PostgreSQL vs Supabase Vector comparison

PostgreSQL and Supabase are both solutions in the Vector Databases category. PostgreSQL is ranked #6 with an average rating of 8.0, while Supabase is ranked #9 with an average rating of 8.8. PostgreSQL holds a 7.9% mindshare in VD, compared to Supabase’s 7.4% mindshare. Additionally, 96% of PostgreSQL users are willing to recommend the solution, compared to 100% of Supabase users who would recommend it.

PostgreSQL

Read 127 PostgreSQL reviews

10,667 Views
3,840 Comparison Views

96% willing to recommend

Supabase Vector

Read 5 Supabase Vector reviews

2,873 Views
2,873 Comparison Views

100% willing to recommend

PostgreSQL

Supabase Vector

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Executive SummaryUpdated on Mar 5, 2025

PostgreSQL and Supabase Vector compete in the relational database and vector search categories. Supabase Vector seems to have the upper hand in machine learning applications due to its advanced vector search capabilities.

Features: PostgreSQL offers robust data integrity, an extensive range of data types, and comprehensive documentation. Supabase Vector is known for its rapid deployment of vector similarity searches, seamless integration with other Supabase services, and specialized vector functionalities for machine learning.

Ease of Deployment and Customer Service: PostgreSQL provides extensive community support and a rich ecosystem for complex deployments. Supabase Vector simplifies deployment with quick-start features and automated scaling, providing responsive customer support, contrasting PostgreSQL's community-driven solutions.

Pricing and ROI: PostgreSQL offers a cost-effective, scalable pricing model with high ROI through reduced licensing fees. Supabase Vector provides a predictable, usage-based pricing model catering to budget-conscious projects, aligning well with AI applications' emerging needs, ensuring affordability while maintaining specialization.

To learn more, read our detailed PostgreSQL vs. Supabase Vector Report (Updated: April 2026).

Buyer's Guide

PostgreSQL vs. Supabase Vector

April 2026

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Review summaries and opinions

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

Categories and Ranking

PostgreSQL

Ranking in Vector Databases

6th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

127

Ranking in other categories

Open Source Databases (1st)

Supabase Vector

Ranking in Vector Databases

9th

Average Rating

8.4

Reviews Sentiment

5.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2026, in the Vector Databases category, the mindshare of PostgreSQL is 7.9%, up from 4.9% compared to the previous year. The mindshare of Supabase Vector is 7.4%, up from 5.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Mindshare Distribution
Product	Mindshare (%)
PostgreSQL	7.9%
Supabase Vector	7.4%
Other	84.7%

Vector Databases

Featured Reviews

Shobhit Goel

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.

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AmritDash

Automation Engineer at a educational organization with 11-50 employees

Unified course data has streamlined our AI study assistant and still needs better large-scale search

There can be compute spikes when we scale up, which we noticed during our intake season while processing millions of records. Massive similarity searches on a lot of vectors can spike the database CPU and potentially slow down API requests, so we had to move to a higher plan in Supabase for handling this during our intake season. There is no native hybrid search yet, which can combine keyword search and vector search. Supabase supports both, but combining them requires writing a custom Postgres function, while dedicated tools on other platforms allow you to do that out of the box. On some level, we face indexing complexity with Supabase Vector because although vectors expedite searches, we need to use indexes such as HNSW or IVF Flat. Tuning these indexes in Postgres requires advanced knowledge, and we needed a dedicated Supabase expert or to hire someone capable of understanding these complex queries and set this up for us, making it not a plug-and-play solution for a massive scale project with tens of millions of vectors. Vectors are stored in Postgres, and we can perform a lot of similarity searches on millions of vectors, which can spike database CPU and potentially slow down the app, but apart from that, everything seems positive.

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Quotes from Members

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

Pros

"It is easy to use."

"The solution is quite a good database for light applications for sure."

"We use this solution as part of our backend to store data that is coming from the sensors, and it is needed to save the meter data from the sensors."

"I would say that the combination of PostgreSQL's features, plus the reliability and performance it offers in combination with the fact that you don't pay for a license was the reason I chose it."

"PostgreSQL is very powerful, easy to manage, and has many features."

"It's been really easy to learn and it's been useful since ramping up it use."

"We don't regret any choosing this solution."

"The solution has many valuable features such as it is easy to use and the interface is intuitive."

Cons

"It is rather complex to set up correctly. The configuration file is over 400 lines in length and many parameters have only vaguely defined suggestions."

