AI/ML Engineer at a tech services company with 11-50 employees
Real User
Top 10
Jul 7, 2026
As an AI Engineer, my primary use case of Supabase Vector is for storing vector databases that I use at retrieval and inference in my AI agent and RAG pipelines. I have been working with RAG solutions, and often times I have to chunk and embed information into vectors that need to be stored in databases so that at inference we can always retrieve the relevant chunks from the database. I was building on Pinecone, but then I had to test something with pgvector locally. After testing pgvector locally for a solution I built for Algorithmi UK, which is essentially a teacher co-pilot solution, I realized I had some information that I retrieve in raw text stored in buckets, some in SQL databases on Supabase, and then I had other ones that were semantic that I needed to run semantic search over. For those sections, I ended up testing locally with pgvector. When it was time to go live to production for internal testing or to staging for internal testing, I realized that instead of tearing down all of that pgvector coding and having to write the code for Pinecone, I could just as well use pgvector in internal testing. Recently, I started building a personalized portfolio website. I was not really impressed with all of the automated solutions out there, the frameworks that you have to put in specific information that grants a specific kind of output. I wanted to build a personal portfolio website where, beyond being able to pull my projects from GitHub, it is also able to have an AI-powered RAG solution on the back end that visitors can interact with and ask about me, ask if I have used a particular framework, if I have used a particular library, if I have built a solution that looks a certain way, and then it can answer without them having to contact me. That was my most recent use of Supabase Vector because the information that came with every README, every text file that I used for each project shown on my personal portfolio website had to be chunked so that the AI can use it in responding to user queries whenever they visit the website.
Co-Founder at a tech services company with 1-10 employees
Real User
Top 5
Jun 9, 2026
I use Supabase Vector for semantic duplicate detection. The app allows citizens to submit and vote on political proposals, and we do not want twenty nearly identical versions of the same idea. When a user submits a proposal, we generate an embedding of the title and description, run a vector similarity search against existing proposals, and if anything scores above a set threshold, we show those matches to the user so they can vote on an existing item or an existing element instead of creating a duplicate. The approach using Supabase Vector works really well for me. The app does not have a huge amount of users, but some of them have used that feature and avoided duplication. I'm really happy with that. Although it was a proof of concept at the beginning, when I launched that application in real life and saw users use that feature the way I was expecting, it was really encouraging. The feature regarding duplicate detection is basic and simple, and I was expecting the user to avoid duplication in the database. It helps to reduce the number of elements that create noise and bad use cases or bad elements. It was really nice to use that implementation and that technology in my app.
Director at a tech services company with 1-10 employees
Real User
Top 5
May 23, 2026
Our main use case for Supabase Vector is to use pgvector as the vector database solution to store our embeddings for large language model applications. A specific example of how I'm using Supabase Vector for our large language model application is when we are building the Retrieval Augmented Generation (RAG) pipeline for an education application for one of our clients. We used the client's training material to perform chunking and embedding generation, and then stored the embeddings into Supabase Vector. The major purpose of my use case with Supabase Vector is to store the embeddings.
Software Developer at a performing arts with 1-10 employees
Real User
Top 10
Apr 7, 2026
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 create a backend for that part and host it. I can use Supabase APIs, such as their connection string, and it functions as a BaaS, backend as a service.
I have wanted to delve into the backend process, which is just another whole different universe. Supabase Vector puts such an entire universe into a small planet and gives it to the front-end developer, making it really good. I use it as my database for authentication for real-time features and a lot of stuff. The tool is literally used for BaaS. If, as a solo developer, I want to start something of my own, and I do not have the resources to afford backend developers, or if I even want to learn about the backend, then Supabase Vector is a great replacement. I don't have to deploy the backend because it already comes as a deployed service on the tool's cloud. The main USP of Supabase Vector is that the API requests are not charged, so they are unlimited.
We use the product primarily to handle data models and API integrations. It simplifies security setup, API documentation generation, and database management.
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...
As an AI Engineer, my primary use case of Supabase Vector is for storing vector databases that I use at retrieval and inference in my AI agent and RAG pipelines. I have been working with RAG solutions, and often times I have to chunk and embed information into vectors that need to be stored in databases so that at inference we can always retrieve the relevant chunks from the database. I was building on Pinecone, but then I had to test something with pgvector locally. After testing pgvector locally for a solution I built for Algorithmi UK, which is essentially a teacher co-pilot solution, I realized I had some information that I retrieve in raw text stored in buckets, some in SQL databases on Supabase, and then I had other ones that were semantic that I needed to run semantic search over. For those sections, I ended up testing locally with pgvector. When it was time to go live to production for internal testing or to staging for internal testing, I realized that instead of tearing down all of that pgvector coding and having to write the code for Pinecone, I could just as well use pgvector in internal testing. Recently, I started building a personalized portfolio website. I was not really impressed with all of the automated solutions out there, the frameworks that you have to put in specific information that grants a specific kind of output. I wanted to build a personal portfolio website where, beyond being able to pull my projects from GitHub, it is also able to have an AI-powered RAG solution on the back end that visitors can interact with and ask about me, ask if I have used a particular framework, if I have used a particular library, if I have built a solution that looks a certain way, and then it can answer without them having to contact me. That was my most recent use of Supabase Vector because the information that came with every README, every text file that I used for each project shown on my personal portfolio website had to be chunked so that the AI can use it in responding to user queries whenever they visit the website.
I use Supabase Vector for semantic duplicate detection. The app allows citizens to submit and vote on political proposals, and we do not want twenty nearly identical versions of the same idea. When a user submits a proposal, we generate an embedding of the title and description, run a vector similarity search against existing proposals, and if anything scores above a set threshold, we show those matches to the user so they can vote on an existing item or an existing element instead of creating a duplicate. The approach using Supabase Vector works really well for me. The app does not have a huge amount of users, but some of them have used that feature and avoided duplication. I'm really happy with that. Although it was a proof of concept at the beginning, when I launched that application in real life and saw users use that feature the way I was expecting, it was really encouraging. The feature regarding duplicate detection is basic and simple, and I was expecting the user to avoid duplication in the database. It helps to reduce the number of elements that create noise and bad use cases or bad elements. It was really nice to use that implementation and that technology in my app.
Our main use case for Supabase Vector is to use pgvector as the vector database solution to store our embeddings for large language model applications. A specific example of how I'm using Supabase Vector for our large language model application is when we are building the Retrieval Augmented Generation (RAG) pipeline for an education application for one of our clients. We used the client's training material to perform chunking and embedding generation, and then stored the embeddings into Supabase Vector. The major purpose of my use case with Supabase Vector is to store the embeddings.
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 create a backend for that part and host it. I can use Supabase APIs, such as their connection string, and it functions as a BaaS, backend as a service.
I am exploring Supabase for my project on UMKM.
I have wanted to delve into the backend process, which is just another whole different universe. Supabase Vector puts such an entire universe into a small planet and gives it to the front-end developer, making it really good. I use it as my database for authentication for real-time features and a lot of stuff. The tool is literally used for BaaS. If, as a solo developer, I want to start something of my own, and I do not have the resources to afford backend developers, or if I even want to learn about the backend, then Supabase Vector is a great replacement. I don't have to deploy the backend because it already comes as a deployed service on the tool's cloud. The main USP of Supabase Vector is that the API requests are not charged, so they are unlimited.
We use the product primarily to handle data models and API integrations. It simplifies security setup, API documentation generation, and database management.