

Qdrant and Supabase Vector compete in the vector databases category. Qdrant has the upper hand due to its better feature set, making it particularly appealing for advanced analytics.
Features: Qdrant offers robust high-dimensional vector search, open-source customization, and advanced machine learning application support. Supabase Vector is known for its integration capabilities, seamless data handling, and compatibility with existing systems.
Ease of Deployment and Customer Service: Supabase Vector provides user-friendly deployment and comprehensive support, ideal for teams with limited technical expertise. Qdrant may need technical expertise for deployment but benefits from strong community support and documentation.
Pricing and ROI: Qdrant's pricing is considered justified due to its customization and functionality, offering high ROI for complex use cases. Supabase Vector's competitive pricing and quick setup provide fast time-to-value, beneficial for rapid deployment and cost efficiency.
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.
The use of these technologies definitely impacts reducing the time and cost of implementation or deployment.
I have seen a return on investment, as it obviously saves us a few hundred dollars every month compared with the approach of deploying the vector database on other providers.
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 recommend Supabase Vector to other users.
Customer support is handled using emails at the moment.
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.
The scalability of Supabase Vector is impressive; it is pretty scalable and stable at the same time.
Supabase Vector's scalability works fine so far in our scale of 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.
I would revise that to a five because there is currently downtime going on in India.
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.
When I'm in Supabase Vector, there is a feature where I have to create a table. At the start, for newcomers, it's difficult, and then it becomes hard.
I wish that there was a convenient way to make it compatible with the general Postgres database SDK.
An improvement for Supabase Vector would be to have it enabled by default.
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.
The price is good.
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.
The best features of Qdrant are GPU support, which enables very fast processing, and a very light footprint as it uses fewer resources.
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 (%) |
|---|---|
| Qdrant | 6.7% |
| Supabase Vector | 6.3% |
| Other | 87.0% |


| Company Size | Count |
|---|---|
| Small Business | 8 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 1 |
| Large Enterprise | 3 |
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?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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