

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.
This lowers our LLM input token consumption by roughly 30 to 40 percent, translating directly into lower monthly OpenAI API bills.
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
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.
For customizations and extensions, the community is very active and useful.
It's open source, so we house it on our server.
The documentation provided by Qdrant covers most queries effectively.
Qdrant's customer support is responsive and developer-focused.
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 is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.
I have never seen any performance issue in PostgreSQL.
Built in Rust, it delivers sub-15 millisecond response times and rock-solid update and write-ahead logging to guarantee that newly indexed data is immediately searchable without dropping queries or producing inconsistent context for LLMs.
You need to patch Qdrant as soon as patches are released.
It is easy to use whether on LangChain or on its own.
PostgreSQL remains a strong choice for enterprise applications due to its stability, extensibility, and SQL standards compliance.
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.
The managed PostgreSQL itself is open source with no license fees.
The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost nothing.
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.
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.
Its robustness and reliability are incredible and stable, which is crucial for critical data, especially with AI model outputs.
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.
| Product | Mindshare (%) |
|---|---|
| PostgreSQL | 12.0% |
| Qdrant | 4.3% |
| Other | 83.7% |


| Company Size | Count |
|---|---|
| Small Business | 58 |
| Midsize Enterprise | 26 |
| Large Enterprise | 49 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 2 |
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.
We monitor all Open Source Databases reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.