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Qdrant vs Vespa comparison

 

Comparison Buyer's Guide

Executive Summary

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

Qdrant
Ranking in Open Source Databases
9th
Ranking in Vector Databases
3rd
Average Rating
9.0
Reviews Sentiment
5.7
Number of Reviews
6
Ranking in other categories
AI Data Analysis (12th)
Vespa
Ranking in Open Source Databases
20th
Ranking in Vector Databases
20th
Average Rating
7.8
Reviews Sentiment
5.3
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2026, in the Open Source Databases category, the mindshare of Qdrant is 4.5%, up from 3.5% compared to the previous year. The mindshare of Vespa is 1.7%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
Qdrant4.5%
Vespa1.7%
Other93.8%
Open Source 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.
Ganaraj Amakrishna - PeerSpot reviewer
Lead Technical Architect at Zoro UK
Vector search has improved e‑commerce relevance but setup and learning curve still need work
Vespa definitely had its own set of challenges. It was really hard to get into initially, especially when I started implementing it in 2024 along with one junior employee, and the lack of documentation made it difficult. I aimed for an implementation with ColBERT, a sparse embedding mechanism, which I believed would fit well for e-commerce. We went through iterations during A/B testing because the initial set did not work as expected, which extended the process to about one and a half years. Vespa has a considerable learning curve, making it challenging for most people to get into, and it is also expensive, which can deter startups or those with smaller budgets from using it. Community support was decent, and we turned to it for clarifications. However, substantial improvements in documentation are necessary, especially more examples for handling DSL effectively. Having a runtime testing feature would greatly facilitate quick iterations.

Quotes from Members

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

Pros

"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."
"Qdrant has positively impacted my organization by consuming much less time than building systems through coding."
"Using Qdrant's hybrid search capability has improved my search results."
"Qdrant is an excellent vector database that anyone would want to use with RAG AI."
"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."
"Qdrant has reduced our response time to less than one second for our 128 KB token sizes, and we are satisfied with that performance."
"The most outstanding features and characteristics of Vespa include an architecture that lets you focus on implementing features, the function that automatically manages sharding and shards is excellent, and the flexibility of the server cluster and infrastructure architecture is outstanding."
"Vespa is very good and it improves our product, and we got more clients."
"The best feature to me is the LTR feature, the ranking feature to be specific."
"While conducting A/B testing, Vespa seemed to be performing slightly better than Elasticsearch, especially in search relevancy within live production systems, and its performance was decent."
 

Cons

"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 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."
"One of the key limitations is that Qdrant does not have built-in role-based access control, and while being self-hosted is a benefit, it can also be improved."
"Architectural complexity was a key friction point, as our primary database was set in Supabase, necessitating synchronization of two separate systems for user data, permissions, and states."
"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 integration is actually a pain."
"There were aspects of Vespa that needed improvement, such as if a monitoring dashboard were provided—and not only the monitoring dashboard, but also related supplementary tools for the administrative aspects—that would be better."
"We want Vespa to implement some UI features so that we can visualize how our data goes and what embeddings it stores."
"Vespa has a considerable learning curve, making it challenging for most people to get into, and it is also expensive, which can deter startups or those with smaller budgets from using it."
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Top Industries

By visitors reading reviews
Comms Service Provider
11%
Financial Services Firm
11%
Manufacturing Company
10%
Computer Software Company
9%
Computer Software Company
16%
Comms Service Provider
12%
Financial Services Firm
9%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business8
No data available
 

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 is your experience regarding pricing and costs for Vespa?
The setup cost is definitely huge, and pricing is also steep. In terms of licensing, it seems generous for those who do not want to engage with Vespa's hosted services.
What needs improvement with Vespa?
Vespa definitely had its own set of challenges. It was really hard to get into initially, especially when I started implementing it in 2024 along with one junior employee, and the lack of documenta...
What is your primary use case for Vespa?
My main use case for Vespa is implementing it as the back-end search engine for an e-commerce site, where we have about six million products, or six million SKUs, that we are selling. I implemented...
 

Comparisons

 

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. Yahoo 2. Verizon Media 3. Oath 4. Tumblr 5. AOL 6. Huffington Post 7. TechCrunch 8. Engadget 9. MapQuest 10. Moviefone 11. Autoblog 12. AOL Mail 13. Yahoo Mail 14. Yahoo Finance 15. Yahoo Sports 16. Yahoo News 17. Yahoo Search 18. Yahoo Answers 19. Yahoo Messenger 20. Yahoo Groups 21. Yahoo Weather 22. Yahoo Maps 23. Yahoo Fantasy Sports 24. Yahoo TV 25. Yahoo Movies 26. Yahoo Music 27. Yahoo Style 28. Yahoo Beauty 29. Yahoo Travel 30. Yahoo Autos 31. Yahoo Health 32. Yahoo Tech
Find out what your peers are saying about Qdrant vs. Vespa and other solutions. Updated: June 2026.
900,644 professionals have used our research since 2012.