Best Self Hosted Alternatives to Zilliz Cloud (Milvus)

Meilisearch is an open source search engine exposed through an API, designed to provide fast, relevant search experiences for websites and applications. It combines traditional full-text search with optional vector-based semantic retrieval to support hybrid search and AI retrieval workflows.

Key Features

• REST API for indexing documents and running searches
• Search-as-you-type with low-latency results
• Typo tolerance and configurable ranking/relevancy tuning
• Filtering, faceting, and sorting for building rich search UIs
• Geosearch for location-based filtering and ranking
• Vector storage and vector search for semantic retrieval and hybrid search
• API key-based access control, including tenant tokens for multi-tenancy

Use Cases

• Site and application search with instant results and typo tolerance
• E-commerce and catalog search with facets, filters, and sorting
• AI retrieval and RAG pipelines using hybrid (full-text + vector) search

Limitations and Considerations

• Some advanced capabilities (for example sharding and certain snapshot features) are reserved for the Enterprise Edition under a non-open-source license
• Telemetry is enabled by default but can be disabled

Meilisearch is well-suited for teams that want a developer-friendly search API that is easy to integrate, performs well out of the box, and can evolve from classic keyword search to modern hybrid AI retrieval as needs grow.

Typesense is an open source search engine designed for low-latency, “search-as-you-type” experiences. It focuses on developer-friendly operations and an easy-to-use API, while supporting both traditional full-text search and modern vector-based retrieval.

Key Features

• Typo-tolerant fuzzy search optimized for instant results
• Search-as-you-type autocomplete and relevance tuning at query time
• Faceting, filtering, grouping/distinct, and dynamic sorting
• Geo search for location-based queries
• Synonyms and pinning/merchandising controls for curated results
• Vector and semantic search, including hybrid retrieval patterns
• Scoped API keys and multi-tenant access patterns
• High-availability options via replication

Use Cases

• Site and in-app search for documentation, content, and product catalogs
• E-commerce discovery with facets, filtering, sorting, and pinned results
• Semantic search and hybrid keyword+vector retrieval for knowledge bases

Typesense is well-suited for teams that want a streamlined search stack with strong defaults, low operational complexity, and an HTTP API that integrates easily into modern applications.

OpenSearch is an Apache 2.0-licensed, community-driven distributed search and analytics engine designed for indexing and querying large volumes of data. It provides a RESTful API and is commonly used as the core search backend for applications and as a foundation for log and event analytics.

Key Features

• Distributed indexing and search for horizontal scalability and high availability
• RESTful API for indexing, querying, and cluster operations
• Full-text search and relevance scoring for unstructured and semi-structured data
• Aggregations for analytical queries over large datasets
• Extensible architecture with plugins for additional capabilities

Use Cases

• Powering application search for websites, product catalogs, and documentation
• Centralized log search and analytics for infrastructure and applications
• Building analytics experiences over event, text, and time-based datasets

Limitations and Considerations

• Operational complexity can be significant for large clusters (sizing, tuning, shard management)
• Query performance and cost depend heavily on index design and workload patterns

OpenSearch is a strong fit when you need scalable search and analytics with an open ecosystem and a well-known REST interface. It can serve as a primary search backend or as a core component in broader observability and analytics pipelines.

Manticore Search is an open-source search database designed for building fast full-text and hybrid (text + filters) search applications. It provides a SQL-first experience with MySQL protocol compatibility and an HTTP JSON API for programmatic indexing and querying.

Key Features

• Full-text search with relevance ranking (BM25-style), highlighting, and many match operators
• SQL interface with MySQL protocol support for querying and management
• HTTP JSON API, including Elasticsearch-compatible bulk writes for easier ingestion
• Real-time indexing so newly inserted or updated documents are searchable immediately
• Advanced search capabilities such as faceting, geo-spatial search, autocomplete, fuzzy search, and spell correction
• Vector search (KNN) to support semantic and similarity search scenarios
• Multiple storage modes, including row-wise and optional columnar storage for larger datasets
• High-availability options including built-in replication and load balancing
• Built-in backup and restore tooling (including SQL BACKUP)

Use Cases

• Application search for catalogs, marketplaces, documentation, and knowledge bases
• Log/event search and analytics-style querying on large datasets
• Hybrid search combining keyword relevance with filters, geo, and vector similarity

Limitations and Considerations

• Not fully ACID-compliant; transaction semantics differ from general-purpose relational databases
• Some features (such as columnar storage) may require additional components and tuning depending on workload

Manticore Search is well-suited when you need a high-performance, resource-efficient search engine with familiar SQL workflows and flexible APIs. It aims to be an approachable alternative to Elasticsearch for many search and analytics scenarios.

Apache Solr is an open-source, high-performance search platform that extends the Apache Lucene library to provide full-text, vector and geospatial search capabilities. It exposes REST-like APIs, a responsive admin UI and tooling for indexing, querying and cluster management. (lucene.apache.org)

Key Features

• Full-text search with advanced query parsing, scoring, spellcheck, highlighting and suggestions. (solr.apache.org)
• Dense-vector (ANN) search and text-to-vector integration for neural/semantic search workflows. (solr.apache.org)
• Faceting, aggregations and JSON Facet API for powerful drill-down and analytics. (solr.apache.org)
• Scalable SolrCloud mode with distributed indexing, replica management and centralized configuration. (solr.apache.org)
• Built-in admin UI, metrics (JMX), plugin/extension points and rich document parsing (Apache Tika integration). (solr.apache.org)

Use Cases

• Site and application search for e-commerce, media catalogs and documentation with faceted navigation and relevance tuning.
• Semantic search and recommendations using dense-vector indexing and external embedding providers.
• Large-scale, multi-tenant search deployments requiring distributed indexing, high availability and automated failover (SolrCloud).

Limitations and Considerations

• SolrCloud relies on ZooKeeper for cluster coordination, which adds an operational component to manage and monitor. (solr.apache.org)
• Vector search and "text-to-vector" features typically require external embedding services or model integrations to produce vectors; performance and storage costs should be evaluated for large vector collections. (solr.apache.org)

Apache Solr is a mature, extensible search engine suited for both small projects and massive, production search clusters. It combines Lucene search primitives with cluster orchestration, extensibility and modern features like neural search to support a wide range of search and discovery applications. (lucene.apache.org)