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PostgreSQL vs Qdrant comparison

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Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.7
PostgreSQL offers cost savings and rapid ROI with free open-source capabilities, ideal for startups and growing enterprises.
Sentiment score
5.5
Qdrant boosts ROI by cutting costs, improving productivity, and enhancing efficiency through payload filtering and open-source flexibility.
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.
Automation Engineer at a educational organization with 11-50 employees
This lowers our LLM input token consumption by roughly 30 to 40 percent, translating directly into lower monthly OpenAI API bills.
MLOps Engineer at a tech services company with 501-1,000 employees
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Customer Service

Sentiment score
6.7
PostgreSQL support is strong with community forums, detailed documentation, and high-rated resources, offering both free and paid options.
Sentiment score
4.8
Qdrant customer service excels with developer-focused support via Discord and documentation, with reduced need for direct contact.
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.
Software Engineer at GSS Academy, Noida
Overall, we have a very small customer service team and a good engineering team with no overburden or bandwidth issues.
Data Science Architect at publicis Sapient
For customizations and extensions, the community is very active and useful.
Software Engineer – Rust Systems & AI Evaluation at Turing
It's open source, so we house it on our server.
Chief Ai Scientist at Predictive Systems
The documentation provided by Qdrant covers most queries effectively.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Qdrant's customer support is responsive and developer-focused.
MLOps Engineer at a tech services company with 501-1,000 employees
 

Scalability Issues

Sentiment score
7.4
PostgreSQL is highly scalable, handling large on-premise or cloud deployments effectively, supporting high transactions and growing user demands.
Sentiment score
5.7
Qdrant excels in scalability and performance, efficiently managing large datasets, particularly when deployed in Docker for enhanced growth.
Now, we are doing the same level of transactions in PostgreSQL, around 100,000 transactions, and we are getting good throughput with no latency.
Data Science Architect at publicis Sapient
In the recruiting agency project, the reliance on the vector database has expanded from storing hundreds of resumes to thousands.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
When Qdrant is deployed in Docker, it scales really fast, and you can assign multiple CPUs to enhance performance.
Analyst at Synergy Connect
Qdrant is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.
MLOps Engineer at a tech services company with 501-1,000 employees
 

Stability Issues

Sentiment score
8.0
PostgreSQL is praised for its stability and reliability, outperforming MySQL, with issues mainly from misconfiguration, not intrinsic faults.
Sentiment score
7.9
Qdrant, built in Rust, is praised for reliability, fast queries, and precision, despite minor cloud termination limitations.
I have never seen any performance issue in PostgreSQL.
Data Science Architect at publicis Sapient
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.
MLOps Engineer at a tech services company with 501-1,000 employees
You need to patch Qdrant as soon as patches are released.
Co Founder & CEO at SaYukth Private Limited
It is easy to use whether on LangChain or on its own.
Product Engineer at a tech vendor with 11-50 employees
 

Room For Improvement

PostgreSQL users seek improvements in interface, performance, scalability, integration, documentation, and support for large-scale real-time applications.
Qdrant needs developer experience enhancements, including multi-query fusion, embedding support, schema management, and improved deployment, UI, and documentation.
PostgreSQL remains a strong choice for enterprise applications due to its stability, extensibility, and SQL standards compliance.
Software Engineer – Rust Systems & AI Evaluation at Turing
Query optimization improves slow queries by using proper indexes, avoiding unnecessary joins, and using EXPLAIN ANALYZE to inspect query plans.
Software Engineer at GSS Academy, Noida
If I need to increase the dimension to 3,000 or 5,000, that option should be available.
Data Science Architect at publicis Sapient
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.
Product Engineer at a tech vendor with 11-50 employees
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.
Co Founder & CEO at SaYukth Private Limited
Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Setup Cost

PostgreSQL offers cost-effective scalability and flexibility with zero licensing fees, though setup and support may incur additional costs.
Qdrant provides cost-effective, predictable billing and zero upfront investment, with open-source access and easy setup options.
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.
Data Science Architect at publicis Sapient
The managed PostgreSQL itself is open source with no license fees.
Software Engineer – Rust Systems & AI Evaluation at Turing
The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost nothing.
MLOps Engineer at a tech services company with 501-1,000 employees
Using Qdrant is free.
Chief Ai Scientist at Predictive Systems
Regarding pricing, setup costs, and licensing, since I am using only the free tier of Qdrant Cloud, there are no setup costs involved.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Valuable Features

PostgreSQL excels in spatial support, high availability, JSONB handling, integration, scalability, and community-driven advanced features for diverse applications.
Qdrant provides fast, efficient vector search with hybrid indexing, Python support, and open-source configuration for scalable AI projects.
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.
Software Engineer at GSS Academy, Noida
The best feature is performance, because of which I decided on PostgreSQL.
Data Science Architect at publicis Sapient
Its robustness and reliability are incredible and stable, which is crucial for critical data, especially with AI model outputs.
Software Engineer – Rust Systems & AI Evaluation at Turing
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.
Chief Ai Scientist at Predictive Systems
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.
Analyst at Synergy Connect
Qdrant supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
CTO at Honeycomb AI
 

