

Find out in this report how the two Open Source Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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.
If PostgreSQL is hosted on cloud services such as Amazon RDS or Google Cloud SQL, the support is handled by the cloud provider, who provides automated backups, monitoring, infrastructure management, and technical support tickets.
Overall, we have a very small customer service team and a good engineering team with no overburden or bandwidth issues.
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.
Now, we are doing the same level of transactions in PostgreSQL, around 100,000 transactions, and we are getting good throughput with no latency.
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.
I have never seen any performance issue in PostgreSQL.
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.
Query optimization improves slow queries by using proper indexes, avoiding unnecessary joins, and using EXPLAIN ANALYZE to inspect query plans.
If I need to increase the dimension to 3,000 or 5,000, that option should be available.
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.
Even with doing 100,000 transactions right now within PostgreSQL, we are happy with PostgreSQL and not seeing that it is expensive or going out of budget.
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.
PostgreSQL improves reliability, performance, and scalability in production. Since it is ACID compliant, it ensures that database transactions are safe and consistent, preventing partial data updates, maintaining data integrity, and allowing multiple users to read or write data simultaneously using MVCC.
The best feature is performance, because of which I decided on PostgreSQL.
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.
| Product | Mindshare (%) |
|---|---|
| PostgreSQL | 13.1% |
| Qdrant | 4.5% |
| Other | 82.4% |


| Company Size | Count |
|---|---|
| Small Business | 57 |
| Midsize Enterprise | 27 |
| Large Enterprise | 48 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
PostgreSQL is a versatile and reliable database management system commonly used for web development, data analysis, and building scalable databases.
It offers advanced features like indexing, replication, and transaction management. Users appreciate its flexibility, performance, and ability to handle large amounts of data efficiently. Its robustness, scalability, and support for complex queries make it highly valuable.
Additionally, PostgreSQL's extensibility, flexibility, community support, and frequent updates contribute to its ongoing improvement and stability.
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.
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