Upstash Vector: Serverless Vector Database for AI and LLMs

Upstash Vector is a serverless vector database designed to efficiently store and query high-dimensional vector embeddings for AI models and large language models. This database aims to simplify the management of vector embeddings by utilizing Approximate Nearest Neighbor (ANN) algorithms, specifically DiskANN and FreshDiskANN, to optimize search performance while reducing resource consumption. Upstash Vector supports various similarity functions, including cosine similarity, Euclidean distance, and dot product, to measure vector similarity. It also allows for the storage of JSON metadata alongside vector embeddings, with plans to introduce metadata filtering for refined searches. Initially deployed on AWS regions us-east-1 and eu-west-1, the service offers a multi-tenant model with flexible pricing plans, including free, pay-as-you-go, and fixed options. Upstash Vector provides a REST API and SDKs in Python, JavaScript/TypeScript, and Go to facilitate interactions with the database, while future roadmap features include metadata filtering, index replication, and namespaces. The database differentiates itself in a crowded market by balancing performance, developer experience, and cost-effectiveness, targeting both startups and enterprises.