No more typing reviews! Try our Samantha, our new voice AI agent.

Qdrant vs Redis comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 15, 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
5.2
Qdrant's integration streamlined support ticket resolution, enhancing efficiency and cost-effectiveness through improved retrieval and self-service capabilities.
Sentiment score
7.2
Redis enhances ROI by improving performance, reducing costs, increasing productivity, and ensuring reliable, scalable, and efficient service.
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
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
I have seen a significant return on investment from using Qdrant because it is very easy to integrate and highly efficient, saving a lot of time in my day-to-day operations, which ultimately saves money as well.
Full Stack Product Engineer at a tech vendor with 11-50 employees
It improved API latency from two seconds to 450 milliseconds for P99.
Senior Software Developer at NIT
We reduced the database read load by around 30 to 40 percent and improved API response time by 20 to 30 percent, specifically for frequently accessed endpoints.
SDE 2 at Virtusa
 

Customer Service

Sentiment score
5.4
Qdrant's community-driven approach provides ample online resources and documentation, minimizing direct customer support needs and enhancing satisfaction.
Sentiment score
5.8
Redis is stable and reliable, with helpful support, strong documentation, and often minimal need for direct assistance.
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
I rate the technical support of Qdrant as a nine because I think we have never reached out to them directly, but Qdrant has good support available online, and I can get answers from forums.
Co Founder & CEO at SaYukth Private Limited
The documentation and community support for Redis are very strong, making troubleshooting quicker.
Senior Software Developer at NIT
Since Redis is quite stable and well-documented, we have not needed much support, but when required, the response has been helpful.
SDE 2 at Virtusa
 

Scalability Issues

Sentiment score
5.5
Qdrant's scalability in Docker enables efficient expansion and performance with multiple CPUs, attracting migrations from alternative solutions.
Sentiment score
7.8
Redis excels in horizontal and vertical scaling, offering clustering, sharding, and compatibility with Azure and AWS for enterprise adaptability.
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 handles growing workloads and data volumes well for me, which was a significant reason for my shift from other popular alternatives to Qdrant.
Full Stack Product Engineer at a tech vendor with 11-50 employees
Data migration and changes to application-side configurations are challenging due to the lack of automatic migration tools in a non-clustered legacy system.
Data Engineer at a photography company with 1,001-5,000 employees
I scale Redis horizontally using clustering and sharding, where data is distributed across multiple nodes to handle higher traffic and larger data sets.
Senior Software Developer at NIT
With features such as clustering and replication, it can handle high traffic and a large database very effectively.
SDE 2 at Virtusa
 

Stability Issues

Sentiment score
7.7
Qdrant is stable, reliable, easy to use, but inactive clouds terminate after a week, affecting continuous hosting.
Sentiment score
7.8
Redis is stable, handles heavy loads, offers high availability, and uses persistence mechanisms, making it a trusted choice.
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.
Full Stack Product Engineer at a tech vendor with 11-50 employees
Qdrant is stable, except for the limitation concerning the termination of inactive clouds after a week.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Redis is fairly stable.
Data Engineer at a photography company with 1,001-5,000 employees
 

Room For Improvement

Qdrant requires UI enhancements, improved backup management, and integrated automation to address operational complexities and improve features.
Redis users face challenges with scalability, GUI, documentation, security, and seek enhancements in monitoring, analytics, and multi-tenancy features.
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.
Full Stack 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
Data persistence and recovery face issues with compatibility across major versions, making upgrades possible but downgrades not active.
Data Engineer at a photography company with 1,001-5,000 employees
Redis itself does not enforce consistency with the primary database, so developers need to carefully design cache invalidation strategies.
Software Engineer at ValueMomentum
One issue is cache invalidation. Keeping cache data consistent with the source of truth can be tricky, especially in distributed systems.
Senior Software Developer at NIT
 

Setup Cost

Qdrant offers cost-effective enterprise pricing but scaling may require migrating to paid plans for advanced features and support.
Redis pricing depends on memory, cluster size, and infrastructure, with higher costs than SQL due to RAM usage.
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
Licensing posed no issues, as Qdrant is open-source software with no upfront fees.
Automation Engineer at a educational organization with 11-50 employees
Since we use an open-source version of Redis, we do not experience any setup costs or licensing expenses.
Data Engineer at a photography company with 1,001-5,000 employees
The costs are primarily driven by memory consumption and cluster size, since Redis operates in-memory.
Senior Software Developer at NIT
The pricing is reasonable for the performance provided.
SDE 2 at Virtusa
 

Valuable Features

Qdrant enhances search precision using hybrid vectors, offers cost-effective deployment, and supports efficient AI handling with flexible APIs.
Redis offers low latency, high throughput, and scalability with rich data structures, ideal for real-time applications and caching.
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
The best features of Qdrant are GPU support, which enables very fast processing, and a very light footprint as it uses fewer resources.
Co Founder & CEO at SaYukth Private Limited
It functions similarly to a foundational building block in a larger system, enabling native integration and high functionality in core data processes.
Data Engineer at a photography company with 1,001-5,000 employees
First is its in-memory preference, as Redis is extremely fast, making it ideal for caching and session management where low latency is critical.
Software Engineer at ValueMomentum
Real API latency improved from around two seconds to approximately 450 milliseconds for P99.
Senior Software Developer at NIT
 

