DataStax Enterprise vs Qdrant comparison

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DataStax Enterprise

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Executive SummaryUpdated on Feb 8, 2026

DataStax Enterprise and Qdrant are products in the database management category. DataStax appears to have the upper hand due to its comprehensive features and robustness in enterprise environments.

Features: DataStax Enterprise offers advanced analytics, rich search integration, and multi-model capabilities that ensure scalability and performance. Qdrant provides efficient vector similarity search, real-time data processing, and quick data retrieval functionality.

Ease of Deployment and Customer Service: DataStax Enterprise offers flexible deployment options, including cloud, hybrid, and on-premise solutions, along with extensive customer service support. Qdrant's simplicity in its deployment model and quick setup are user-friendly, although it offers fewer deployment configurations.

Pricing and ROI: DataStax Enterprise has a higher initial setup cost that is justified by its rich feature set which can lead to better ROI for large-scale operations. Qdrant delivers a cost-effective initial setup suitable for small to medium applications with efficient resource usage ensuring a solid ROI.

To learn more, read our detailed DataStax Enterprise vs. Qdrant Report (Updated: August 2026).

DataStax Enterprise vs. Qdrant

August 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:

ROI

Sentiment score

5.4

DataStax Enterprise accelerates development, reduces deployment time, and ensures 99.9% uptime, enhancing efficiency despite version update challenges.

Sentiment score

5.5

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

We have seen a return on investment with DataStax Enterprise as we saved a lot of money and time, despite investing more on infrastructure; our ongoing business success with a 99.9% uptime helps us earn more.

AshishSingh3

Senior database engineer at ToTheNew

Earlier it was around 15 months, and we have been able to deploy and scale our application within 10 months.

Arinjay Gangwar

Software Developer at a consultancy with 11-50 employees

We realized significant cost savings after migrating to DataStax Enterprise, as the previously slow critical queries now run significantly faster, improving efficiency and performance metrics across the board.

Pdixit Dixit

Solution Architect at a consultancy with 51-200 employees

For more quotes and insights, download the DataStax Enterprise report

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

7.8

DataStax Enterprise's customer service is praised for its responsive support, swift issue resolution, and excellent escalation process.

Sentiment score

4.8

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

I would rate the customer support a 10 because I always receive the help I need from them.

Pooja Singh

Devops Specialist at a tech vendor with 10,001+ employees

Any requests I have raised receive prompt responses, often within minutes.

Pdixit Dixit

Solution Architect at a consultancy with 51-200 employees

Real-time transaction processing, both reads and writes, is where DataStax Enterprise shines the most.

For more quotes and insights, download the DataStax Enterprise report

Senior Software Engineer at Deloitte

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

8.7

DataStax Enterprise offers scalable, multi-region deployment, reducing costs and enhancing efficiency with auto-scaling and improved response times.

Sentiment score

5.7

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

Overall, we saw a decrease in operational costs due to better resource usage and less manual work, which made my team more efficient and allowed us to focus on new projects.

Vivek Yadav

DevSecOps Engineer (Software Development Engineer II) at a financial services firm with 1,001-5,000 employees

DataStax Enterprise's scalability is very fast with linear scalability and hence is very scalable.

For more quotes and insights, download the DataStax Enterprise report

Senior Software Engineer at Deloitte

The active-active architecture helped us really scale and provide data to both Singapore and Indian users.

Shubham-Kumar

Senior Engineer at a financial services firm with 10,001+ employees

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.

For more quotes and insights, download the Qdrant report

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

Stability Issues

Sentiment score

8.2

DataStax Enterprise offers high stability, reducing downtime by 40%, and effectively handles large-scale data, despite occasional node issues.

Sentiment score

7.9

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

DataStax Enterprise provides enough stability for our organization, and scaling can be done up to terabytes and petabytes.

Shubham-Kumar

Senior Engineer at a financial services firm with 10,001+ employees

After using DataStax Enterprise, our system downtime dropped by approximately 40%, helping us avoid lost revenue.

Vivek Yadav

DevSecOps Engineer (Software Development Engineer II) at a financial services firm with 1,001-5,000 employees

For more quotes and insights, download the DataStax Enterprise report

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

DataStax Enterprise users desire improved backup, faster restoration, user-friendly UI, intuitive monitoring, cost efficiency, automation, and enhanced support.

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

There should be an alternative for restoring where data can be provided to live traffic so it does not impact customers in the case of a disaster.

Pooja Singh

Devops Specialist at a tech vendor with 10,001+ employees

Better compatibility with prior versions in terms of codebases should also be improved.

For more quotes and insights, download the DataStax Enterprise report

Senior Software Engineer at Deloitte

For example, it can implement some cost optimization where the license can be expensive, and compared to open-source Cassandra, cost is a concern.

Shubham-Kumar

Senior Engineer at a financial services firm with 10,001+ employees

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

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

For smaller organizations working under a tight budget, it might not be very affordable compared to other alternatives.

