MongoDB Enterprise Advanced vs Qdrant comparison

MongoDB and Qdrant are both solutions in the Open Source Databases category. MongoDB is ranked #6 with an average rating of 8.4, while Qdrant is ranked #11 with an average rating of 10.0. MongoDB holds a 5.1% mindshare in OSD, compared to Qdrant’s 4.2% mindshare. Additionally, 92% of MongoDB users are willing to recommend the solution, compared to 100% of Qdrant users who would recommend it.

MongoDB Enterprise Advanced

Read 82 MongoDB Enterprise Advanced reviews

6,047 Views
2,419 Comparison Views

92% willing to recommend

Qdrant

Read 2 Qdrant reviews

3,083 Views
1,295 Comparison Views

100% willing to recommend

MongoDB Enterprise Advanced

Qdrant

Comparison Buyer's Guide

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Executive SummaryUpdated on Feb 8, 2026

MongoDB Enterprise Advanced and Qdrant compete in the database management category. MongoDB Enterprise Advanced has the upper hand in broader enterprise-level functionalities due to its robust support and security features, while Qdrant is more suitable for applications requiring advanced vector search capabilities.

Features: MongoDB Enterprise Advanced offers sharding, replication, advanced security, and in-memory processing, ensuring high availability and data protection. Qdrant focuses on vector search capabilities, combining real-time updates with efficient scaling in AI applications.

Ease of Deployment and Customer Service: MongoDB Enterprise Advanced is known for its ease of deployment in complex environments and is supported by comprehensive documentation and enterprise-level customer service. Qdrant offers a smoother deployment process for tasks focused on vector similarity search but may not match MongoDB's support and documentation.

Pricing and ROI: MongoDB Enterprise Advanced involves higher setup costs but promises significant ROI through its extensive range of features and security enhancements. Qdrant, while having a comparatively lower initial cost, offers substantial ROI for AI-driven projects requiring advanced similarity searches.

To learn more, read our detailed Open Source Databases Report (Updated: February 2026).

Buyer's Guide

Open Source Databases

February 2026

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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

MongoDB Enterprise Advanced

Ranking in Open Source Databases

6th

Average Rating

8.2

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

NoSQL Databases (1st), Managed NoSQL Databases (4th)

Qdrant

Ranking in Open Source Databases

11th

Average Rating

9.0

Reviews Sentiment

4.8

Number of Reviews

Ranking in other categories

Vector Databases (4th), AI Data Analysis (17th)

Mindshare comparison

As of March 2026, in the Open Source Databases category, the mindshare of MongoDB Enterprise Advanced is 5.1%, up from 4.4% compared to the previous year. The mindshare of Qdrant is 4.2%, up from 3.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Open Source Databases Mindshare Distribution
Product	Mindshare (%)
MongoDB Enterprise Advanced	5.1%
Qdrant	4.2%
Other	90.7%

Open Source Databases

Featured Reviews

Fabien GOUINEAU

Architecte Cloud at Visiativ SA

Offers reliable engine for legacy needs but requires enhanced cost management and AI features

While MongoDB is a good product, it is also an expensive product for support, and its scalability is acceptable, but the big problem with MongoDB is the cost. For security in MongoDB, we work with encrypted databases by default, but we have not contracted the security options in our contract because it is too expensive, so we only implement encrypted databases without the security pack, which is very expensive for us; in security, we are at the first steps, just using encrypted databases. I think additional features needed in MongoDB include perhaps vector databases, as I think they are not supported right now.

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reviewer2811174

AI Developer at a tech services company with 11-50 employees

Vector search has transformed support workflows and drives faster, more accurate responses

Qdrant can be improved in several ways. A dashboard or UI for re-indexing large collections without downtime and performance degradation would be valuable. The ecosystem around managed backups and cross-region replication could be more seamless for global deployments. Built-in analytics or observability tooling, such as a query performance dashboard and index health monitor, would reduce reliance on external tools. Tighter integration with popular orchestration frameworks like LangChain and LlamaIndex out of the box and more intuitive documentation would be very helpful. Developers need parameters for advanced fine-tuning, such as HNSW settings, and documentation could be clearer. For people without much experience in AI frameworks or vector databases, easier documentation would be helpful. At least the setup part could be simpler. These are some negatives I am observing.

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Quotes from Members

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

Pros

"Easier to maintain the data with its document-based storage."

"MongoDB is fast and efficient."

"It is very easy to create a MongoDB cluster. You can deploy three nodes in one hour. You can do small configurations to enable routing. It is easy to implement."

"MongoDB has definitely helped us improve our network monitoring and reporting dashboard, so I would say it has impacted our operations positively overall."

"The solution's most important aspect is its seamless database."

"The most valuable features of MongoDB are we have a lot of documentation and SQL-based applications that run on it."

"It facilitates the generation of heatmaps for graphical data analysis."

"The solution has good flexibility and very fast performance for searching data."

More MongoDB Enterprise Advanced pros

"Due to its quantization ability, we were able to store the same amount of data in less space, which reduced our cloud bills by 30%."

"Using Qdrant's hybrid search capability has improved my search results."

Cons

"MongoDB should not be used for reporting, analytics, or number-crunching tasks."

"The user interface is not as friendly as Oracle, which is something that can be improved."

"I'd like to see an ID generator. It's very technical but I don't think it has one, so we have to go to great lengths to work around that."

"The performance of the solution could be improved."

"It has certain limitations when it comes to handling hierarchical data, enforcing relationships, and performing complex joins, which should be taken into account when designing databases for applications with intricate data requirements."