Categories and Ranking

PostgreSQL
Ranking in Open Source Databases
1st
Ranking in Vector Databases
7th
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
128
Ranking in other categories
No ranking in other categories
Qdrant
Ranking in Open Source Databases
5th
Ranking in Vector Databases
2nd
Average Rating
8.8
Reviews Sentiment
5.8
Number of Reviews
10
Ranking in other categories
AI Data Analysis (6th)
 

Mindshare comparison

As of September 2026, in the Open Source Databases category, the mindshare of PostgreSQL is 12.0%, down from 16.9% compared to the previous year. The mindshare of Qdrant is 4.3%, up from 4.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
PostgreSQL12.0%
Qdrant4.3%
Other83.7%
Open Source Databases
 

Featured Reviews

Shobhit Goel - PeerSpot reviewer
Data Science Architect at publicis Sapient
High-volume transactions have reduced failures and improve customer service efficiency
The best feature is performance, because of which I decided on PostgreSQL. I have also enabled the PG vector plugin on top of PostgreSQL. I have the opportunity to use two different features and two different flavors in a single product, which is the best thing about PostgreSQL. Initially, we had some hiccups around the performance part, but later we did indexing in PostgreSQL and now it is working very well. Even when we are doing 100,000 transactions in a day, PostgreSQL is working excellently. The interface is another best feature. If I need to do any query, I simply install the plugin on my local, which is pgAdmin. Through pgAdmin, I am able to communicate with PostgreSQL and execute all my SQL queries. I am getting a better UI with PostgreSQL as the backend, which is also one of the best options. PG vector is also very strong from PostgreSQL where I have implemented RAG and on a daily basis, I inject thousands of pages of PDF. More than 100 PDFs are coming into my system and one PDF is around 1,000 pages. We are injecting them into PostgreSQL and converting them into dimensions and inserting them into PG vector. The level of transactions we are doing on a daily basis is substantial, and we are getting very good throughput and low latency from PostgreSQL. When we were doing more than 50,000 transactions in a minute with the previous database, we were getting a lot of latency issues with threads getting blocked and abruptly closed unwantedly. After doing extensive research, we decided to move to PostgreSQL. Now, we are doing around 100,000 transactions in PostgreSQL and we are getting good throughput with no latency.
Pawel Cislo - PeerSpot reviewer
MLOps Engineer at a tech services company with 501-1,000 employees
Adaptive assistant has delivered faster grounded answers and has reduced token costs significantly
The main limitations I notice come down to developer experience and native features rather than performance. Building hybrid retrieval and fusion pipelines still requires considerable manual orchestration and code. Having more built-in multi-query fusion strategies natively inside Qdrant would be a significant time-saver. Additionally, managing dynamic metadata schemas and tracking index build progress during bulk ingest could be more transparent in the web UI. To make things easier for developers, Qdrant could provide more native tools for managing payload schema evolution over time. As metadata needs change in production RAG systems, updating existing payloads across large collections currently requires custom migration scripts. Built-in schema versioning and simpler automated index testing during CI/CD would make running Qdrant in rapidly evolving production environments even smoother. I rate Qdrant 9 out of 10 because its speed, sub-15 millisecond retrieval, and single-stage payload filtering make it top-tier for production RAG pipelines. I deduct one point mainly for developer experience. Setting up multi-query fusion still requires extra boilerplate code, payload metadata schema updates require custom migration scripts, and real-time visibility into HNSW graph indexing progress during bulk ingest could be improved in the web UI.
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Top Industries

By visitors reading reviews
Financial Services Firm
11%
Comms Service Provider
10%
Outsourcing Company
9%
Computer Software Company
8%
Comms Service Provider
13%
Manufacturing Company
11%
Financial Services Firm
10%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business58
Midsize Enterprise26
Large Enterprise49
By reviewers
Company SizeCount
Small Business10
Midsize Enterprise2
 

Questions from the Community

How does Firebird SQL compare with PostgreSQL?
PostgreSQL was designed in a way that provides you with not only a high degree of flexibility but also offers you a cheap and easy-to-use solution. It gives you the ability to redesign and audit yo...
What is your experience regarding pricing and costs for PostgreSQL?
I am not directly involved in the licensing or procurement decisions, so I cannot comment in detail on the price. From an engineering perspective, PostgreSQL is cost-efficient because it is open so...
What needs improvement with PostgreSQL?
While improving reliability, I have noticed that the limitations in PostgreSQL can be complex. For large-scale deployments, configuration, performance tuning, and related tasks can be complex and i...
What is your experience regarding pricing and costs for Qdrant?
I find Qdrant's pricing and licensing extremely straightforward and cost-effective. The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost n...
What needs improvement with Qdrant?
Qdrant is available through a containerized Docker, but a normal deployment in Qdrant is not there, and that can actually be worked out. That was one aspect I thought about, because I need to have ...
What is your primary use case for Qdrant?
We have a full-fledged RAG system using Qdrant Vector Database, and that is how it has benefited us. For example, we have implemented a techno-commercial evaluator using that, and it is in producti...
 

Comparisons

 

Overview

 

Sample Customers

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Find out what your peers are saying about PostgreSQL vs. Qdrant and other solutions. Updated: September 2026.
913,683 professionals have used our research since 2012.