[ Milvus Reviews ](https://www.g2.com/products/milvus/reviews) [ Milvus Reviews ](https://www.g2.com/products/milvus/reviews) # Milvus Pricing Overview [Editedit](https://my.g2.com/milvus/pricings) Free Trial ## Milvus Pricing Key Insights Last updated on Jul 30, 2026 * * * Milvus offers **1 pricing edition** , starting at **$0**. Milvus pricing tiers are designed to support different usage levels and team sizes. Milvus also offers a **free trial**. Compare the Milvus pricing table below to figure out the best fit for your needs. Some plans may require you to contact **ZILLIZ** for **custom pricing**. * * * Free & Open Source — $0 Rated 4.7 / 5 \*Pricing information is supplied by the software provider or retrieved from publicly accessible pricing materials. Final cost negotiations must be conducted with the seller. Free & Open Source Free Milvus is open source software and free to use under the Apache 2.0 license. To learn more, visit: https://github.com/milvus-io/milvus/blob/master/LICENSE - Easy to use - Blazing fast - Highly available - Highly scalable - Cloud-native - Feature-rich Show More Milvus is open source software and free to use under the Apache 2.0 license. To learn more, visit: https://github.com/milvus-io/milvus/blob/master/LICENSE Pricing information for Milvus is supplied by the software provider or retrieved from publicly accessible pricing materials. Final cost negotiations to purchase Milvus must be conducted with the seller. Pricing information was last updated on October 10, 2024 Show More ## Milvus Pricing FAQs Generated using AI Is Milvus free, or does it offer a free trial? Yes. According to G2 data, Milvus offers a free plan with limited features. Users can upgrade to paid plans for additional capabilities. Visit the seller's official pricing page to compare plan options and features. How much does Milvus cost in 2026? Pricing details for Milvus are not publicly listed. Users should visit the ZILLIZ's official pricing page or contact the vendor directly for current pricing information. Who is Milvus pricing best suited for? Milvus's pricing is best suited for engineering and AI teams across all company sizes who need production-grade vector search without licensing costs. G2 reviewers span small businesses in computer software and security, mid-market firms in education and aerospace, and enterprises in financial services and internet industries. Small businesses value the open-source flexibility for building custom AI pipelines; mid-market teams leverage it for semantic search and recommendation systems; enterprises deploy it at scale for image, video, and RAG-based retrieval. Teams comfortable with Kubernetes and distributed systems extract the most value. QA teams validating LLM and embedding-based features are a particularly strong fit per G2 reviews of Milvus. What are the key differences between the free and paid versions of Milvus? Milvus is entirely free under the Apache 2.0 open-source license, with no paid tiers gating core functionality. According to G2's pricing data for Milvus, all features — hybrid search, multiple index types, real-time insertions, dense and sparse embedding support, and cloud-native scalability — are available at no cost. Enterprise needs can be addressed through custom quotes. G2 reviewers note that the standalone version suits testing only, while production deployments require Kubernetes or container orchestration, adding infrastructure cost outside Milvus's pricing itself. No features are paywalled; the primary differentiation is deployment scale and operational complexity, not licensing tiers. Is Milvus considered good value for its pricing? Milvus delivers exceptional value given its fully free, Apache 2.0 open-source pricing. G2 reviewers across small business, mid-market, and enterprise segments consistently rate Milvus at 4.0 to 5.0 stars, with high satisfaction tied to millisecond-speed similarity search, scalability to billions of vectors, and active community support. G2 reviews of Milvus highlight meaningful ROI: teams eliminate custom vector search infrastructure, reduce engineering overhead, and accelerate AI testing pipelines. Primary cost concerns are indirect — Kubernetes orchestration, infrastructure planning, and steep learning curves add operational overhead. For teams willing to invest in setup, Milvus's pricing structure on G2 represents strong value relative to