Cassandra vs Qdrant comparison

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Cassandra

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Executive Summary

We performed a comparison between Cassandra and Qdrant based on real PeerSpot user reviews.

Find out in this report how the two Vector Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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

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Cassandra vs. Qdrant

August 2026

Helped 913,806 peers since 2012

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

Cassandra

Ranking in Vector Databases

12th

Average Rating

8.0

Reviews Sentiment

6.0

Number of Reviews

Ranking in other categories

NoSQL Databases (5th)

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 Cassandra is 3.9%, up from 1.9% 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%
Cassandra	3.9%
Other	89.8%

Vector Databases

Featured Reviews

Monirul Islam Khan

Head, Data Integration & Management at a non-profit with 10,001+ employees

Has maintained secure document storage and efficient data distribution with peer-to-peer architecture

The functions or features in Cassandra that I have found most valuable are that it is a distributed system similar to Mongo. It's good enough for comparison with another SQL database, so it's smooth and organized for distributed database system. The peer-to-peer architecture in Cassandra is helpful for network decentralization, and I have already introduced that feature. Cassandra features in peer-to-peer as well as another monitoring, so basically, it's good enough for our service. The tunable consistency level in Cassandra is good, and we are using that feature already. In terms of built-in caching and lightweight transactions in Cassandra, the transaction level is good, and it's optimized, so there are no more issues in that database. Based on my experience, Cassandra is good for document management system, as well as distributed database system, and the automatic recovery process is there. Additionally, the database monitoring system or auditing system is well-comparable with other database systems, so we are actually happy to be using this Cassandra database.

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Pawel Cislo

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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Quotes from Members

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

Pros

"The most valuable features are the counter features and the NoSQL schema, and it also has good scalability because you can scale Cassandra to any infinite level."

"The most valuable features of this solution are its speed and distributed nature."

"A consistent solution."

"Cassandra has some features that are more useful for specific use cases where you have time series where you have huge amounts of writes. That should be quick, but not specifically the reads. We needed to have quicker reads and writes and this is why we are using Cassandra right now."

"The solution's database capabilities are very good."

"Cassandra is a stable solution."

"The most valuable feature for us is the technical evaluation, it's the best technology."

"I am getting much better performance than relational databases."

More Cassandra pros

"Qdrant is an excellent vector database that anyone would want to use with RAG AI."

"Qdrant has positively impacted my organization by consuming much less time than building systems through coding."

"Qdrant has positively impacted my organization by significantly improving our RAG system's latency, cost-efficiency, and accuracy."

"Qdrant is a good and scalable vector database, and it is free."

"Qdrant is one of the best vector databases out there that is also open source."

"Qdrant has reduced our response time to less than one second for our 128 KB token sizes, and we are satisfied with that performance."

"We saw a clear return on investment from Qdrant, particularly in the engineering time saved and the empowerment of team members to handle self-service tasks instead of reducing headcount."

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

More Qdrant pros

Cons

"We experience configuration issues when accommodating the volumes we require, which often necessitates consultation with the Cassandra development team."

"The initial setup of Cassandra can be difficult in the configuration. There might be a need to have assistance. The implementation process can six months for connecting to certain databases."

"There could be more integration, and it could be more user-friendly."

"Cassandra can improve by adding more built-in tools. For example, if you want to do some maintenance activities in the cluster, we have to depend on third-party tools. Having these tools build-in would be e benefit."

"The solution is limited to a linear performance."

"Some issues arise from our vendors like Apache slowness and distribution or load balancing from HAProxy, which should better handle consumption for high-level concurrency."

"Cassandra could be more user-friendly like MongoDB."

"We have had stability issues including out of memory issues and crashes with earlier versions of the product."

More Cassandra cons

"There can always be improvements needed for Qdrant, but I do not want to add anything at this time."

"A lot of our work is agentic right now, and we have also segmented the content to be logical, so there's not a lot of vector search anymore."

"Qdrant is available through a containerized Docker, but a normal deployment in Qdrant is not there, and that can actually be worked out."

"Qdrant is pretty good overall, but it can be improved. For example, the UI could be better, and it has pretty much very generic features that every other vector database provides."

"A pain point I have encountered was the inactive expiration of the cloud created for certain projects; if the cloud is not used for a week, it gets terminated, which is frustrating."

"The area for improvement in Qdrant is its clustering capability. 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."

"The main limitations I notice come down to developer experience and native features rather than performance."

"The file system lock in Qdrant prevents the API and scripts from hitting it directly, and to surpass this limitation, I have to run Qdrant client as a service, which incurs additional costs for running it continuously, so if something about that could be done, it would be really amazing."

More Qdrant cons

Pricing and Cost Advice

"I use the tool's open-source version."

"There are licensing fees that must be paid, but I'm not sure if they are paid monthly or yearly."

"I don't have the specific numbers on pricing, but it was fairly priced."

"We pay for a license."

"We are using the open-source version of Cassandra, the solution is free."

"Cassandra is a free open source solution, but there is a commercial version available called DataStax Enterprise."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Comms Service Provider

10%

Construction 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	9
Midsize Enterprise	2
Large Enterprise	14

By reviewers
Company Size	Count
Small Business	10
Midsize Enterprise	2

Questions from the Community

What is your experience regarding pricing and costs for Cassandra?

The pricing for Cassandra is a little bit high, so it would be better for our community services if they consider community pricing for any non-profit organization like an NGO or other things. It w...

What needs improvement with Cassandra?

Regarding areas of improvement for Cassandra, currently, we are not facing significant issues. Some issues arise from our vendors like Apache slowness and distribution or load balancing from HAProx...

What is your primary use case for Cassandra?

My use case for Cassandra is for a document and other unstructured data management system as well as structured data for ultra-poor member community edition, community members' PII information, so ...

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 Enterprise Advanced vs Cassandra

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Product Reports

Download Cassandra product report

Cassandra

September 2026

Download Qdrant product report

September 2026

Overview

Cassandra is a distributed and scalable database management system used for real-time data processing.

It is highly valued for its ability to handle large amounts of data, scalability, high availability, fault tolerance, and flexible data model.

It is commonly used in finance, e-commerce, and social media industries.

Apache

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

1. Apple 2. Netflix 3. Facebook 4. Instagram 5. Twitter 6. eBay 7. Spotify 8. Uber 9. Airbnb 10. Adobe 11. Cisco 12. IBM 13. Microsoft 14. Yahoo 15. Reddit 16. Pinterest 17. Salesforce 18. LinkedIn 19. Hulu 20. Airbnb 21. Walmart 22. Target 23. Sony 24. Intel 25. Cisco 26. HP 27. Oracle 28. SAP 29. GE 30. Siemens 31. Volkswagen 32. Toyota

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