MongoDB Enterprise Advanced vs Qdrant comparison

MongoDB Enterprise Advanced

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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 MongoDB Enterprise Advanced vs. Qdrant Report (Updated: September 2026).

MongoDB Enterprise Advanced vs. Qdrant

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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:

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

5.5

Qdrant boosts ROI by cutting costs, improving productivity, and enhancing efficiency through payload filtering and open-source flexibility.

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.

For more quotes and insights, download the MongoDB Enterprise Advanced report

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.

Amrit Dash

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.

For more quotes and insights, download the Qdrant report

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

4.8

Qdrant customer service excels with developer-focused support via Discord and documentation, with reduced need for direct contact.

We have received fairly good support whenever we reached out to the technical teams; they were prompt.

For more quotes and insights, download the MongoDB Enterprise Advanced report

Co-Founder at Vsigma IT Labs Pvt Ltd

I think they resolved it, but it was very long.

Romel Jr Zerna

Software Engineer at Pioneer Dev AI

It's open source, so we house it on our server.

AllanTan1

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.

For more quotes and insights, download the Qdrant report

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

5.7

Qdrant excels in scalability and performance, efficiently managing large datasets, particularly when deployed in Docker for enhanced growth.

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.

For more quotes and insights, download the MongoDB Enterprise Advanced report

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.

Cameron-Bashaw

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.

Vedant Wani

Analyst at Synergy Connect

Qdrant is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.

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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

7.9

Qdrant, built in Rust, is praised for reliability, fast queries, and precision, despite minor cloud termination limitations.

It's pretty much stable; we have not faced any major challenges or difficulties with MongoDB Enterprise Advanced.

For more quotes and insights, download the MongoDB Enterprise Advanced report

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.

China Venkanna Varma Ponnamanda

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.

reviewer2848647

Product Engineer at a tech vendor with 11-50 employees

For more quotes and insights, download the Qdrant report

Room For Improvement

MongoDB Enterprise Advanced can improve by enhancing security, support, integration, scalability, analytics, user interface, and mobile features.

Qdrant needs developer experience enhancements, including multi-query fusion, embedding support, schema management, and improved deployment, UI, and documentation.

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.

For more quotes and insights, download the MongoDB Enterprise Advanced report

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.

reviewer2848647

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.

China Venkanna Varma Ponnamanda

Co Founder & CEO at SaYukth Private Limited

Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.

For more quotes and insights, download the Qdrant report

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 provides cost-effective, predictable billing and zero upfront investment, with open-source access and easy setup options.

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.

For more quotes and insights, download the MongoDB Enterprise Advanced report

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.

AllanTan1

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.

For more quotes and insights, download the Qdrant report

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 fast, efficient vector search with hybrid indexing, Python support, and open-source configuration for scalable 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.

For more quotes and insights, download the MongoDB Enterprise Advanced report

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.

Cameron-Bashaw

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.

AllanTan1

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.

Vedant Wani

Analyst at Synergy Connect

Qdrant supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.

Mir Shahzad Mubeen

CTO at Honeycomb AI

For more quotes and insights, download the Qdrant report

Categories and Ranking

MongoDB Enterprise Advanced

Ranking in Open Source Databases

8th

Average Rating

8.2

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

NoSQL Databases (2nd), Managed NoSQL Databases (4th)

Qdrant

Ranking in Open Source Databases

5th

Average Rating

8.8

Reviews Sentiment

5.8

Number of Reviews

Ranking in other categories

Vector Databases (2nd), AI Data Analysis (6th)

Mindshare comparison

As of September 2026, in the Open Source Databases category, the mindshare of MongoDB Enterprise Advanced is 6.4%, up from 4.5% compared to the previous year. The mindshare of Qdrant is 4.3%, up from 4.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Open Source Databases Mindshare Distribution
Product	Mindshare (%)
Qdrant	4.3%
MongoDB Enterprise Advanced	6.4%
Other	89.3%

Open Source Databases

Featured Reviews

Have managed customer transaction data efficiently and supported high-demand workloads with reliable performance

Co-Founder at Vsigma IT Labs Pvt Ltd

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 could see. I will not be able to clearly say there is currently a deficiency there. All our DBs are integrated with a backend data warehouse, not necessarily AWS. When third-party data warehouses are integrated, we are seeing some ETL job performance issues. It is a one-off scenario so we have not thoroughly done any troubleshooting on that. It could be platform or third-party related issue. From the AWS standpoint, if robust integration and data warehouse integration specific tools are added in the advanced suite, that would definitely be helpful.

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Adaptive assistant has delivered faster grounded answers and has reduced token costs significantly

MLOps Engineer at a tech services company with 501-1,000 employees

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

15%

Manufacturing Company

Outsourcing Company

Comms Service Provider

13%

Manufacturing Company

11%

Financial Services Firm

10%

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	36
Midsize Enterprise	13
Large Enterprise	39

By reviewers
Company Size	Count
Small Business	10
Midsize Enterprise	2

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?

Qdrant is available through a containerized Docker, but a normal deployment in Qdrant is not there, and that can actually be worked out. That was one aspect I thought about, because I need to have ...

What is your primary use case for Qdrant?

We have a full-fledged RAG system using Qdrant Vector Database, and that is how it has benefited us. For example, we have implemented a techno-commercial evaluator using that, and it is in producti...

MongoDB Atlas vs MongoDB Enterprise Advanced

Comparisons

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Firebird SQL vs MongoDB Enterprise Advanced

Compared 3% of the time

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PostgreSQL vs Qdrant

Compared 9% of the time

Supabase vs Qdrant

Compared 9% of the time

MySQL vs Qdrant

Compared 7% of the time

Microsoft Azure Cosmos DB vs Qdrant

Compared 6% of the time

Vespa vs Qdrant

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Product Reports

MongoDB Enterprise Advanced

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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.

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

MongoDB Enterprise Advanced vs. Qdrant