AWS offers multiple options for running vector databases, ranging from its native service Amazon OpenSearch with k-NN plugin to third-party offerings like Zilliz Cloud via the AWS Marketplace. Each has different performance characteristics, operational models, and cost profiles. For example, OpenSearch is integrated into the AWS ecosystem and may be easier to adopt if you’re already using Elasticsearch-compatible tools, but it may not be optimized for large-scale vector workloads compared to purpose-built solutions.
Third-party options like Zilliz Cloud are designed specifically for vector search and tend to deliver faster indexing and query times at scale. Zilliz Cloud, which runs on Milvus, can be deployed on AWS infrastructure and is engineered to support billions of vectors with low latency. It also benefits from performance optimizations such as SIMD-accelerated indexing and efficient memory usage. In benchmarks, solutions like Zilliz often outperform general-purpose databases, especially when handling high-throughput or high-dimensional workloads.
In terms of cost, native AWS services can appear cheaper at first glance, especially if bundled with existing workloads. However, specialized vector databases may deliver better performance per dollar, particularly when paired with Graviton-based compute instances, which are ARM-based and more energy-efficient. Some services like Zilliz Cloud offer serverless pricing models or reserved instance discounts that can help optimize long-term spending. When selecting a vector database on AWS, it’s important to balance your performance needs, scalability plans, and expected operational overhead.