Comparisons anchored on Milvus, the Apache-2.0 distributed vector database under the LF AI and Data Foundation, against lighter vector stores.
A distributed vector database against a vector extension for the PostgreSQL you already run. Compares index types, scaling and multi-tenancy, filtering alongside relational data, the services each needs in production, and when Milvus's disaggregated storage and compute justify running etcd, object storage and extra services.
A distributed vector database built for production scale against a developer-first store that runs in memory, single-node or distributed. Compares deployment modes, dependencies, APIs, index and hybrid search options, access control, and when a Chroma prototype should move to Milvus.
Choosing a Milvus deployment mode: the embedded Python Milvus Lite for prototypes, Standalone in one Docker image for up to about 100 million vectors, and Distributed on Kubernetes for larger collections, with the message queue, object storage and metadata store each needs.
Buying Zilliz Cloud, the managed Milvus service, or running Milvus yourself: Free, Serverless, Dedicated and bring-your-own-cloud options, against the etcd, object storage and Kubernetes operations of self-hosted Milvus Distributed and the data-location questions that push regulated teams to self-host.
When the FAISS library inside your own service is enough and when a Milvus database is worth running: persistence, filtering, multi-tenancy, scaling past one machine, GPU support in both, and the operating cost of Milvus's dependencies.
Tell us what you're working on, whether it's a deployment, an audit, a security test or a cyber range. You'll speak with an engineer who can help you scope it.