SvectorDB

SvectorDB is a serverless vector database built specifically for AWS. It lets you store, index, and search vector embeddings — the numeric representations of data like text, images, or user behavior — without managing any infrastructure. You upload vectors, run similarity queries, and only pay for what you use. There's a free tier with up to 5,000 records per index, and no credit card is required to start. The setup is minimal: you can go from zero to a working prototype with just a few lines of JavaScript or Python code.

What makes SvectorDB stand out is its pay-per-request pricing and instant updates. When you upsert or delete items, changes are reflected immediately — no waiting for eventual consistency. It supports hybrid search using Lucene/ElasticSearch-style filters on key-value pairs, so you can narrow results by metadata. You can use the built-in vectorizers for text (like All-MiniLM-L6-v2) and images (CLIP), or bring your own embeddings. The service also integrates with AWS CloudFormation, making it easy to include in existing infrastructure templates. The team is transparent about trade-offs: there's no snapshotting, a default limit of 1 million records per database, and the company is a small startup, but they emphasize direct support from the people who built the product.

SvectorDB is ideal for developers and AI engineers building recommendation engines, semantic document or image search, or retrieval-augmented generation (RAG) pipelines. Anyone who wants a vector database without the operational overhead of managing servers or worrying about scaling will find the serverless model refreshing. The pricing comparison on the site shows it can be significantly cheaper than alternatives like Pinecone, especially at moderate query volumes.