LanceDB
Best for self-hosting
Why teams pick it
Organizations requiring data sovereignty with local or self-hosted deployment
Open-source alternatives to Zilliz
Compare community-driven replacements for Zilliz in vector databases workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
Zi
Zilliz
Cloud-native vector database service built on Milvus for storing and searching high-dimensional vectors. Supports similarity search, AI applications, and machine learning workloads.Read more
Key stats
- 13Alternatives
- 5Support self-hosting
Run on infrastructure you control
- 12Active development
Recent commits in the last 6 months
- 12Permissive licenses
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to Zilliz.
Start with these picks
These projects match the most common migration paths for teams replacing Zilliz.
Vespa
Privacy-first alternative
Why teams pick it
Keep customer data in-house with privacy-focused tooling.
All open-source alternatives
LanceDB
Multimodal AI lakehouse with fast, scalable vector search
Self-host friendlyActive developmentPermissive licenseRust
Why teams choose it
- Search billions of vectors in milliseconds with advanced indexing and GPU support
- Unified platform for vector similarity, full-text search, and SQL queries
- Store and query multimodal data including text, images, videos, and point clouds
Watch for
Relatively newer project compared to established vector databases
Migration highlight
Semantic Image Search
Index millions of images with embeddings and enable users to search by visual similarity, keywords, or SQL filters across metadata in milliseconds.
Milvus
High-performance vector database built for AI at scale
Active developmentPermissive licenseAI-powered workflowsGo
Why teams choose it
- Distributed architecture with horizontal scaling for billions of vectors
- Hardware acceleration (CPU/GPU) and support for HNSW, IVF, DiskANN, SCANN indexes
- Hybrid search combining dense vectors, sparse vectors, and full-text (BM25)
Watch for
Distributed mode requires Kubernetes expertise for optimal deployment
Migration highlight
Retrieval-Augmented Generation (RAG)
Build AI assistants that retrieve relevant context from billions of documents in real-time to generate accurate, grounded responses with hybrid search combining semantic and full-text retrieval.
RAFT
Reusable CUDA-accelerated primitives for high-performance GPU ML
Active developmentPermissive licenseIntegration-friendlyCuda
Why teams choose it
- Header‑only C++ templates with optional shared library for faster builds
- Host‑accessible runtime APIs usable without a CUDA compiler
- Lightweight Python wrappers (`pylibraft`) and multi‑GPU Dask integration (`raft-dask`)
Watch for
Requires CUDA‑aware development expertise
Migration highlight
Custom clustering algorithm
Leverage RAFT sparse operations and random blob generation to implement a GPU‑accelerated clustering pipeline.
VectorDB
Pythonic vector database with CRUD, sharding, and replication
Self-host friendlyPermissive licenseIntegration-friendlyPython
Why teams choose it
- Full CRUD operations (index, search, update, delete) with DocArray schema definitions
- Multiple backends including InMemoryExactNN and HNSW for different performance profiles
- Native sharding and replication for horizontal scalability
Watch for
Depends on DocArray and Jina ecosystem; less flexibility for standalone use
Migration highlight
LLM Context Retrieval
Enrich language model prompts by retrieving semantically relevant documents from indexed embeddings, improving generation quality with contextual grounding.
Infinity
AI-native database delivering millisecond hybrid search for LLM applications
Active developmentPermissive licenseFast to deployC++
Why teams choose it
- Sub-millisecond hybrid search combining dense vectors, sparse vectors, tensors, and full-text
- 15K+ QPS on million-scale datasets with 0.1ms query latency
- ColBERT reranking and multiple fusion methods (RRF, weighted sum)
Watch for
Requires x86_64 CPUs with AVX2; no ARM or older architecture support
Migration highlight
Retrieval-Augmented Generation (RAG) Pipeline
Enable LLMs to retrieve relevant context from millions of documents in under 1ms, improving answer accuracy while reducing hallucinations through hybrid dense/sparse vector search with ColBERT reranking.
pgvector
Vector similarity search integrated directly into PostgreSQL
Active developmentFast to deployIntegration-friendlyC
Why teams choose it
- Exact and approximate nearest‑neighbor search with configurable indexes (HNSW, IVFFlat).
- Supports multiple vector types (float, half‑precision, binary, sparse) up to thousands of dimensions.
