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Qdrant vs Supabase comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jul 22, 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
5.1
Qdrant in RAG setups accelerates ticket resolution, cuts costs, boosts efficiency, and allows rapid and cost-effective launches.
Sentiment score
5.3
Supabase Vector boosts efficiency and profitability with faster development, better user engagement, reduced costs, and higher conversion rates.
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.
Product Engineer at a tech vendor with 11-50 employees
The dashboard's management made access straightforward for users and super easy to maintain, resulting in very few errors.
Co-Founder & CTO at Mango Giraffe
We notice significant improvements when measuring metrics such as average response times, which have shifted from 800 milliseconds to 2.5 seconds down to around 200 milliseconds to 800 milliseconds, with click-through rates for recommendations improving by 45 to 70%.
Product Engineer at a tech vendor with 11-50 employees
The use of these technologies definitely impacts reducing the time and cost of implementation or deployment.
Co-Founder at a tech services company with 1-10 employees
 

Customer Service

Sentiment score
4.3
Qdrant's community-driven technical support offers extensive documentation and forums, minimizing direct customer service needs and ensuring reliable functionality.
Sentiment score
5.4
Supabase users find customer service satisfactory with efficient support and resources, enhancing their platform experience with minimal 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
I would rate the customer support a nine since they replied quickly and answered my questions properly, which helped me a lot.
Co-Founder & CTO at Mango Giraffe
I have always been able to solve it out with the help of my community or sometimes YouTube.
Automation Specialist at a consultancy with 11-50 employees
Community support from helpful developers and engineers provides fast responses on GitHub issues and community forums.
AI Solutions Lead at ADP
 

Scalability Issues

Sentiment score
5.3
Qdrant efficiently scales with data growth, excelling in performance through Docker, favored for handling expanding workloads effectively.
Sentiment score
5.6
Supabase excels in small to medium applications but requires optimization for larger scales; free tier benefits startups.
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.
Product Engineer at a tech vendor with 11-50 employees
As we move toward larger scales, such as multi-million vectors, it requires careful engineering to maintain predictable performance.
Product Engineer at a tech vendor with 11-50 employees
I have basically used it for small teams, not large teams that need to cover thousands of users.
Automation Specialist at a consultancy with 11-50 employees
Supabase Vector is highly scalable for small to medium to large scale applications.
AI Solutions Lead at ADP
 

Stability Issues

Sentiment score
7.8
Qdrant is stable, user-friendly, integrates well, and requires timely maintenance, with minimal concerns about inactive cloud termination.
Sentiment score
7.6
Supabase Vector is stable and reliable, with occasional downtime in India, mainly due to coding errors, not the platform.
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.
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
From my experience, Supabase Vector is stable.
Co-Founder at a tech services company with 1-10 employees
Achieving the best performance at higher scales depends largely on optimization of queries and indexes.
Product Engineer at a tech vendor with 11-50 employees
I basically use it for my web-coded apps and for the RAG agent and it does all of the needs that I want it to do for my project and for my client's project.
Automation Specialist at a consultancy with 11-50 employees
 

Room For Improvement

Qdrant needs improvements in clustering, global deployment, usability features, and integration for better operational efficiency and user experience.
Supabase Vector needs improved documentation and support, better performance, scalability, indexing, and enhanced tool integration and language support.
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.
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
Better query debugging tools and built-in evaluation toolkits for vector search would be incredibly helpful for developers.
Product Engineer at a tech vendor with 11-50 employees
If they could make the debugging process clearer to prevent the error messages, that will make development faster for web-coded apps.
Automation Specialist at a consultancy with 11-50 employees
For index optimization guidance, clearer instructions on when to use IVFFlat versus HNSW indexes would be helpful.
AI Solutions Lead at ADP
 

