

Qdrant and Supabase Vector compete in the vector database category. Qdrant seems to have the upper hand in high-dimensional vector handling, whereas Supabase Vector excels in unified SQL integration.
Features: Qdrant is renowned for its robust integration of high-dimensional vector storage, dense and sparse vector combination, and hybrid search capabilities that enhance AI-driven projects. In contrast, Supabase Vector integrates SQL with vector search, making it familiar for developers working with PostgreSQL and offering streamlined hybrid search functionality.
Room for Improvement: Qdrant could improve its GUI for managing large collections, global deployment support, and integration with frameworks like LangChain. Supabase Vector needs more intuitive hybrid search capabilities, better handling of large-scale deployments, and deeper AI embedding integration.
Ease of Deployment and Customer Service: Qdrant is ideal for on-premises deployment with strong community support, while Supabase Vector offers a cloud-based solution with enhanced user support, making it suitable for those using public and hybrid cloud platforms.
Pricing and ROI: Qdrant, being open source, offers substantial cost savings with no initial setup costs or licensing fees, leading to a significant ROI. Supabase Vector, though more cost-intensive, provides convenience and integrated solutions at a competitive price, making it suitable for those needing a comprehensive solution.
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.
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
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.
The dashboard's management made access straightforward for users and super easy to maintain, resulting in very few errors.
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%.
The use of these technologies definitely impacts reducing the time and cost of implementation or deployment.
It's open source, so we house it on our server.
The documentation provided by Qdrant covers most queries effectively.
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.
I would rate the customer support a nine since they replied quickly and answered my questions properly, which helped me a lot.
I have always been able to solve it out with the help of my community or sometimes YouTube.
Community support from helpful developers and engineers provides fast responses on GitHub issues and community forums.
In the recruiting agency project, the reliance on the vector database has expanded from storing hundreds of resumes to thousands.
When Qdrant is deployed in Docker, it scales really fast, and you can assign multiple CPUs to enhance performance.
Qdrant handles growing workloads and data volumes well for me, which was a significant reason for my shift from other popular alternatives to Qdrant.
As we move toward larger scales, such as multi-million vectors, it requires careful engineering to maintain predictable performance.
I have basically used it for small teams, not large teams that need to cover thousands of users.
Supabase Vector is highly scalable for small to medium to large scale applications.
You need to patch Qdrant as soon as patches are released.
It is easy to use whether on LangChain or on its own.
Qdrant is stable, except for the limitation concerning the termination of inactive clouds after a week.
From my experience, Supabase Vector is stable.
Achieving the best performance at higher scales depends largely on optimization of queries and indexes.
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.
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.
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.
Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.
Better query debugging tools and built-in evaluation toolkits for vector search would be incredibly helpful for developers.
If they could make the debugging process clearer to prevent the error messages, that will make development faster for web-coded apps.
For index optimization guidance, clearer instructions on when to use IVFFlat versus HNSW indexes would be helpful.
Using Qdrant is free.
Regarding pricing, setup costs, and licensing, since I am using only the free tier of Qdrant Cloud, there are no setup costs involved.
Licensing posed no issues, as Qdrant is open-source software with no upfront fees.
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.
For now, I think the pricing is perfect because every business person can afford it and a developer can afford that price.
I utilize the free tier, which includes a 500 MB database with vector support at no cost, allowing support for millions of embeddings.
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.
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 supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
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.
Supabase Vector is a managed service, so I do not need to worry about scaling the database and managing the infrastructure.
Supabase Vector has positively impacted my organization by significantly reducing our testing time.
| Product | Mindshare (%) |
|---|---|
| Supabase Vector | 5.7% |
| Qdrant | 6.6% |
| Other | 87.7% |


| Company Size | Count |
|---|---|
| Small Business | 10 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 1 |
| Large Enterprise | 7 |
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.
Supabase Vector offers an efficient way to manage and query vector embeddings, catering to the needs of developers and data scientists seeking scalable solutions for vector-based data handling.
Supabase Vector is designed to streamline the process of storing, managing, and querying vector embeddings, essential for applications like machine learning algorithms and personalized recommendations. Its intuitive API and integration capabilities make it a preferred choice for tech professionals seeking a reliable backend for their vector data requirements. With flexible storage options and robust querying features, it accommodates the dynamic demands of AI-driven projects.
What are its key features?
What benefits or ROI should users look for?
Supabase Vector can be particularly beneficial in industries such as e-commerce for personalized product recommendations, in finance for fraud detection through pattern analysis, and in healthcare for patient data insights. Its capability to handle diverse sets of embeddings makes it versatile across different sectors needing robust data processing tools.
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