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MongoDB Enterprise Advanced vs Qdrant comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Feb 8, 2026

Review summaries and opinions

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

ROI

Sentiment score
5.0
Users report varied ROI from MongoDB Enterprise Advanced, with some benefiting and others finding measurement difficult, considering alternatives.
Sentiment score
5.1
Qdrant offers financial benefits by reducing costs, improving efficiency, and boosting productivity through enhanced response times and HNSW searching.
Actually, with MongoDB, it's difficult to calculate the return on investment; it's too expensive for our use.
Cloud Architect at a computer software company with 1,001-5,000 employees
I would say we see value in money and return on investment with MongoDB Enterprise Advanced.
Co-Founder at Vsigma IT Labs Pvt Ltd
Thanks to Qdrant's open-source nature, our initial licensing and setup costs were nearly zero, allowing for swift testing and launch of our RAG prototype.
Automation Engineer at a educational organization with 11-50 employees
This lowers our LLM input token consumption by roughly 30 to 40 percent, translating directly into lower monthly OpenAI API bills.
MLOps Engineer at a tech services company with 501-1,000 employees
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Customer Service

Sentiment score
6.2
MongoDB Enterprise Advanced offers prompt support for paid users, while Community Edition relies on self-help and community resources.
Sentiment score
4.4
Qdrant's customer service is lauded for its excellent support, active community, and comprehensive documentation, reducing direct support needs.
We have received fairly good support whenever we reached out to the technical teams; they were prompt.
Co-Founder at Vsigma IT Labs Pvt Ltd
I think they resolved it, but it was very long.
Software Engineer at Pioneer Dev AI
It's open source, so we house it on our server.
Chief Ai Scientist at Predictive Systems
The documentation provided by Qdrant covers most queries effectively.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Qdrant's customer support is responsive and developer-focused.
MLOps Engineer at a tech services company with 501-1,000 employees
 

Scalability Issues

Sentiment score
7.5
MongoDB Enterprise Advanced is praised for its high scalability and adaptability, despite some limitations and cost implications.
Sentiment score
5.2
Qdrant excels in scalability, handling large data sets with efficient sharding, supporting rapid expansion and improved performance in Docker.
In CosmoDB, the scalability is much better than with the MongoDB ReplicaSet models.
Cloud Architect at a computer software company with 1,001-5,000 employees
MongoDB is highly scalable.
Head Of Data Science at Mjunction Services
Overall, on a scale of one to ten, I would rate MongoDB an eight; it's mostly because we're still running a monolithic environment on old hardware, so there are some limitations with read-write access.
General Manager at Lytwave
In the recruiting agency project, the reliance on the vector database has expanded from storing hundreds of resumes to thousands.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
When Qdrant is deployed in Docker, it scales really fast, and you can assign multiple CPUs to enhance performance.
Analyst at Synergy Connect
Qdrant is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.
MLOps Engineer at a tech services company with 501-1,000 employees
 

Stability Issues

Sentiment score
7.6
MongoDB Enterprise Advanced is generally stable, reliable for backups, though performance may vary; rated seven to nine in stability.
Sentiment score
7.8
Qdrant is reliable and accurate with high recall, precise vector matching, but requires regular updates and suffers inactive cloud termination.
It's pretty much stable; we have not faced any major challenges or difficulties with MongoDB Enterprise Advanced.
Co-Founder at Vsigma IT Labs Pvt Ltd
Built in Rust, it delivers sub-15 millisecond response times and rock-solid update and write-ahead logging to guarantee that newly indexed data is immediately searchable without dropping queries or producing inconsistent context for LLMs.
MLOps Engineer at a tech services company with 501-1,000 employees
You need to patch Qdrant as soon as patches are released.
Co Founder & CEO at SaYukth Private Limited
It is easy to use whether on LangChain or on its own.
Product Engineer at a tech vendor with 11-50 employees
 

