Cost comparisons and considerations
Understanding the cost structure of different vector database options is essential for making informed implementation decisions. The following table outlines some key cost considerations for various vector database solutions, including both individual databases and managed services. Each option has distinct pricing factors that can impact total cost of ownership (TCO), from pay-as-you-go models to infrastructure and operational costs.
Vector database |
Cost model |
Cost considerations |
|---|---|---|
Amazon Kendra |
Pay as you go, based on queries |
Costs can vary based on the number of queries and the amount of data
indexed. Additional charges can apply for data storage and data transfer.
For more information, see Amazon Kendra pricing |
Amazon OpenSearch Service |
Pay as you go, based on instance hours and storage |
Costs include instance hours, storage (Amazon EBS volumes), data transfer, and
optional UltraWarm storage. Reserved Instances can provide up to 30%
savings. GPU-accelerated instances offer better price-performance for vector
workloads. For more information, see Amazon OpenSearch Service pricing |
Open-source OpenSearch |
Open-source, no direct cost (you don't have to pay to download or use the software, and there are no license costs) |
Costs include infrastructure (such as servers and storage) and operational costs (such as maintenance and monitoring). Organizations need to budget for personnel to manage and maintain the infrastructure. |
Amazon RDS for PostgreSQL with pgvector |
Pay as you go, based on usage |
Costs include database instance types, storage, data transfer, and
backups. Additional charges can apply for data transfer, instance types, and
storage beyond the AWS Free Tier. For more information, see Amazon RDS
pricing |
Amazon DocumentDB |
Pay as you go, based on instance hours and storage |
Costs include instance hours, storage (GB-month), I/O requests, backup
storage, and data transfer. Elastic clusters enable dynamic scaling.
Reserved Instances available for cost optimization. For more information,
see Amazon DocumentDB
pricing |
Amazon MemoryDB |
Pay as you go, based on node hours and data storage |
Costs include node hours (per node type), data storage (GB-hour), snapshot
storage, and data transfer. Reserved Nodes can provide up to 55% savings.
Optimized for high-throughput, low-latency workloads. For more information,
see Amazon MemoryDB
pricing |
Amazon Neptune Analytics |
Pay as you go, based on capacity units |
Costs include Neptune Capacity Units (NCUs), storage (GB-month), and
data transfer. Auto-scaling based on workload with no upfront commitments.
Minimum 128 NCUs required. For more information, see Amazon Neptune pricing |
Amazon S3 Vectors |
Pay as you go, based on storage and requests |
Costs include storage (GB-month), PUT and GET requests, vector index
management, and data transfer. Provides up to 90% cost savings compared to
specialized vector databases. Amazon S3 Intelligent-Tiering and lifecycle
policies available for additional optimization. For more information, see
Amazon S3
pricing |
Amazon Bedrock Knowledge Bases |
Pay as you go, based on usage |
Costs can vary based on the usage of the knowledge base and additional
services such as Amazon OpenSearch Serverless. Additional charges can
apply for data storage, data transfer, and additional features. For more
information about pricing, see Amazon OpenSearch Service pricing |