- Multiple distance metrics: L2, inner product, cosine, L1, Hamming, Jaccard.
Watch for
Approximate indexes increase memory consumption.
Migration highlight
Semantic product search
Store product embeddings alongside catalog data and retrieve similar items with a single SQL query.
DingoDB
Distributed multi-modal vector database with MySQL compatibility
Active developmentPermissive licenseIntegration-friendlyJava
Why teams choose it
- Scalar-vector hybrid retrieval with unified SQL interface and MySQL compatibility
- Built-in high availability without external dependencies or complex orchestration
- Automatic elastic data sharding with dynamic splitting and merging
Watch for
Primary implementation in Java may limit performance compared to native alternatives
Migration highlight
E-commerce Product Search
Combine semantic similarity search on product descriptions with structured filters for price, category, and inventory using unified SQL queries
Vespa
Real-time AI-powered search and recommendation at any scale
Self-host friendlyActive developmentPermissive licenseJava
Why teams choose it
- Real-time vector and tensor search with built-in ranking models
- Scalable, fault-tolerant architecture handling billions of documents
- Integrated machine‑learning inference at query time
Watch for
Self‑hosting requires distributed‑systems expertise
Migration highlight
E‑commerce product search with personalized ranking
Delivers sub‑100 ms results combining text relevance, vector similarity, and real-time user behavior models.
Annoy
Fast, memory-efficient approximate nearest-neighbor search with shared on-disk indexes
Active developmentPermissive licenseIntegration-friendlyC++
Why teams choose it
- Static file indexes that can be mmap‑shared across processes
- Supports Euclidean, Manhattan, cosine, Hamming, and dot product metrics
- Low memory usage with optional on‑disk building for massive datasets
Watch for
Index is immutable after build – cannot add items later
Migration highlight
Music recommendation at Spotify
Retrieve similar tracks in milliseconds, powering personalized playlists
Chroma
Embedding database for building LLM apps with memory
Self-host friendlyActive developmentPermissive licenseRust
Why teams choose it
- 4-function API for adding documents, querying by similarity, and filtering results
- Automatic embedding generation with support for custom models and providers
- Seamless scaling from in-memory prototypes to client-server production deployments
Watch for
Rust-based core may require compilation for certain deployment scenarios
Migration highlight
Retrieval-Augmented Generation (RAG)
Query relevant documents from your knowledge base and inject them into LLM context windows for grounded, factual responses
Qdrant
Fast, scalable vector search engine for AI-driven applications
Self-host friendlyActive developmentPermissive licenseRust
Why teams choose it
- Advanced payload filtering with JSON support and complex boolean logic
- Hybrid search combining dense vectors and sparse token-level vectors
- Built-in vector quantization and on-disk storage to reduce RAM usage
Watch for
Requires understanding of vector embeddings to get best results
Migration highlight
Semantic Text Search
Find relevant documents based on meaning rather than keywords, improving retrieval accuracy for chatbots and knowledge bases.
Weaviate
Scalable vector database for semantic search and AI applications
Active developmentPermissive licensePrivacy-firstGo
Why teams choose it
- Millisecond‑scale semantic search over billions of vectors
- Integrated vectorizers (OpenAI, Cohere, HuggingFace) plus self‑provided embeddings
- Hybrid query combining vector similarity, BM25 keyword, and image search in one call
Watch for
Operational complexity for large‑scale clusters
Migration highlight
Retrieval‑Augmented Generation for Q&A
Provides up‑to‑date answers by retrieving relevant documents and feeding them to LLMs directly from the database.
Faiss
High-performance library for similarity search on dense vectors
Active developmentPermissive licenseAI-powered workflowsC++
Why teams choose it
- Scales from thousands to billions of vectors with compressed and exact search methods
- GPU acceleration for fastest nearest neighbor search and k-means clustering
- Multiple distance metrics: L2, dot product, and cosine similarity
Watch for
Compressed methods sacrifice search precision for scalability
Migration highlight
Semantic Search Engine
Search billions of document embeddings in milliseconds to return relevant results for user queries with GPU-accelerated approximate nearest neighbor algorithms
Choosing a vector databases alternative
Teams replacing Zilliz in vector databases workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
- 5 projects let you self-host and keep customer data on infrastructure you control.
- 12 options are actively maintained with recent commits.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Zilliz.