"MongoDB should better support small and medium companies. There are a lot of clients out there that are interested, however, they need something lighter and less complex and something not so expensive upfront."

"The performance could be faster."

"I think that MongoDB's search engine should be improved."

More MongoDB Enterprise Advanced cons

"Qdrant can be improved in several ways."

Pricing and Cost Advice

"You only have to pay for the paid version, not the open-source version."

"There is an enterprise license and it could be cheaper. We are using the free open source version."

"I'm using the free version of MongoDB."

"MongoDB is not expensive."

"MongoDB is an open-source product."

"MongoDB is a bit expensive compared to its competitors."

"It's a community edition, so we do not pay anything."

"I don't know, but I have heard from people who procure it that it is much cheaper than Oracle."

More MongoDB Enterprise Advanced pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

Manufacturing Company

University

Computer Software Company

12%

Financial Services Firm

11%

Comms Service Provider

11%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	35
Midsize Enterprise	13
Large Enterprise	38

No data available

Questions from the Community

What do you like most about MongoDB?

MongoDB's approach to handling data in documents rather than traditional tables has been particularly beneficial.

See all answers

What is your experience regarding pricing and costs for MongoDB?

For a small company, the cost of MongoDB Enterprise Advanced is reasonable, but for heavy data usage, we see a little bit of cost pressure but it's acceptable. I will not be able to elaborate on th...

See all answers

What needs improvement with MongoDB?

The integration between data warehouse could be improved. Nowadays, a lot of data is getting generated, so certain ETL flexible scripts with backend database integrations would be an improvement I ...

See all answers

What is your experience regarding pricing and costs for Qdrant?

Using Qdrant is free. We house it and have a VM where we just installed it on the VM.

See all answers

What needs improvement with Qdrant?

I should check if real-time data updates in Qdrant have helped improve my models, as I don't even know they have that feature. A lot of our work is agentic right now, and we have also segmented the...

See all answers

What is your primary use case for Qdrant?

My primary use cases for Qdrant are legal and educational.

See all answers

Comparisons

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Compared 5% of the time

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Compared 5% of the time

InfluxDB vs MongoDB Enterprise Advanced

Compared 5% of the time

Couchbase Enterprise vs MongoDB Enterprise Advanced

Compared 5% of the time

Neo4j Graph Database vs MongoDB Enterprise Advanced

Compared 3% of the time

More MongoDB Enterprise Advanced Competitors

Supabase Vector vs Qdrant

Compared 20% of the time

Microsoft Azure Cosmos DB vs Qdrant

Compared 7% of the time

LanceDB vs Qdrant

Compared 7% of the time

PostgreSQL vs Qdrant

Compared 6% of the time

Faiss vs Qdrant

Compared 6% of the time

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Product Reports

Buyer's Guide

MongoDB Enterprise Advanced

March 2026

Download MongoDB Enterprise Advanced product report

Buyer's Guide

Open Source Databases

February 2026

Download Qdrant product report

Overview

MongoDB Enterprise Advanced is a comprehensive platform renowned for its scalability, user-friendliness, and high performance, underpinned by its flexible document-based storage and open-source model. JSON compatibility, clustering, and security elevate its standing among professionals.

The platform facilitates efficient data management through developer-friendly tools and a strong aggregation framework. MongoDB’s no-schema requirement, supported by community expertise, underlines its adaptability. While its sharding capabilities and affordably support large data volumes, there are aspects such as security enhancement and enterprise tool integration that need attention. Indexing and query optimization pose challenges, alongside high costs. Improvements in analytics and UI could advance its infrastructure further.

What are the key features of MongoDB Enterprise Advanced?

Scalability: Dynamic adjustments to workloads with ease.
JSON Compatibility: Seamless data structuring with JSON support.
Clustering: Enhanced data management through robust clustering.
Security Integration: Built-in security measures for data protection.
Sharding: Efficient distribution of large datasets.

What benefits should users look for in reviews?

Document-Based Storage: Flexibility in managing varied data types.
Open-Source: Access to a broad community and resources.
Low-Cost Implementation: Economic efficiency in deployment.
Scalability: Supports the growth of data without sacrificing performance.

Industries leverage MongoDB Enterprise Advanced for significant roles in data storage within IoT platforms, healthcare apps, public service monitoring, and big data analytics. Companies in logistics and telecommunications find it instrumental for business process management and video content management, benefiting from its seamless integration and unstructured data support.

MongoDB

Qdrant is a powerful tool for efficiently organizing and searching large volumes of data. It is particularly useful for tasks such as data indexing, similarity search, and recommendation systems.

With fast and accurate results, it is suitable for various applications including e-commerce, content management, and data analysis. Users appreciate Qdrant's efficient search capabilities, high performance, and ease of use.

Its quick and accurate retrieval of relevant information allows for easy navigation and analysis of large datasets.

The intuitive interface and straightforward setup process make it accessible to users with varying levels of technical expertise.

Qdrant

Sample Customers

Facebook, MetLife, City of Chicago, Expedia, eBay, Google

1. Airbnb 2. Amazon 3. Apple 4. BMW 5.Cisco 6. CocaCola 7. Dell 8. Disney 9. Google 10. HP 11. IBM 12. Intel 13. JPMorgan Chase 14. Kraft Heinz 15. L'Oreal 16. McDonalds 17. Merck 18. Microsoft 19. Nike20. Oracle 21. PG 22. PepsiCo 23. Procter and Gamble 24. Samsung 25. Shell 26. Sony 27. Toyota 28. Visa 29. Walmart 30. WeWork

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February 2026

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