

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.
It offers at least 25 percent cost savings compared to maintaining on-premises databases.
Now, we use embedded PostgreSQL vectors, which will undoubtedly reduce the TCO by using a much more cost-effective solution.
We've reduced our total ownership cost because we are not spending on expensive SQL server licenses.
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.
Once we open a support case, we have people engaged within about 20 minutes, especially for a Sev 1 issue.
On a scale from one to ten, I rate customer service and technical support a nine because they are quick in responding and in working with me to rectify any issues we come across.
The documentation and training we've received through Microsoft Learn on how to migrate, deploy, and manage the solution is exceptional.
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.
We do not have to buy everything and build it. It is already there.
However, we can see how well it scales after we deploy it for some large enterprise customers or big government organizations.
The scaling options with FlexServer provide us with the flexibility we need based on application complexity.
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.
It is the go-to database if seeking performance and capacity.
I have not experienced any downtime, crashes, or performance issues; it has been very stable.
There is a stability issue where, if the database usage peaks quickly, it may crash and require intervention to restore functionality.
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.
It does not presently support knowledge graph functionalities as Neo4j does.
Azure Database for PostgreSQL can be improved by allowing quicker scaling without blips.
I believe there could be improvements in the mirroring part and Change Data Capture (CDC).
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.
We've reduced costs by 60 percent compared to maintaining on-premises solutions.
We chose it because it is more cost-effective than Microsoft SQL.
Once you are comfortable and ready to make some commitments, you get about 30 percent saving if you are going with one-year or three-year reservation cost.
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.
My takeaway as a CTO is that they're comfortable with the security posture, the features, the observability, alerts, and now it integrates into the rest of the Azure landscape.
The query analyzers help me find out what's happening in each of the queries.
If a database has any task that has been interrupted or any connection issue, it will alert LogicMonitor, and we have a centralized panel for all the alerts.
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 (%) |
|---|---|
| Azure Database for PostgreSQL | 1.9% |
| Qdrant | 4.5% |
| Other | 93.6% |


| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
Azure Database for PostgreSQL offers efficient management, robust networking, and seamless Microsoft integration. Known for its strong performance and high satisfaction in enterprise settings, it provides operational efficiency, security, and monitoring.
With features that facilitate Azure integration, easy configuration, and AI integration, Azure Database for PostgreSQL serves as a valuable choice for businesses requiring operational efficiency and cost-effectiveness. Users benefit from powerful vector capabilities, seamless Microsoft service integration, straightforward management, and user authentication ensuring high satisfaction. Easy optimization, query analysis, and backup operations make it suitable for varied enterprise applications. However, improvements in flexible scaling, cost-effectiveness of monitoring tools, and enhanced integration with Azure OpenAI would enhance its capabilities further.
What Are the Key Features of Azure Database for PostgreSQL?Industries such as healthcare, retail, and finance leverage Azure Database for PostgreSQL for backend solutions, incident reporting, and public information sharing. Managed service providers utilize its strong performance for client needs, while administrators use it for applications like ControlM. Its flexibility supports containerized applications and disaster recovery, ensuring compatibility with diverse environments.
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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