The Cost of Open Source Vector Databases: An Engineer’s Guide to DYI Pricing

The cost of open source vector databases can be complex and challenging to quantify. Engineers often start projects using free software like Milvus, but hardware costs soon arise. Running a distributed database requires setting up dependencies such as Kafka or Pulsar for WAL, etcd for metadata storage, and Kubernetes for orchestration. Additionally, costs include load balancers, monitoring and logging tools, EC2 instances for worker nodes, and storage solutions like S3 or Azure Blob. Some aspects of running an open-source vector database are difficult to quantify, such as capacity planning, setup phase tasks, routine maintenance, troubleshooting latency issues, and disaster recovery plans. Other costs include time to market, engineering morale and retention, and risk mitigation. To assess costs in vector database management, performance tests should be conducted to gather data on how the database handles real-life workloads. Optimizing for cost involves adopting dynamic scaling, adjusting recall accuracy, latency, and throughput according to project needs, and using MMap to store less data in memory. The decision on how to manage a vector database ultimately depends on comparing costs and making an intelligent economic choice based on the most cost-effective option.