-latency queries |
Hybrid SQL and vector workloads |
MongoDB-compatible apps needing vectors |
Real-time, ultra-low latency apps |
GraphRAG and knowledge graphs |
Long-term, cost-effective storage |
Ideal query pattern |
Frequent enterprise searches |
High-frequency real-time queries |
Mixed SQL and vector queries |
Document queries with semantic search |
Millions of requests per day |
Graph traversals with vector search |
Infrequent queries (minutes to hours) |
Setup complexity |
Low (fully managed) |
Medium (cluster configuration) |
Medium (extension setup) |
Medium (cluster configuration) |
Medium (cluster configuration) |
Low (fully managed) |
Low (serverless) |
Team expertise required |
Minimal |
OpenSearch or Elasticsearch |
PostgreSQL, SQL |
MongoDB |
Redis |
Graph databases |
Amazon S3, basic vector concepts |
Managed service – Amazon Bedrock Knowledge Bases
Amazon Bedrock Knowledge Bases provides a fully managed solution with multiple vector storage options. The following table compares these storage options.
Feature |
Aurora PostgreSQLwith pgvector |
Neptune Analytics |
OpenSearch Service Serverless |
Amazon S3 vectors |
Pinecone |
RedisEnterprise Cloud |
|---|---|---|---|---|---|---|
Primary use case |
Relational DB with vector RAG |
Graph-based vector search for GraphRAG |
Knowledge management RAG |
Cost-optimized vector RAG |
High-performance vector search |
In-memory vector search |
Architecture |
Fully managed relational |
Fully managed graph analytics |
Fully managed serverless |
Serverless object storage |
Fully managed hybrid cloud |
Fully managed in-memory |
Data model |
Relational tables |
Property graph |
JSON documents |
Object storage |
Purpose-built vectors |
Key-value with vectors |
Vector storage |
Through pgvector extension |
Native graph vectors |
Through OpenSearch engine |
Native Amazon S3 vector storage |
Native vector database |
In-memory vectors |
Amazon Bedrock integration |
Native |
Native |
Native |
Native |
Native |
Native |
Automatic ingestion |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Automatic vectorization |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Yes (via Amazon Bedrock) |
Scaling |
Auto-scaling (Aurora Serverless) |
Automatic graph scaling |
Automatic serverless |
Automatic (billions of vectors) |
Auto-scaling pods |
Auto-scaling clusters |
Query performance |
High for relational or vector |
High for graph vectors |
High |
Medium (100 ms or more latency) |
Very high |
Very high |
Maximum vectors |
Millions (instance-dependent) |
Billions |
Billions |
2 billion per index |
Billions |
Millions (memory-dependent) |
Additional capabilities |
SQL queries, ACID transactions |
Graph algorithms, traversals |
Full-text search, analytics |
Amazon S3 lifecycle, tiering |
Metadata filtering, namespaces |
Redis data structures, caching |
Cost optimization |
Moderate (Aurora Serverless) |
Moderate (capacity units) |
High (serverless, pay-per-use) |
Very high (up to 90% savings) |
Moderate (pod-based pricing) |
Low (in-memory premium) |
Best for |
Hybrid SQL/vector workloads |
Connected knowledge graphs |
Full-text with vector search |
Long-term, infrequent-access vectors |
Real-time vector search at scale |
Ultra-low latency needs |
Ideal query pattern |
Mixed SQL and vector queries |
Graph traversals with vectors |
Frequent searches with analytics |
Infrequent retrieval (minutes to hours) |
High-frequency real-time queries |
Millions of requests per second |
Setup with Amazon Bedrock |
Simple (managed by Amazon Bedrock) |
Simple (managed by Amazon Bedrock) |
Simple (managed by Amazon Bedrock) |
Simple (managed by Amazon Bedrock) |
Simple (managed by Amazon Bedrock) |
Simple (managed by Amazon Bedrock) |
Data residency |
AWS Regions |
AWS Regions |
AWS Regions |
AWS Regions |
Multi-cloud (AWS and others) |
Multi-cloud (AWS and others) |
Pricing model |
Instance hours and storage |
Capacity units and storage |
Compute and storage (serverless) |
Storage, queries, and transfer |
Pod hours and storage |
Node hours and storage |
Choosing between individual and managed options
Consideration |
Choose individual vector DB |
Choose Amazon Bedrock Knowledge Bases (managed) |
|---|---|---|
RAG implementation |
You want full control over RAG pipeline |
You want fully managed RAG with minimal setup |
Customization |
You need custom retrieval logic and preprocessing |
Standard RAG patterns meet your needs |
Existing infrastructure |
You already have the database deployed |
You're starting fresh or want simplified management |
Team expertise |
Your team has database administration expertise |
You prefer to focus on application logic, not infrastructure |
Integration complexity |
You need deep integration with existing systems |
You want quick integration with Amazon Bedrock models |
Operational overhead |
You can manage database operations |
You want AWS to handle operations |
Cost structure |
You prefer direct database pricing |
You prefer unified Amazon Bedrock pricing |
Time to market |
You have time for custom implementation |
You need rapid deployment |
