Supabase Vector Reviews

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.

I think the best feature Supabase Vector offers, or the most valuable thing I've noticed, is that pg_vector comes built in straight out of the box. I did not have to stand up or maintain a separate vector database. Getting one source of truth or one point that I can keep everything together is the main thing. The embeddings live in the same PostgreSQL as my relational data, so I can join them with metadata, apply row-level security, and keep one source of truth. I think that is the key part and most valuable for me in that regard.

Having everything in one place, embeddings and relational data together, gives me full control through SQL. I can write my own RPC functions from the similarity search using the cosine distance operator, all inside the same database. That gives me control over the threshold, the columns, the return data, and the queries I'm using. Getting all these things together in the same database is a huge advance and it was a very neat implementation of the feature I wanted to build.

I was using Supabase, so not only getting the database but also the authentication, the vectors, and the connections with webhooks to connect everything together, because the app natively used webhooks to connect with n8n. That flow was in charge of connecting with the database on Supabase. Being able to create that flow was amazing to get all that information running from one part to another and create that flow that connects everything. I was also able to launch Firebase notifications straight away to all users using n8n, so it was amazing to manage these complex features with those technologies.

I had problems integrating the vector directly into Supabase, so I had to use Google Vertex to generate the embeddings and the information I needed in the database. It would be nice if all of this could be integrated all in one place with Supabase. I would prefer not to have to use a different or external tool to create these embeddings. It would be nice to have everything integrated in the same way. Apart from that, I think that is one of the cons I found, but it is basic.

That was my main concern in that regard. My advice for others looking into using Supabase Vector is to take into account how you are going to create the embeddings, as it is not an option implemented straight away in Supabase. You will need to handle the embeddings with an external tool, so make sure to consider how these integrations are going to be made.

I have used Supabase Vector for approximately six months.

From my experience, Supabase Vector is stable. There have been no failures, and the app is up and running, so I have no concerns in that regard.

Regarding Supabase Vector's scalability, the app is still in a very basic stage, so scalability has not been an issue at the moment. The app is getting users, but it is not having thousands of users creating problems with scalability. Therefore, I do not have enough information to answer this question properly.

Customer support is handled using emails at the moment, and since I am not using customer support from Supabase, I suppose it does not have any problem or concern in that regard.

At the beginning, I was trying to build that part on my own in my VPS, trying to build the database and everything together. When I realized that this implementation was much more complex than I thought, that is when I decided to move to Supabase. The integration problem was how to connect this complex flow to detect duplicities between the app and Supabase. I decided to integrate n8n to control that part of the flow and determine when a user was creating a duplication and needed to return to the app, or if the proposal was new, go straight away to the database to create embeddings and store everything.

I have always worked on Firebase, but I was tired of using the Firestore database, which did not cover my needs for this project and the features I wanted to implement. I realized that due to the lack of implementation or technology in Firebase, I decided to move to Supabase and use this PostgreSQL database. It allowed me to create the structure for all the complex objects I wanted and needed and also create the vector and embeddings in the same tables. Additionally, I investigated the Datastore on Firebase, which I think has a similar approach, but my main concern with Firebase Datastore is the pricing. I think it is all implemented in cloud services, and I am not willing to pay that price, which is at least 10 pounds monthly. For now, I will stick with Supabase, but if that price changes in the future for Firebase, I would investigate it further.

The integration and setup were complex, but the pricing is decent. I am using the Supabase free trial, which allows two databases, but it would be nice to have more than those two. The biggest complexity lies in the knowledge required to get all these pieces together. The app I have implemented uses several technologies including KMP for building Android and iOS apps, Firebase, Supabase, n8n, webhooks, and everything built on GitHub. You have to have decent knowledge of all those technologies and how to handle the Google API console to create the APKs for the embeddings. The knowledge requirement is the most complex part, but apart from that, the pricing is not a significant issue.

I have not seen specific benefits in terms of time saved or improved efficiency yet because, as I said before, it was a proof of concept implemented in a basic app just to test it and be able to launch from the beginning to production. I was able to test it in production, and due to the good results and the positive feedback I got, I'm trying to increase the use of this technology in other apps and other parts of the software I have. I am definitely keen to keep exploring and making some research on how to implement this technology in other parts, and for sure, it was a great technology.

