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Milvus vs Pinecone comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 5, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Milvus
Ranking in Vector Databases
11th
Average Rating
7.4
Reviews Sentiment
7.5
Number of Reviews
5
Ranking in other categories
Open Source Databases (11th)
Pinecone
Ranking in Vector Databases
4th
Average Rating
8.4
Reviews Sentiment
6.5
Number of Reviews
18
Ranking in other categories
AI Data Analysis (7th), AI Content Creation (2nd)
 

Mindshare comparison

As of June 2026, in the Vector Databases category, the mindshare of Milvus is 6.8%, down from 8.4% compared to the previous year. The mindshare of Pinecone is 6.5%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Pinecone6.5%
Milvus6.8%
Other86.7%
Vector Databases
 

Featured Reviews

reviewer2395743 - PeerSpot reviewer
Data Scientist at a tech services company with 1,001-5,000 employees
Helps convert text and other data into a vector space but could provide detailed insights
Milvus is an open-source vector database designed for efficiently handling large-scale, high-dimensional data. It supports various types of data sources and can be deployed on your own premises, which is crucial for maintaining data security. Milvus offers multiple methods for calculating similarities or distances between vectors, such as L2 norm and cosine similarity. These methods help in comparing different vectors based on specific use cases. For instance, in our use case, we find that the L2 distance works best, but you can experiment with different methods to find the most suitable one for your needs. Milvus also includes its own user interface, known as the Milvus Dashboard, which allows you to visualize and manage your data, including embeddings and metadata. You can filter your data based on various criteria, including metadata and file names, which provides flexibility in data management.
Harshwardhan Gullapalli - PeerSpot reviewer
AI Engineer at a educational organization with 51-200 employees
Semantic search has transformed financial document discovery and supports real-time RAG chat
On the integration side, Pinecone's Python SDK is straightforward. It integrates well with the usual AI stack like LangChain and LlamaIndex. That was smooth for me. Where it could improve is around documentation for edge cases. For instance, handling metadata filtering at scale, understanding the right embedding dimensions for different use cases, and best practices for indexing strategies. Those topics felt sparse in the documentation. More real-world tutorials specific to common patterns like RAG or recommendation systems would help developers ramp up faster. On support, the community is helpful, but if you hit something tricky and you are on a lower-tier plan, getting quick answers can be slow. Better-tiered support or more comprehensive troubleshooting guides would be valuable, especially for production deployments where latency is critical.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The solution is well containerized, and since containerization is quick and easy for me, I can scale it up quickly."
"Milvus has good accuracy and performance."
"I like the accuracy and usability."
"The best feature of Milvus was finding the closest chunk from a huge amount of data."
"Milvus offers multiple methods for calculating similarities or distances between vectors, such as L2 norm and cosine similarity. These methods help in comparing different vectors based on specific use cases. For instance, in our use case, we find that the L2 distance works best, but you can experiment with different methods to find the most suitable one for your needs."
"Pinecone helped us in achieving that, and we are now very fast and accurately generating outputs from our database."
"Pinecone is a great platform; it's easy to use with clean SDKs, so it becomes always a go-to option when I think of a vector database."
"Pinecone's integration with AWS was seamless."
"Overall, the time to go through the documentation has drastically reduced, and Pinecone helps me save about two to three hours daily because of the manual effort required to go through the documentation."
"We chose Pinecone because it covers most of the use cases."
"Pinecone is good for POCs and small projects because it's very easy to implement and very easy to use."
"The product's setup phase was easy."
"Compared to any other vector databases, Pinecone is a little ahead due to its latency, scalability, and robust architecture."
 