"There are some products out there that have a slightly different method of implementation for the SQL language. Some of those are slightly better in some areas, and PostgreSQL is slightly better in some areas. I would probably like to match all of those products together. It is just down to the functionality. For example, Oracle has a number of options within SQL that are outside of what you would class as the SQL standard. PostgreSQL misses some of those, but PostgreSQL does other things that are better than what Oracle does. I would like to merge those two products so that there is a certain amount of functionality in a single product."

"Postgres should focus on building a stronger knowledge base. They also need to expand their integration capabilities, so more tools and resources are available to manage it."

"PostgreSQL could improve by being more user-friendly. In SQL Server they have a studio where you can easily do management but not in this solution."

"The user interface for the clients could be easier to use as they are small businesses. From a technical support perspective, the documentation could be improved."

"We often find the solution's datetime datatype challenging."

"Parallelization and some connection to analytics is needed."

"The performance of PostgreSQL could improve."

Pricing and Cost Advice

"Affordable solution."

"It is free, but if you need support, you can go for the commercial version called EnterpriseDB. They provide paid support, and they can even do hosting for you if you want standby and support."

"We do not pay for licensing."

"The tool is cheaply priced compared to other RDBMS providers in the market."

"Our company pays for it. There are free versions available, but for advanced features, you obviously have to pay."

"The licensing model is good."

"It is an open-source platform."

"We are using the free version of PostgreSQL."

Top Industries

By visitors reading reviews

Financial Services Firm

11%

Computer Software Company

11%

Comms Service Provider

10%

Manufacturing Company

Comms Service Provider

14%

Manufacturing Company

Outsourcing Company

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	57
Midsize Enterprise	27
Large Enterprise	47

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	1
Large Enterprise	2

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...

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What do you like most about PostgreSQL?

It's a transactional database, so we use Postgres for most of our reporting. That's where it's helping.

See all answers

What is your experience regarding pricing and costs for PostgreSQL?

I purchased from the marketplace, so licensing and pricing cost is acceptable. To be honest, there is a separate team who handles the cost of licensing everything. I have admin access on Azure and ...

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What is your experience regarding pricing and costs for Supabase Vector?

The price is good.

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What needs improvement with Supabase Vector?

I think the support system can be better because after Supabase Vector stopped working in India, there is no support. Nobody knows how to deal with the database now. The naming structure is a littl...

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What is your primary use case for Supabase Vector?

I'm using Supabase Vector for the Postgres part. I use their Postgres database as the main requirement for the product from my side. If I am building a small website or any product, I don't need to...

See all answers

Comparisons

Firebird SQL vs PostgreSQL

Compared 16% of the time

MySQL vs PostgreSQL

Compared 8% of the time

MariaDB vs PostgreSQL

Compared 6% of the time

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Compared 5% of the time

Chroma vs PostgreSQL

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Product Reports

Buyer's Guide

PostgreSQL

April 2026

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Buyer's Guide

Vector Databases

April 2026

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Overview

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.

PostgreSQL

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?

Seamless Integration: Connects effortlessly with databases, making it simple to incorporate into existing projects.
Scalable Storage: Allows for efficient storage of large volumes of vector data, accommodating growth in usage.
Flexible Queries: Supports complex queries, facilitating advanced data analysis and insights.
Real-Time Updates: Ensures that data remains current with instantaneous updates.
Developer-Friendly: Comes with comprehensive documentation and an intuitive API for ease of use.

What benefits or ROI should users look for?

Increased Efficiency: Streamlines data management, reducing time spent on backend development.
Cost Effectiveness: Offers scalable options that can save on infrastructure and maintenance costs.
Enhanced Accuracy: Delivers precise query results, improving decision-making processes.
Improved Developer Productivity: Simplifies complex tasks, allowing developers to focus on more critical activities.

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.

Supabase

Sample Customers

1. Apple 2. Cisco 3. Fujitsu 4. Instagram 5. Netflix 6. Red Hat 7. Sony 8. Uber 9. Cisco Systems 10. Skype 11. LinkedIn 12. Etsy 13. Yelp 14. Reddit 15. Dropbox 16. Slack 17. Twitch 18. WhatsApp 19. Snapchat 20. Shazam 21. SoundCloud 22. The New York Times 23. Cisco WebEx 24. Atlassian 25. Cisco Meraki 26. Heroku 27. GitLab 28. Zalando 29. OpenTable 30. Trello 31. Square Enix 32. Bloomberg

Information Not Available

Buyer's Guide

PostgreSQL vs. Supabase Vector

April 2026

Free Report: PostgreSQL vs. Supabase Vector

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