Milvus vs Upstash Vector

Milvus is an open-source vector database from Zilliz for GenAI and scalable vector search. It supports approximate-nearest-neighbor, filtered, range, hybrid, and full-text search, along with reranking, fetch, and query operations. Metadata filtering, sparse vectors, and index types including HNSW, IVF, FLAT, DiskANN, and SCANN are documented. Deployment choices are Milvus Lite as a Python library, Standalone as a single-machine server, and Distributed on Kubernetes. Documentation recommends Lite for up to a few million vectors, Standalone up to 100 million, and Distributed for 100 million to tens of billions. Lite can run locally without a cloud service for private or sensitive data; an online upgrade from Standalone to a cluster is not currently supported. Official interfaces include REST plus Python, Go, Java, Node.js, and C++ APIs or SDKs. C# is contributed by Microsoft and Rust is under development. Security documentation covers authentication, TLS for gRPC and REST, and role-based access control at collection, database, and instance levels. The Apache 2.0 software is free, and Zilliz Cloud is offered as a managed option.

Upstash Vector is a serverless cloud vector database for applications that need vector search, including NLP, RAG, recommendations, image recognition, clustering and classification. It uses DiskANN for approximate nearest-neighbor queries and also lists FreshDiskANN as an index type. Developers can use its REST API and SDKs, with first-class Python and TypeScript support and additional SDKs for Go and PHP. Similarity options include Euclidean distance, cosine similarity and dot product. Vectors can carry metadata, and query filters can restrict matches using metadata expressions. Hybrid indexes combine dense semantic search with sparse exact-token search and fuse the results. Documented integrations include Vercel AI SDK, Flowise, LangChain, Langflow, LlamaIndex and LlamaParse. The pricing page lists a default top-k limit of 1,000, up to 48 KB of metadata and 1 MB of data per vector. The Free plan allows 10K daily queries or updates and up to 1 GB of data and metadata. Pay as You Go costs $0.40 a month, billed per 100K requests, with separate storage and bandwidth terms. Region availability varies by plan.