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Milvus Reviews
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Milvus Reviews
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# Milvus Pricing Overview

[Editedit](https://my.g2.com/milvus/pricings)

Free Trial

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

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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

Milvus has 1 pricing edition that costs $0. A free trial of Milvus is also available.

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

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[
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)

| 

 ![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

[Show More](javascript:void(0);)
 |
| 

 ![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

[Show More](javascript:void(0);)
 |
| 

 ![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

[Show More](javascript:void(0);)
 |

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

More Options
- 

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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Rating Updated (5/5/2026)
Current UserValidated ReviewerIncentivizedSource: Organic

Milvus Comparisons

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##### Categories on G2

[
AWS Marketplace
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AI Search & Retrieval Infrastructure Platforms
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Vector Database
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