Setup Cost

Enterprise users find Qdrant cost-effective initially but face indirect developer costs, with long-term savings from Supabase integration.
Supabase provides flexible pricing with a free tier and paid plans, ensuring scalability and cost efficiency for various projects.
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
It was amazing to be able to create all this technology for free, without the need to pay additional costs to use those technologies, apart from the embeddings ones from Google.
Co-Founder at a tech services company with 1-10 employees
For now, I think the pricing is perfect because every business person can afford it and a developer can afford that price.
Automation Specialist at a consultancy with 11-50 employees
I utilize the free tier, which includes a 500 MB database with vector support at no cost, allowing support for millions of embeddings.
AI Solutions Lead at ADP
 

Valuable Features

Qdrant excels in hybrid search, efficient vector processing, and cost-effective deployment with robust features for enhanced search performance.
Supabase Vector offers easy setup, cost-efficiency, PostgreSQL, SQL with vector search, improving database operations through streamlined, secure features.
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
Qdrant supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
Co-Founder and CTO at HoneyComb AI
We have Supabase basically as the host of most of our business relational database and user data, so since the client's applications are migrating to language model-empowered features, it is very useful, and we do not need to register for other database types.
Director at a tech services company with 1-10 employees
Supabase Vector is a managed service, so I do not need to worry about scaling the database and managing the infrastructure.
Senior Full Stack Engineer at a tech vendor with 11-50 employees
Supabase Vector has positively impacted my organization by significantly reducing our testing time.
Co-Founder & CTO at Mango Giraffe
 

Categories and Ranking

Qdrant
Ranking in Vector Databases
4th
Average Rating
8.8
Reviews Sentiment
5.4
Number of Reviews
8
Ranking in other categories
Open Source Databases (9th), AI Data Analysis (11th)
Supabase
Ranking in Vector Databases
2nd
Average Rating
8.6
Reviews Sentiment
5.6
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2026, in the Vector Databases category, the mindshare of Qdrant is 6.6%, down from 8.3% compared to the previous year. The mindshare of Supabase is 5.7%, down from 8.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Supabase Vector5.7%
Qdrant6.6%
Other87.7%
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.
Boya Uday Kumar - PeerSpot reviewer
AI Solutions Lead at ADP
Semantic search has transformed client sites and drives faster projects with higher conversions
Adapting to Supabase Vector was relatively smooth, but there was definitely a moderate learning curve at the start. The SQL foundation, REST API, documentation, and integration with all the AI tools made it easier. However, understanding embeddings, index types, similarity metrics, and how SQL and vector hybrid queries work, as well as the RLS policies for vectors, required some time to learn. I do not have many things to point out, but a couple of areas for improvement come to mind. For index optimization guidance, clearer instructions on when to use IVFFlat versus HNSW indexes would be helpful. Additionally, having a built-in embedding generation capability would simplify the workflow, as currently, I use external services such as OpenAI or Hugging Face for that purpose.
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Top Industries

By visitors reading reviews
Comms Service Provider
11%
Financial Services Firm
11%
Manufacturing Company
10%
Computer Software Company
10%
Comms Service Provider
13%
Manufacturing Company
10%
Educational Organization
7%
Outsourcing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business10
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise1
Large Enterprise7
 

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?
The UI can be a bit better, but that is just my own personal opinion. Qdrant is pretty good overall, but it can be improved. For example, the UI could be better, and it has pretty much very generic...
What is your primary use case for Qdrant?
I have been designing vector databases, so I use Qdrant mostly to explore how the engineers at Qdrant have built the whole vector database, specifically the multi-vector system. In terms of usage, ...
What is your experience regarding pricing and costs for Supabase Vector?
In this basic implementation or proof of concept project, I use the basic Supabase project available in the free trial. I am not sure which one of those options it falls under, but I use the free S...
What needs improvement with Supabase Vector?
When setting up a database, a PostgreSQL instance, which is the most popular use of Supabase, instead of having to go and write and run an SQL line to create a pgvector on Supabase, it would be nic...
What is your primary use case for Supabase Vector?
As an AI Engineer, my primary use case of Supabase Vector is for storing vector databases that I use at retrieval and inference in my AI agent and RAG pipelines. I have been working with RAG soluti...
 

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
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Find out what your peers are saying about Qdrant vs. Supabase and other solutions. Updated: June 2026.
905,601 professionals have used our research since 2012.