Room For Improvement

MongoDB Enterprise Advanced can improve by enhancing security, support, integration, scalability, analytics, user interface, and mobile features.
Qdrant users seek improved clustering, schema updates, multi-query fusion, intuitive UI, integration, and enhancements in native tools and features.
While solutions for other databases like SQL or PostgreSQL already exist, MongoDB requires additional integrations for developing AI solutions.
Head Of Data Science at Mjunction Services
We have not contracted the security options in our contract because they're too expensive; thus, we implement just encrypted databases and not the security pack.
Cloud Architect at a computer software company with 1,001-5,000 employees
From the AWS standpoint, if robust integration and data warehouse integration specific tools are added in the advanced suite, that would definitely be helpful.
Co-Founder at Vsigma IT Labs Pvt Ltd
Fast large-scale filtering operations could be implemented, such as automatic index suggestions, adaptive query planning, and smart indexing of metadata fields, which would make Qdrant even more efficient.
Product Engineer at a tech vendor with 11-50 employees
While it has clustering functionality, it is not easy to set up, and not everyone can configure the clustering, so there is room for improvement in the clustering configuration.
Co Founder & CEO at SaYukth Private Limited
Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Setup Cost

MongoDB Enterprise Advanced offers cost-effective pricing with various editions and support, valued by small businesses for enterprise transition.
Qdrant offers cost-effective, scalable solutions through open-source access and predictable pricing, suitable for enterprise scaling needs.
We use the free version of MongoDB, so there are no licensing costs.
Head Of Data Science at Mjunction Services
We have to pay approximately 2,000 euros per month for MongoDB.
Cloud Architect at a computer software company with 1,001-5,000 employees
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.
Co-Founder at Vsigma IT Labs Pvt Ltd
The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost nothing.
MLOps Engineer at a tech services company with 501-1,000 employees
Using Qdrant is free.
Chief Ai Scientist at Predictive Systems
Regarding pricing, setup costs, and licensing, since I am using only the free tier of Qdrant Cloud, there are no setup costs involved.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Valuable Features

MongoDB Enterprise Advanced provides scalable, stable, and flexible data management with advanced analytics, NoSQL features, and comprehensive security.
Qdrant provides efficient, cost-effective search and deployment with advanced features, enhanced performance, and seamless cloud integration for AI projects.
It offers flexibility in schema adaptation, allowing us to change the schema and add new data points.
Head Of Data Science at Mjunction Services
In ReplicaSet, it's acceptable, but if your workload needs more performance, and you must pass to a Sharding model, it becomes complicated in MongoDB; in Cosmos DB, however, it's simple.
Cloud Architect at a computer software company with 1,001-5,000 employees
MongoDB has definitely helped us improve our network monitoring and reporting dashboard.
General Manager at Lytwave
The ability of Qdrant to handle high-dimensional vectors for my AI projects is pretty fast, and I think it's the best we have used so far.
Chief Ai Scientist at Predictive Systems
An accuracy boost was definitely observed from 45 to 50% using Faiss to around 85 to 95% using Qdrant, and the users are really happy as they are getting suggested really good schemes that would take a lot of time to find.
Analyst at Synergy Connect
Qdrant supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
CTO at Honeycomb AI
 

Categories and Ranking

MongoDB Enterprise Advanced
Ranking in Open Source Databases
9th
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
82
Ranking in other categories
NoSQL Databases (2nd), Managed NoSQL Databases (4th)
Qdrant
Ranking in Open Source Databases
5th
Average Rating
9.0
Reviews Sentiment
5.4
Number of Reviews
11
Ranking in other categories
Vector Databases (2nd), AI Data Analysis (6th)
 

Mindshare comparison

As of November 2026, in the Open Source Databases category, the mindshare of MongoDB Enterprise Advanced is 5.7%, up from 4.9% compared to the previous year. The mindshare of Qdrant is 4.4%, up from 4.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
Qdrant4.4%
MongoDB Enterprise Advanced5.7%
Other89.9%
Open Source Databases
 