Categories and Ranking

Qdrant
Ranking in Vector Databases
3rd
Average Rating
9.0
Reviews Sentiment
5.7
Number of Reviews
6
Ranking in other categories
Open Source Databases (9th), AI Data Analysis (12th)
Redis
Ranking in Vector Databases
2nd
Average Rating
8.8
Reviews Sentiment
5.9
Number of Reviews
26
Ranking in other categories
NoSQL Databases (3rd), Managed NoSQL Databases (6th), In-Memory Data Store Services (1st), AI Software Development (12th)
 

Mindshare comparison

As of June 2026, in the Vector Databases category, the mindshare of Qdrant is 6.7%, down from 8.1% compared to the previous year. The mindshare of Redis is 6.5%, up from 4.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Redis6.5%
Qdrant6.7%
Other86.8%
Vector Databases
 

Featured Reviews

Chirag Morajkar - PeerSpot reviewer
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Building accurate no-code resume screeners has saved weeks in document search workflows
I see room for improvement in Qdrant based on what another platform called Weaviate offers. Qdrant provides an excellent vector database with a solid searching method. However, it could elevate its offering by integrating embedding features. Currently, for the workflow automation I build, I rely on other platforms for embedding, so incorporating this feature directly in Qdrant Cloud would eliminate the need to depend on external solutions. 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. I think increasing that inactivity window in the free tier would be beneficial, as I have faced limitations due to this seven-day inactivity rule, requiring me to reset up the cloud after its termination.
Varuns Ug - PeerSpot reviewer
Senior Software Developer at NIT
Caching has accelerated complex workflows and delivers low latency for high-traffic microservices
A few features of Redis that I use on a day-to-day basis and feel are among the best are extremely low latency and high throughput. Since Redis is in-memory, it makes it ideal for cases such as caching and rate limiting where response time is critical. TTL expiry support is very useful in Redis as it allows me to automatically evict stale data without manual cleanup, which is something I use heavily in my caching strategy. Another point I can mention is that the rich data structures such as strings, hashes, and even sorted sets are very powerful. I have used strings for caching responses and counters, whereas I have used hashes for storing structured objects. One more feature I can tell you about is atomic operations. Redis guarantees atomicity for operations such as incrementing a counter, which is very useful for rate limiting and avoiding race conditions in distributed systems. Finally, I want to emphasize that Redis is easy to scale and integrate, whether through clustering or using a distributed cache across microservices. Redis has impacted my organization positively by providing default support that is very useful. For metrics, in one of my core systems, introducing Redis as a distributed cache helped me achieve around an 80% cache hit rate, which reduced repeated downstream services. Real API latency also improved from around two seconds to approximately 450 milliseconds for P99. It also helped reduce the load on dependent services and databases, which improved overall system reliability.
report
Use our free recommendation engine to learn which Vector Databases solutions are best for your needs.
900,644 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Comms Service Provider
11%
Financial Services Firm
11%
Manufacturing Company
10%
Computer Software Company
9%
Financial Services Firm
24%
Computer Software Company
10%
University
6%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business8
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise6
Large Enterprise10
 

Questions from the Community

What is your experience regarding pricing and costs for Qdrant?
Licensing posed no issues, as Qdrant is open-source software with no upfront fees. Initially, the setup cost was low since we utilized a self-hosted model on a small cloud VM. However, as we added ...
What needs improvement with Qdrant?
While Qdrant is an exceptionally fast and efficient search engine within vector bases, our engineering team faced operational challenges during its adoption. Architectural complexity was a key fric...
What is your primary use case for Qdrant?
I have been using Qdrant for almost one and a half years. This was actually one of the first vector databases that we picked up in our organization. We started using the RAG modules to create a per...
What needs improvement with Redis?
Overall, Redis is a powerful and reliable tool, but there are a few areas for improvement. One limitation is that Redis is memory-based, so scaling can become expensive compared to disk-based syste...
What is your primary use case for Redis?
My main use case for Redis is caching frequently accessed data to improve performance and reduce database load. For example, I cache API responses and user-related data so that repeated requests ca...
What advice do you have for others considering Redis?
My main advice for those looking into using Redis is to focus on the use case; Redis excels where low latency is critical, such as caching, session management, or real-time features, rather than us...
 

Comparisons

 

Also Known As

No data available
Redis Enterprise
 

Overview

 

Sample Customers

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
1. Twitter 2. GitHub 3. StackOverflow 4. Pinterest 5. Snapchat 6. Craigslist 7. Digg 8. Weibo 9. Airbnb 10. Uber 11. Slack 12. Trello 13. Shopify 14. Coursera 15. Medium 16. Twitch 17. Foursquare 18. Meetup 19. Kickstarter 20. Docker 21. Heroku 22. Bitbucket 23. Groupon 24. Flipboard 25. SoundCloud 26. BuzzFeed 27. Disqus 28. The New York Times 29. Walmart 30. Nike 31. Sony 32. Philips
Find out what your peers are saying about Qdrant vs. Redis and other solutions. Updated: April 2026.
900,644 professionals have used our research since 2012.