# Milvus Reviews **Vendor:** ZILLIZ **Category:** [Vector Database Software](https://www.g2.com/categories/vector-database) **Average Rating:** 4.7/5.0 **Total Reviews:** 12 ## About Milvus Milvus is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus can store, index, and manage a billion+ embedding vectors generated by deep neural networks and other machine learning (ML) models. This level of scale is vital to handling the volumes of unstructured data generated to help organizations to analyze and act on it to provide better service, reduce fraud, avoid downtime, and make decisions faster. Milvus is a graduated-stage project of the LF AI & Data Foundation. ## Milvus Reviews ### 1. Improving AI Testing Efficiency with Scalable Vector Search Using Milvus **Rating:** 5.0/5.0 stars **Reviewed by:** Bharat V. | Lead SDET AI, Legal Services, Enterprise (> 1000 emp.) **Reviewed Date:** April 30, 2026 **What do you like best about Milvus?** One new thing I’ve started to appreciate about Milvus is how well it supports hybrid search and evolving AI use cases. In our recent work, we’ve been exploring scenarios where both vector similarity and metadata filtering are required together. Milvus handles this combination quite effectively, which makes testing more realistic. For example, instead of just validating “similar results,” we can now validate “relevant results within a specific context,” which is closer to how real users interact with AI systems. Another thing I’ve noticed is improved stability when working with larger and more dynamic datasets. As our test data grows and changes frequently, Milvus still maintains consistent performance. This has helped us run more reliable regression tests without worrying about performance drops. I also like how it fits into modern AI workflows that involve retrieval-based systems like RAG. It gives us a solid foundation to test not just similarity, but also how well retrieval impacts final AI responses. One subtle but important benefit is how it enables better experimentation. We can quickly try different indexing or query approaches during testing and see how they affect relevance. This makes it easier to fine-tune AI behavior from a QA perspective. Overall, beyond the core features, Milvus is becoming more useful as we move into more advanced and realistic AI testing scenarios. **What do you dislike about Milvus?** There are a few areas where Milvus can improve, especially from a QA and AI testing perspective. One key improvement would be better guidance around index selection and parameter tuning. Right now, getting the right balance between accuracy and performance often requires trial and error. Having clearer recommendations or built-in suggestions based on use cases would save a lot of time. Observability is another area that could be stronger. When search results are not as expected, it’s not easy to pinpoint whether the issue is with embeddings, indexing, or query behavior. More detailed logs, debugging tools, or visual insights into how results are retrieved would make troubleshooting much easier. The UI could also be enhanced. While API-based interaction works well for development, a more interactive interface for managing collections, running queries, and validating results would help a lot in exploratory testing and quicker validation cycles. From a scaling perspective, simplifying deployment and resource management would be valuable. Running Milvus efficiently in larger environments still requires careful planning, so improvements in auto-scaling or easier configuration would help teams adopt it faster. Lastly, more practical, real-world examples especially focused on testing, validation, and AI quality use cases would make onboarding smoother for QA teams. Overall, Milvus is strong in performance and capability, but improving usability, visibility, and guidance would make it even more effective in real-world workflows. **What problems is Milvus solving and how is that benefiting you?** Milvus solves a key problem for us around validating similarity and relevance at scale in AI and LLM-based applications. In my current QA role, before using Milvus, we struggled with testing embedding-based features like semantic search and recommendations. We relied on smaller datasets and custom scripts, which made validation slow and not very reliable for real-world scenarios. Traditional databases were not efficient for handling high-dimensional vector search. With Milvus, we now store embeddings generated from our models and run similarity queries as part of our test validation process. For example, while testing semantic search, we query nearest neighbors to verify whether the system is returning the most relevant results. This process is now much faster, with responses in milliseconds, which allows us to run multiple validation cycles efficiently. From a workflow perspective, it has improved our automation significantly. We’ve integrated Milvus into our Python-based test pipelines, so instead of manual validation or custom logic, we run automated relevance checks during regression testing of AI features. Another benefit is consistency. Even with large datasets, search performance remains stable, which helps us get repeatable validation results. This is important when testing LLM systems where outputs can vary, so having a reliable similarity layer adds confidence. In terms of ROI, it reduces the need to build and maintain custom vector search solutions. This saves engineering time and allows us to focus more on improving test coverage and quality. Overall, Milvus helps us solve the challenge of scalable vector validation, and it has made our AI testing process faster, more reliable, and easier to manage. ### 2. Native and Efficient Vector Storage for Modern AI **Rating:** 5.0/5.0 stars **Reviewed by:** Pablo H. | Tecnico de Suporte, Small-Business (50 or fewer emp.) **Reviewed Date:** December 18, 2025 **What do you like best about Milvus?