Featured Reviews

Romel Jr Zerna - PeerSpot reviewer
Software Engineer at Pioneer Dev AI
Document data has streamlined prototyping and now supports secure cloud deployments
I use the cloud version of the product. MongoDB Enterprise Advanced is from MongoDB, though I also use DigitalOcean as a third-party provider. Maybe it's all more about preferences. I think MongoDB Enterprise Advanced has weak support on the ORM side, particularly if you're using Prisma. I think mostly the focus now on the tools we have is PostgreSQL. MongoDB Enterprise Advanced is not really used that much on our side for production. It's not really a weak point. It's more of a double-edged sword. If you like document-based management databases and you don't want any migrations, then you go to MongoDB Enterprise Advanced. If you want more secure databases, then you use SQL. On the account management and the way you secure the connection strings, I have utilized advanced security features with MongoDB Enterprise Advanced. I think it's very expensive on the Atlas enterprise side. But if you go to other providers of MongoDB Enterprise Advanced, such as DigitalOcean, it may be harder to set up, but they can save you money.
Pawel Cislo - PeerSpot reviewer
MLOps Engineer at a tech services company with 501-1,000 employees
Adaptive assistant has delivered faster grounded answers and has reduced token costs significantly
The main limitations I notice come down to developer experience and native features rather than performance. Building hybrid retrieval and fusion pipelines still requires considerable manual orchestration and code. Having more built-in multi-query fusion strategies natively inside Qdrant would be a significant time-saver. Additionally, managing dynamic metadata schemas and tracking index build progress during bulk ingest could be more transparent in the web UI. To make things easier for developers, Qdrant could provide more native tools for managing payload schema evolution over time. As metadata needs change in production RAG systems, updating existing payloads across large collections currently requires custom migration scripts. Built-in schema versioning and simpler automated index testing during CI/CD would make running Qdrant in rapidly evolving production environments even smoother. I rate Qdrant 9 out of 10 because its speed, sub-15 millisecond retrieval, and single-stage payload filtering make it top-tier for production RAG pipelines. I deduct one point mainly for developer experience. Setting up multi-query fusion still requires extra boilerplate code, payload metadata schema updates require custom migration scripts, and real-time visibility into HNSW graph indexing progress during bulk ingest could be improved in the web UI.
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Manufacturing Company
9%
Outsourcing Company
8%
Comms Service Provider
8%
Comms Service Provider
13%
Manufacturing Company
12%
Financial Services Firm
10%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business36
Midsize Enterprise13
Large Enterprise39
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise2
 

Questions from the Community

What is your experience regarding pricing and costs for MongoDB?
I think it depends on the provider. For example, on DigitalOcean, they have strict routing. You have to whitelist an IP address, or you risk getting DDoS. Then if you're going for the Atlas one tha...
What needs improvement with MongoDB?
I don't really have a deep dive into it, but I see that MongoDB Enterprise Advanced has RAG and Vertex support. However, we really didn't use it because I think one thing also is that Prisma, the O...
What is your primary use case for MongoDB?
A typical use case for MongoDB Enterprise Advanced is mostly for the database, storing our data.
What is your experience regarding pricing and costs for Qdrant?
I find Qdrant's pricing and licensing extremely straightforward and cost-effective. The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost n...
What needs improvement with Qdrant?
I do not have any suggestions for how Qdrant could be improved. So far, I like what has been done and I have used it several times. I have nothing to add to it now, and I do not wish to add more ab...
What is your primary use case for Qdrant?
My main use case for Qdrant is local AI development. A specific example of how I'm using Qdrant in my development work is that I use it for my RAG development using N8n. In my drug development work...
 

Comparisons

 

Overview

 

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
Find out what your peers are saying about MongoDB Enterprise Advanced vs. Qdrant and other solutions. Updated: November 2026.
920,769 professionals have used our research since 2012.