For more quotes and insights, download the DataStax Enterprise report

Senior Software Engineer at Deloitte

I was not involved in the licensing for DataStax Enterprise, which is managed by a different team, but I understand it operates on a core-based licensing model, which is standard.

Pdixit Dixit

Solution Architect at a consultancy with 51-200 employees

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

DataStax Enterprise excels in scalability, reliability, and management, enhancing productivity and application performance with advanced features and tools.

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

The scaling and speed of data access have benefited my team because the scaling and the speeding of data provide linear scale as well as multi-data centers' real-time replication of data such that we can maintain uptime even with the loss of multiple data centers.

For more quotes and insights, download the DataStax Enterprise report

Senior Software Engineer at Deloitte

I can confirm that the outcomes of using DataStax Enterprise show that our database uptime has increased drastically to around 99.9%.

AshishSingh3

Senior database engineer at ToTheNew

DataStax Enterprise has positively impacted my organization because during research for a NoSQL database, developers are very positive about using DataStax Enterprise because of its really easy setup and the querying to the database is very efficient.

Arinjay Gangwar

Software Developer at a consultancy with 11-50 employees

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

DataStax Enterprise

Ranking in Vector Databases

15th

Average Rating

8.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

NoSQL Databases (11th)

Qdrant

Ranking in Vector Databases

2nd

Average Rating

8.8

Reviews Sentiment

5.8

Number of Reviews

Ranking in other categories

Open Source Databases (5th), AI Data Analysis (6th)

Mindshare comparison

As of September 2026, in the Vector Databases category, the mindshare of DataStax Enterprise is 2.1%, up from 0.7% compared to the previous year. The mindshare of Qdrant is 6.3%, down from 8.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Mindshare Distribution
Product	Mindshare (%)
Qdrant	6.3%
DataStax Enterprise	2.1%
Other	91.6%

Vector Databases

Featured Reviews

Pooja Singh

Devops Specialist at a tech vendor with 10,001+ employees

Reliable backup and recovery has protected critical telecom customer data in daily operations

When I was taking the backup with a data volume of 15 to 20 GB, the full backup running for the first time did not provide metrics to understand what percent of progress had been made. We had to read the logs, but we did not see any percentage in those logs. This is an improvement item for DataStax Enterprise to provide metrics, a bar, or a percentage so we can grasp how much data has been backed up or restored. There are no graphs available to make us aware of how long the process will take to complete, which is a drawback I noticed during backup and restore. In addition, I notice a huge time lag during restoration because each node takes a lot of time to be added to the pool and to the cluster node, one by one. The entire time the production is down presents a concerning situation. There should be an alternative for restoring where data can be provided to live traffic so it does not impact customers in the case of a disaster.

Read full review

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

14%

Construction Company

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	3
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	10
Midsize Enterprise	2

Questions from the Community

What is your experience regarding pricing and costs for DataStax Enterprise?

My experience with pricing, setup cost, and licensing is pretty good. I do not find any difficulty in it.

What needs improvement with DataStax Enterprise?

The user interface of DataStax Enterprise can be simpler and easier to use, especially for new users, and I think it should be improved in that area. Additionally, more built-in monitoring and aler...

What is your primary use case for DataStax Enterprise?

My main use cases for DataStax Enterprise are building a scalable and high availability application that needs to handle large amounts of data across multiple locations, and for real-time analytics...

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...

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DataStax Enterprise

September 2026

Download Qdrant product report

September 2026

Overview

DataStax Enterprise offers a high-performance, scalable database solution designed for modern data requirements, supporting a wide array of use cases that demand real-time analytics and robust security.

Focusing on delivering powerful distributed databases, DataStax Enterprise integrates the open-source foundation of Apache Cassandra, delivering enhanced features for enterprises. It supports mission-critical applications at scale, providing real-time query capabilities and fault tolerance. Designed with high availability and operational efficiency, it supports complex data models and simplifies management with advanced tools for monitoring and repair.

What are the standout features of DataStax Enterprise?

Advanced Security: Provides comprehensive data protection with encryption and identity management.
Graph Database Support: Allows complex data relationships to be easily modeled and queried.
Search Integration: Facilitates full-text search capabilities combined with Cassandra's strengths.
Analytics: Offers integrated analytics with Apache Spark for real-time data processing.
Multi-Cloud Support: Ensures easy deployment across different cloud environments without vendor lock-in.

What benefits should reviews highlight?

Scalability: Seamlessly expands to accommodate growing data volumes and user demands.
Performance: Maintains high-speed data processing even at massive scales.
Resilience: Provides high availability to ensure business continuity.
Cost Efficiency: Enables optimization of infrastructure investments with flexible deployments.

In industries such as finance, telecommunications, and retail, DataStax Enterprise is implemented to handle immense data workloads, often leveraging its capabilities for fraud detection, personalized customer experiences, and real-time decision-making. Its deployment in these sectors highlights its adaptability and performance in demanding environments.

IBM

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

ING, Netflix, UBS, eBay, Constant Contact, Aeris, Arise, ClearCapital, Dyn, Engine, Noble Group, Pantheon, Target

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