** Native architecture for vectors Specifically designed for large-scale vector storage and search, unlike traditional databases that are adapted. Efficient support for dense and sparse embeddings, essential for modern AI models. **What do you dislike about Milvus?** Operational and deployment complexity Intricate distributed architecture: Multiple components (coordinators, workers, etc.) require separate configuration and monitoring. Heavy infrastructure dependency: Need for Kubernetes or container orchestration for production deployment. Limited standalone version: The "standalone" version is not suitable for production, only for testing. **What problems is Milvus solving and how is that benefiting you?** Support for real-time insertions, updates, and deletions without full reindexing (in specific scenarios). ### 3. Best help desk tool 2026 **Rating:** 5.0/5.0 stars **Reviewed by:** Felipe B. | Assistente de TI, Mid-Market (51-1000 emp.) **Reviewed Date:** January 26, 2026 **What do you like best about Milvus?** Highlight for Omnichannel, all modes of service in a single tool. Real-time monitoring of terminals. SLA management, reports, and dashboards. Knowledge base with self-service for end users. **What do you dislike about Milvus?** Configuration complexity for smaller companies, with a wide range of functionalities. Structure more oriented towards the IT sector. Cloud-based platform, any instability interrupts access. **What problems is Milvus solving and how is that benefiting you?** Call handling, monitoring SLA, reports, and Omnichannelity. ### 4. Milvus Evaluation **Rating:** 4.0/5.0 stars **Reviewed by:** Marcos D. | Analista Sr Financeiro, Enterprise (> 1000 emp.) **Reviewed Date:** January 22, 2026 **What do you like best about Milvus?** Clarity in calls and notices. Category and pauses. **What do you dislike about Milvus?** There is no automation by queue and SLA deadline. **What problems is Milvus solving and how is that benefiting you?** Demand for payment, organization. ### 5. milvus - a must have DB **Rating:** 5.0/5.0 stars **Reviewed by:** Chetan B. | Devops engg, Small-Business (50 or fewer emp.) **Reviewed Date:** August 01, 2024 **What do you like best about Milvus?** Milvus stands proud as an outstanding open-source vector database for its effective guide for similarity seek and AI programs. What I like satisfactory approximately Milvus is its distinctly efficient and scalable architecture, which seamlessly handles massive-scale datasets with millions or even billions of vectors **What do you dislike about Milvus?** One major drawback is its quite steep learning curve, especially for users new to vector database and AI applications **What problems is Milvus solving and how is that benefiting you?** For us, this capability translates into faster, extra relevant search outcomes, enhancing user experience in our applications. ### 6. Milvus user experience is amazing **Rating:** 4.5/5.0 stars **Reviewed by:** Bishal B. | Technical Support Engineer II , Mid-Market (51-1000 emp.) **Reviewed Date:** June 19, 2024 **What do you like best about Milvus?** Milvus excels in performing similarity searches on high-dimensional vector data, which is crucial for applications like image retrieval, natural language processing (NLP), and recommendation systems. **What do you dislike about Milvus?** It is complex to setup and configure Milvus in a distributed environment. **What problems is Milvus solving and how is that benefiting you?** One of the core problems Milvus tackles is performing fast and accurate similarity searches on high-dimensional vector data. ### 7. Daily user of Milvus **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** July 30, 2024 **What do you like best about Milvus?** It is one of the fastest vector database out there. **What do you dislike about Milvus?** The code to make collection can be difficult to understand for a beginner. **What problems is Milvus solving and how is that benefiting you?** It is really good at storing the database in vector form and fast to fetch the usefull and relative information from the database using similarity search algorithm using ML libraries. ### 8. Tons of great features and customization options for an open source database product **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** February 14, 2024 **What do you like best about Milvus?** The ability to take the open source code base and build something really powerful for our own use case. **What do you dislike about Milvus?