Milvus Alternatives

Rapidly create and search high-dimensional vector collections with minimal performance impact, scaling to billions of vectors with a distributed architecture.
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Zilliz

If you're looking for another option besides Milvus, Zilliz is a good option. It's a managed vector database service based on open-source Milvus, tuned for large-scale vector data and billion-scale vector search. Zilliz provides a managed service to deploy and scale vector search applications without worrying about the underlying infrastructure, with features like 10x faster vector retrieval speed, 99.95% monthly uptime, and availability on AWS, Azure and GCP. It supports a wide range of use cases, including retrieval augmented generation, recommender systems, and multimodal similarity search.

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Qdrant

Another option is Qdrant, an open-source vector database and search engine designed for fast and scalable vector similarity searches. It's designed for cloud-native architecture and written in Rust for high-performance processing of high-dimensional vectors, Qdrant offers cloud-native scalability and high availability. It can be easily integrated with leading embeddings and frameworks and can be deployed in a variety of ways, including a free tier with a 1GB cluster. Qdrant is well-suited for use cases like advanced search, recommendation systems, and data analysis.

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Vespa

If you're looking for a more complete solution that integrates search with AI, you might want to look at Vespa. This platform supports vector search, lexical search, and search in structured data, which makes it easy to apply AI to large data sets. Vespa combines features like fast vector search and filtering with machine-learned models and supports scalable and efficient machine-learned model inference. It also has auto-elastic data management, which means it can automatically manage data to ensure high end-to-end performance and low latency, and supports a variety of machine learning tools.

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Pinecone

Last, Pinecone is another good option, providing fast querying and retrieval of similar matches across billions of items. It supports low-latency vector search, metadata filtering, real-time updates, and hybrid search. With an average query latency of 51ms and 96% recall, Pinecone is built for scalability and performance, with a range of pricing tiers and support for major cloud providers. It's secure and enterprise-ready, with SOC 2 and HIPAA certifications.

More Alternatives to Milvus

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Jina

Boost search capabilities with AI-powered tools for multimodal data, including embeddings, rerankers, and prompt optimizers, supporting over 100 languages.

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OpenSearch

Build scalable, high-performance search solutions with out-of-the-box performance, machine learning integrations, and powerful analytics capabilities.

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DataStax

Rapidly build and deploy production-ready GenAI apps with 20% better relevance and 74x faster response times, plus enterprise-grade security and compliance.

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Trieve

Combines language models with ranking and relevance fine-tuning tools to deliver exact search results, with features like private managed embeddings and hybrid search.

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Vectorize

Convert unstructured data into optimized vector search indexes for fast and accurate retrieval augmented generation (RAG) pipelines.

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Elastic

Combines search and AI to extract meaningful insights from data, accelerating time to insight and enabling tailored experiences.

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Neum AI

Build and manage data infrastructure for Retrieval Augmented Generation and semantic search with scalable pipelines and real-time vector embeddings.

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Meilisearch

Delivers fast and hyper-relevant search results in under 50ms, with features like search-as-you-type, filters, and geo-search, for a tailored user experience.

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Algolia

Delivers fast, scalable, and personalized search experiences with AI-powered ranking, dynamic re-ranking, and synonyms for more relevant results.

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Neo4j

Analyze complex data with a graph database model, leveraging vector search and analytics for improved AI and ML model performance at scale.

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LlamaIndex

Connects custom data sources to large language models, enabling easy integration into production-ready applications with support for 160+ data sources.

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Redis

Redis is an in-memory data platform for building high-performance, low-latency applications quickly.

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Baseplate

Links and manages data for Large Language Model tasks, enabling efficient embedding, storage, and versioning for high-performance AI app development.

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VectorShift

Build and deploy AI-powered applications with a unified suite of no-code and code tools, featuring drag-and-drop components and pre-built pipelines.

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Couchbase

Unlocks high-performance, flexible, and cost-effective AI-infused applications with a memory-first architecture and AI-assisted coding.

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SingleStore

Combines transactional and analytical capabilities in a single engine, enabling millisecond query performance and real-time data processing for smart apps and AI workloads.

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GoSearch

Instantly search and access information across internal sources with unified search, AI-powered recommendations, and multimodal search capabilities.

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Predibase

Fine-tune and serve large language models efficiently and cost-effectively, with features like quantization, low-rank adaptation, and memory-efficient distributed training.

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Airbyte

Seamlessly integrate data from 300+ sources to destinations, with features like custom connector building, unstructured data extraction, and automated schema evolution.

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Ontotext

Connects disparate data sources with a large-scale knowledge graph, combining AI-infused tools for enterprise knowledge graphs, metadata management, and content analysis.