capability delivered. ## Top-Rated Alternatives [ ![Elasticsearch](https://images.g2crowd.com/uploads/product/hd_favicon/e7602b1e1f7be279a08b2d0959fa2432/elastic-elasticsearch.svg "Elasticsearch") Elasticsearch 4.5/5(292) ](https://www.g2.com/products/elastic-elasticsearch/reviews) [ ![Algolia](https://images.g2crowd.com/uploads/product/hd_favicon/879f5b5f5ad6000281c7c48f17d0e442/algolia.svg "Algolia") Algolia 4.5/5(453) ](https://www.g2.com/products/algolia/reviews) [ ![Supabase](https://images.g2crowd.com/uploads/product/hd_favicon/71506b5f01eaefca430fe84b5207cf1c/supabase-supabase.jpeg "Supabase") Supabase 4.6/5(193) ](https://www.g2.com/products/supabase-supabase/reviews) [ View All Alternatives ](https://www.g2.com/products/milvus/competitors/alternatives) ## Milvus Alternatives Pricing The following is a quick overview of editions offered by other [Vector Database Software](https://www.g2.com/categories/vector-database) | Product | Price | Features | | --- | --- | --- | | ![Elasticsearch](https://images.g2crowd.com/uploads/product/image/small_square/small_square_da633d2d1c1fd43e5e3cc46236965f55/elastic-elasticsearch.png)[Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews) Elastic Cloud Serverless | Pay As You GoPer Month | A fully automated, usage-based "Search AI" platform. Ideal for variable workloads where compute scales independently from storage, requiring zero capacity planning or infrastructure management. No-ops management: Elastic handles all upgrades and scaling Decoupled compute and storage scaling Specialized VCUs for Ingest, Search, and Machine Learning Managed LLM services for AI Playground and Assistant Rapid deployment of vector and hybrid search applications Search AI Lake for cost-efficient long-term data retention 99.95% Monthly Uptime SLA | | ![Algolia](https://images.g2crowd.com/uploads/product/image/small_square/small_square_e8b7928d2e1cefffe2f9fb20febf2898/algolia.png)[Algolia](https://www.g2.com/products/algolia/reviews) Elevate | Contact Us | Grow further, get full AI offering Rules: 10,000/index 90 days analytics retention Professional Services Available SSO NeuralSearch (Keyword + Semantic Search) Full AI Suite (AI Ranking, AI Synonyms, AI Collections) Dynamic Re-ranking (Revenue & Relevance) Real-Time Personalization (Expanded) Full API & SDK Integration Fetch API Crawl Allowance: Try for free. Volume discounts available. Enhanced SLA Support Plans available as add-ons Foundation Plans available as add-ons Try Guides for free; Volume discounts Available | | ![SearchStax](https://images.g2crowd.com/uploads/product/image/small_square/small_square_ec9891e3cac6641f308f9c38bfd1b323/searchstax.png)[SearchStax](https://www.g2.com/products/searchstax/reviews) SearchStax Site Search - AI-Powered Website Search | Contact UsPer Year | SearchStax Site Search solution is engineered to give marketers the agility they need to optimize site search to drive business outcomes. Gain full visibility into search analytics and make real-time changes with a few clicks. Get full pricing details available at https://www.searchstax.com/pricing/site-search/ Smart Answers: generative AI summaries Site Search Analytics and Dashboard Search and Discovery Experience Self-Service Marketing and Business Tools Developer Tools, APIs, UI Kits and Integrations Crawler Support and SLA Site Search Optimizations AI-Powered Smart Match Assist AI-Powered Smart Ranking | Various alternatives pricing & plans Free Trial Pricing information for the above various Milvus alternatives is supplied by the respective software provider or retrieved from publicly accessible pricing materials. Final cost negotiations to purchase any of these products must be conducted with the seller. ## Milvus Pricing Reviews (1) ![Bharat V.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bharat V.") BV Bharat V. Lead SDET AI Legal Services Enterprise (\> 1000 emp.) 4/30/2026 Business partner of the seller or seller's competitor, not included in G2 scores. "Improving AI Testing Efficiency with Scalable Vector Search Using Milvus" 5/5 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. Review collected by and hosted on G2.com. 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. Review collected by and hosted on G2.com. 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. Review collected by and hosted on G2.com. 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