When using AWS S3 Vector, you should monitor several key performance metrics through Amazon CloudWatch to ensure optimal application performance and cost efficiency. Query latency is the most critical metric, measuring the time from when you submit a QueryVectors request until receiving results. S3 Vector is designed for sub-second response times, so consistently higher latencies might indicate issues with index size, query complexity, or regional placement. You should establish baseline latency measurements for your typical query patterns and set up CloudWatch alarms for deviations that could impact user experience.
Throughput metrics include the number of vector operations per second, covering both query operations and data modification operations like PutVectors and DeleteVectors. Monitoring query frequency helps you understand usage patterns and predict costs, while tracking ingestion rates ensures your data pipeline can keep up with business requirements. Error rates and throttling metrics are equally important, as they indicate when you’re approaching service limits or experiencing configuration issues. Track the success rate of vector operations and monitor for specific error types like dimension mismatches, invalid metadata, or access permission issues.
Storage and cost metrics provide insight into the economic efficiency of your S3 Vector implementation. Monitor the total storage consumed by your vector indexes, the number of vectors stored per index, and the growth rate of your vector data. These metrics help with capacity planning and cost optimization. Additionally, track metadata storage separately since metadata can contribute significantly to overall storage costs. Query result metrics like average number of results returned and result set sizes help optimize your application’s performance and understand user behavior patterns. For applications using filtered queries, monitor the effectiveness of your metadata filters by tracking how they reduce the search space and improve query performance.
Will Amazon S3 vectors kill vector databases or save them?
S3 vectors looks great particularly in terms of price and integration into the AWS ecosystem. So naturally, there are a lot of hot takes. I’ve seen folks on social media and in engineering circles say this could be the end of purpose-built vector databases—Milvus, Pinecone, Qdrant, and others included. Bold claim, right?
As a group of people who’s spent way too many late nights thinking about vector search, we have to admit that: S3 Vectors does bring something interesting to the table, especially around cost and integration within the AWS ecosystem. But instead of “killing” vector databases, I see it fitting into the ecosystem as a complementary piece. In fact, its real future probably lies in working with professional vector databases, not replacing them.
Check out James’ post to learn why we think that—looking at it from three angles: the tech itself, what it can and can’t do, and what it means for the market. We’ll also share S3 vectors’ strenghs and weakness and in what situations you should choose an alternative such as Milvus and Zilliz Cloud.
Will Amazon S3 Vectors Kill Vector Databases—or Save Them?
Or if you’d like to compare Amazon S3 vectors with other specialized vector databases, visit our comparison page for more details: Vector Database Comparison