Quick comparison # App Pricing Verified price Open source Platforms Verified facts Score 1 MilvusOpen source — Yes Self-hosted, Linux 3 of 3 66.5 2 PineconeFree plan + paid $20 a month flat (Builder) No Web 9 of 9 64.8 3 QdrantOpen source — Yes Self-hosted, Web 5 of 5 61.8 4 WeaviateOpen source From $45/month Yes Self-hosted, Web 4 of 4 57.5 5 ChromaOpen source — Yes Self-hosted, Linux, macOS +1 5 of 5 55.9
1 Best for: Teams self-hosting vector search that need to scale to very large collections
Open source Self-hostable Self-hosted, Linux
Why switch from Zilliz Cloud
The same open-source engine, free to self-host if you'd rather run the clusters yourself.
Pros Apache 2.0 licence with no paid core Same API from a local Python library up to a distributed cluster Many index types, including disk-based and GPU indexes Managed option available through Zilliz CloudCons Distributed mode needs Kubernetes and has many moving parts Heavier to operate than single-binary options like Qdrant or Chroma Managed hosting is a separate product from a separate siteHow this score is calculated Editorial fit ×0.3 100
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 85
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
2 Best for: Teams building RAG or semantic search who want a vector database with no infrastructure to manage
Free plan + paid Web
Why switch from Zilliz Cloud
Popular serverless vector database with a simpler API and less cluster tuning to think about.
Pros Serverless, so there are no clusters to size or tune Hybrid dense, sparse and full-text indexes Free Starter plan for prototypesCons Closed source with no self-hosted option Production plans carry a monthly minimum spend Costs can be hard to predict with heavy query volumeHow this score is calculated Editorial fit ×0.3 87
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
3 Best for: Teams wanting a permissively licensed vector engine they can run anywhere
Open source Self-hostable Self-hosted, Web
Why switch from Zilliz Cloud
Open-source Rust vector database with a managed cloud and strong payload filtering.
Pros Apache 2.0 licence and easy Docker self-hosting Strong metadata filtering alongside vector search Free-forever cloud cluster for testingCons Free cloud cluster is tiny at 1 GB of RAM Cluster sizing is up to you, unlike serverless Pinecone Fewer built-in AI conveniences than some managed rivalsHow this score is calculated Editorial fit ×0.3 77
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
4 Best for: Developers building RAG or semantic search who want open source with a managed cloud option
Open source Self-hostable Self-hosted, Web
Why switch from Zilliz Cloud
Managed or self-hosted vector search with built-in hybrid keyword search and vectorizer modules, without the Milvus stack.
Pros Open source and self-hostable Hybrid search and built-in embedding integrations Free cloud tier for experimentsCons Free cloud tier is capped at 100,000 objects and one collection Cloud pricing spans several usage dimensions Self-hosting at scale needs Kubernetes experienceHow this score is calculated Editorial fit ×0.3 63
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
5 Best for: Developers who want a simple, free, self-hosted vector store for RAG and semantic search prototypes.
Open source Self-hostable Self-hosted, Linux, macOS, Windows
Why switch from Zilliz Cloud
Much simpler to start with: an embedded open-source vector store for prototypes and small RAG apps, with a hosted option.
Pros Apache 2.0 licence with no usage limits on the open-source version Quick to start with pip or npm and an embedded local mode Combines vector, BM25, full-text, regex and metadata searchCons Hosted Chroma Cloud is being wound down after the Replit deal You run, scale and back up the database yourself Future roadmap now depends on Replit's prioritiesHow this score is calculated Editorial fit ×0.3 57
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula