SvectorDB

What is SvectorDB?

SvectorDB is a serverless vector database built from the ground up for AWS, designed to provide high-performance, cost-effective vector search. With a pay-per-request pricing model, you only pay for what you use, making it up to 20x cheaper than alternatives. There are no minimum fees or upfront costs, and no provisioning or scaling required, allowing you to focus on your product rather than database management.

Key features include instant upserts and deletions with no eventual consistency, Lucene/ElasticSearch-style filtering on key-value pairs, and built-in vectorizers for text and images, or the option to bring your own embeddings. SvectorDB integrates seamlessly with CloudFormation templates and offers an official OpenAPI specification, enabling use with any programming language.

Use cases range from recommendation engines and document/image search to Retrieval Augmented Generation (RAG). For example, Wikipedia embeddings with 1 million vectors at 768 dimensions achieve an average query latency of 9ms and 97.4% recall at 32. The database scales effortlessly from 1 to 1 million vectors, with a default limit of 1 million records per database (increasable by contacting support).

SvectorDB is built by a micro start-up that values transparency, meaning you interact directly with the product builders. While it maintains internal backups, snapshot creation is not offered. This hyper-responsive approach ensures quick adaptation to customer needs, though some may prefer a larger vendor. Overall, SvectorDB delivers a simple, powerful, and affordable vector search solution for AWS users.

Use Cases

Key Features

serverless vector database pay per request pricing instant upserts and deletions Lucene style filtering built in vectorizers OpenAPI specification CloudFormation integration no provisioning required cost effective vector search

Who is it for?

AI engineers, machine learning teams, AWS developers, product builders, recommendation system engineers, search engineers

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