** The learning curve to get everything set up has been a challenge. **What problems is Milvus solving and how is that benefiting you?** Our customers had given us feedback that our current stack restricted how they could deploy our platform. Milvus gives us more control and more options for our end users. ### 9. Milvus is an Exceptional vector database **Rating:** 5.0/5.0 stars **Reviewed by:** Namee O. | Founder, Small-Business (50 or fewer emp.) **Reviewed Date:** February 06, 2024 **What do you like best about Milvus?** Milvus is extremely scalable, easy to use **What do you dislike about Milvus?** Nothing to report whatsoever ........... **What problems is Milvus solving and how is that benefiting you?** Milvus is exceptional at handling large amounts of unstructured data ### 10. Milvus2.x version is mature **Rating:** 5.0/5.0 stars **Reviewed by:** Xingxing D. | Enterprise (> 1000 emp.) **Reviewed Date:** March 20, 2023 **What do you like best about Milvus?** Milvus has a cloud native architecture, excellent performance, rich index types, and can support a variety of application scenarios, making it very suitable for large-scale landing in enterprises. With rich api support, it is very convenient to build a platform in enterprises. We use milvus in image similarity search, video similarity search, recommender system scenarios, by using milvus our system significantly improved performance and stability. **What do you dislike about Milvus?** Milvus should improve the web UI (Attu), currently the function is relatively simple, and also the upsert feature is **What problems is Milvus solving and how is that benefiting you?** We use Milvus in image similarity search, video similarity search, recommender system scenarios. By using Milvus our system significantly improved performance and stability. ### 11. a perfect vector similarity search engine **Rating:** 5.0/5.0 stars **Reviewed by:** morgen z. | Small-Business (50 or fewer emp.) **Reviewed Date:** March 17, 2023 **What do you like best about Milvus?** The Milvus community is very active; many users raise issues and get answers daily. The product update speed is breakneck, and it is constantly optimized. At present, its performance and stability meet our needs. With its efficient and scalable architecture, Milvus can easily handle large-scale data sets, providing fast and accurate results even on complex queries. **What do you dislike about Milvus?** Some docs are not accurate, with little mistakes. **What problems is Milvus solving and how is that benefiting you?** Store vector in Milvus and help us to search similar mobile apps. ### 12. data processing in search, recommendation and AI , including processing of data features and vectors **Rating:** 4.0/5.0 stars **Reviewed by:** liu l. | Small-Business (50 or fewer emp.) **Reviewed Date:** March 23, 2023 **What do you like best about Milvus?** Distributed deployment on k8s。 Much faster than before。 **What do you dislike about Milvus?** Restful mode query speed is too slow，is slower than python api and java api. I hope optimize restful request method。 **What problems is Milvus solving and how is that benefiting you?** Image similarity Text similarity Recommended Recall - [View Milvus pricing details and edition comparison](https://www.g2.com/products/milvus/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-05+03%3A52%3A26+-0500&secure%5Bsession_id%5D=dc7cb383-5186-4760-9223-aa238adfb458&secure%5Btoken%5D=db0fd82512ae60813f05e7a2d467364a3a22aaf782898ede7c59f6d6236be138&format=llm_user) ## Milvus Integrations - [Apache Phoenix](https://www.g2.com/products/apache-phoenix/reviews) ## Milvus Features **Data Indexing** - Semantic Search - Indexing Data **Agentic AI - AWS Marketplace** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration **Retrieval intelligence - AI Search & Retrieval Infrastructure Platforms** - Advanced relevance tuning - Query understanding & expansion - Multistage retrieval & re-ranking - Context-aware & personalized search **Embedding & model management - AI Search & Retrieval Infrastructure Platforms** - Embedding versioning & lifecycle management - Multimodal search support - Pluggable embedding & LLM providers **Filters** - Accurate Search - Single Stage Filtering - Vector Database **LLM retrieval & RAG optimization - AI Search & Retrieval Infrastructure Platforms** - Retrieval pipeline orchestration - LLM-aware retrieval optimization - Hybrid retrieval strategy optimization **Data Enrichment & Index Intelligence - AI Search & Retrieval Infrastructure Platforms** - Incremental & streaming index updates - Built-in data enrichment **Security & governance - AI Search & Retrieval Infrastructure Platforms** - Fine-grained access controls - Data residency & retention policies - Audit logs & retrieval traceability **Operations, observability & reliability - AI Search & Retrieval Infrastructure Platforms** - Search analytics & relevance debugging - High availability & disaster recovery ## Top Milvus Alternatives - [Algolia](https://www.g2.com/products/algolia/reviews) - 4.5/5.0 (428 reviews) - [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews) - 4.5/5.0 (288 reviews) - [SearchStax](https://www.g2.com/products/searchstax/reviews) - 4.5/5.0 (172 reviews)