Cons

"Milvus' documentation is not very user-friendly and doesn't help me get started quickly."
"Milvus could make it simpler. Simplifying the requirements and making it more accessible. It could be more user-friendly."
"I've heard that when we store too much data in Milvus, it becomes slow and does not work properly."
"Milvus has higher resource consumption, which introduces complexity in implementation."
"The main challenge was not performance itself, it was cost."
"The major improvement I am expecting from Pinecone is increased vector size."
"If Pinecone gave us RAG as a service, we'd be more than happy to use that."
"Pinecone is good as it is, but had it been on AWS infrastructure, we wouldn't experience some network lags because it's outside AWS."
"Onboarding could be better and smoother."
"A major reason we did not use Pinecone is that the serverless region was only in the United States; if it were available in India with serverless out-of-the-box implementation, we would have definitely used Pinecone."
"One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata."
"From a cost perspective, I believe Pinecone is a bit expensive compared to other solutions such as FAISS and Milvus, which are free and open source, while Weaviate is more cost-effective at scale, so I would request improvement in Pinecone's pricing structure."
 

Pricing and Cost Advice

"Milvus is an open-source solution."
"Milvus is an open-source solution."
"I have experience with the tool's free version."
"The solution is relatively cheaper than other vector DBs in the market."
"Pinecone is not cheap; it's actually quite expensive. We find that using Pinecone can raise our budget significantly. On the other hand, using open-source options is more budget-friendly."
"I think Pinecone is cheaper to use than other options I've explored. However, I also remember that they offer a paid version."
report
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Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
9%
Manufacturing Company
8%
University
8%
Computer Software Company
10%
University
10%
Manufacturing Company
9%
Financial Services Firm
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise2
Large Enterprise8
 

Questions from the Community

What needs improvement with Milvus?
Milvus could be improved how it could automatically generate insights from the data it holds. Milvus maintains embedding information and knows the relationships between data points. It would be use...
What is your primary use case for Milvus?
Milvus is primarily used in RAG, which involves retrieving relevant documents or data to augment the generation of new content. Milvus helps convert text and other data into a vector space, and the...
What advice do you have for others considering Milvus?
Milvus works well for various use cases and is quite flexible in terms of deployment. For on-premises deployment, you can use the open-source version with Docker. The system requirements are relati...
What needs improvement with Pinecone?
I do not have anything on top of my head for how Pinecone can be improved, as they are really good and it is one of the best vector databases on the planet. If I were to add something about necessa...
What is your primary use case for Pinecone?
Our main use case for Pinecone is that we have human capital data for the last 50 years, as we are a culture operating system that works on human behaviors and organization culture and the research...
What advice do you have for others considering Pinecone?
My advice for others looking into using Pinecone is to first know your use case; previously, we started by building an in-house database search, then realized our requirement was for vector databas...
 

Comparisons

 

Overview

 

Sample Customers

1. Alibaba Group 2. Tencent 3. Baidu 4. JD.com 5. Meituan 6. Xiaomi 7. Didi Chuxing 8. ByteDance 9. Huawei 10. ZTE 11. Lenovo 12. Haier 13. China Mobile 14. China Telecom 15. China Unicom 16. Ping An Insurance 17. China Life Insurance 18. Industrial and Commercial Bank of China 19. Bank of China 20. Agricultural Bank of China 21. China Construction Bank 22. PetroChina 23. Sinopec 24. China National Offshore Oil Corporation 25. China Southern Airlines 26. Air China 27. China Eastern Airlines 28. China Railway Group 29. China Railway Construction Corporation 30. China Communications Construction Company 31. China Merchants Group 32. China Evergrande Group
1. Airbnb 2. DoorDash 3. Instacart 4. Lyft 5. Pinterest 6. Reddit 7. Slack 8. Snapchat 9. Spotify 10. TikTok 11. Twitter 12. Uber 13. Zoom 14. Adobe 15. Amazon 16. Apple 17. Facebook 18. Google 19. IBM 20. Microsoft 21. Netflix 22. Salesforce 23. Shopify 24. Square 25. Tesla 26. TikTok 27. Twitch 28. Uber Eats 29. WhatsApp 30. Yelp 31. Zillow 32. Zynga
Find out what your peers are saying about Milvus vs. Pinecone and other solutions. Updated: April 2026.
900,644 professionals have used our research since 2012.