The most valuable part using Supabase Vector is how complex things I was able to build and deploy in a short period of time. Regarding the performance, I do not have enough data because, as I said, the app is still getting bigger and gaining more users. The number of users or the performance is not being a problem, or at least I did not have any issue in that regard. It is a technology I think is good and decent to be put in production. I am constantly looking for new problems or new features that I can implement and use this technology.

I have not seen a return on investment yet because I am not investigating those points. However, I can say that building these kinds of applications with so many complex components in less than a couple of months is amazing. The use of these technologies definitely impacts reducing the time and cost of implementation or deployment. It has a really good impact on companies, employees, and finances.

In this basic implementation or proof of concept project, I use the basic Supabase project available in the free trial. I am not sure which one of those options it falls under, but I use the free Supabase options to create two different databases—one for debugging or testing and the second one for release. I tend not to mix both environments. Apart from that, I use it with the full functions I needed using the free tier, which was amazing to be able to create all this technology for free, without the need to pay additional costs to use those technologies, apart from the embeddings ones from Google.

It was amazing to be able to create this software using Supabase Vector. I really recommend it and hope to see more improvements of this tool in the future.

My advice for others looking into using Supabase Vector is to take into account how you are going to create the embeddings, as it is not an option implemented straight away in Supabase. You will need to handle the embeddings with an external tool, so make sure to consider how these integrations are going to be made. I would rate this product an 8 out of 10.

My main use case for Supabase Vector is building a RAG pipeline, specifically for one of India's largest, one of the top five news channels, for their blogging website. We built a product where, on a news article, we show the relevant product according to that article and the user. We basically built a RAG pipeline based on that product, where it searches for the product according to the article and shows it to the user on the blog page.

For my use case, I wanted to show the relevant products according to the article to the user, and the setup was fairly straightforward. We extracted the details from the articles, including the category and other details, and used embedding to search the products' embedding that we had. We performed cosine similarities accordingly, leading to a simple and straightforward setup.

Supabase Vector specifically helped us match the relevant products to the articles and users through vector embedding, which has been a huge help. Supabase also has a lot of connections or trigger functions that you can use and manage from the dashboard itself, making it pretty helpful and useful.

The best features Supabase Vector offers include trigger functions and easy management from the dashboard. Triggering Postgres functions from the dashboard and enabling vector embedding, along with downloading those PG extensions, was super easy.

Out of those features, I find the entire dashboard to be the quickest part, along with using and testing the functions with the SQL editor, which is simple and straightforward.

Supabase Vector has positively impacted my organization by significantly reducing our testing time. Initially, when we used a traditional RDS database, our engineer struggled with proper testing, but once we moved to Supabase Vector, it got super easy with the dashboard and everything being clear. We shared credentials across giving access to our internal developers, allowing them to access and test cosine similarities of the vector, making the process simple compared to the complexity of connecting via terminal and SSH.

I estimate that we saved a lot of time after switching to Supabase Vector; it took us just two days to fully do the entire end-to-end testing setup. Initially, we planned it would take us more than two weeks if we had followed the normal route without using Supabase Vector, such as normal Postgres setups on a cloud server.

I think an improvement for Supabase Vector would be to have it enabled by default. Currently, you need to go through additional steps; we also deployed the open-source version of it, which lacks many features that the platform provides.

I would say the edge functions and logs of vectors and those triggers are often missing, especially in the open-source version of Supabase, which stands as a needed improvement.

Supabase Vector is fine as it is; I do not believe it needs any improvements. It is a perfectly fine product.

I have been using Supabase Vector for two to three years, and we were using it for a RAG pipeline.

Supabase Vector is stable; I find it to be pretty stable.

The scalability of Supabase Vector is impressive; it is pretty scalable and stable at the same time.

My experience with customer support is positive; I needed to reach out to Supabase, and the customer support team was pretty good, responding quickly to my issue. They resolved tickets efficiently, which I found very productive.

I would rate the customer support a nine since they replied quickly and answered my questions properly, which helped me a lot.

Before choosing Supabase Vector, we used AWS RDS as our previous solution, and we switched because we wanted a few of our engineers to test the RAG system and manipulate parameters with the SQL editor. We also needed to enable them to see the product data and its embedding, which was easier with Supabase Vector, making the migration from AWS pretty straightforward for our different use cases.

My experience with Supabase Vector's pricing, setup cost, and licensing is that it was pretty straightforward; it has an MIT license, which is good for us. We generally use a $200 to $300 subscription based on our database size, which is also fine for vectorization and other tasks.

We did not purchase Supabase Vector through the AWS Marketplace; instead, we simply did a git clone on an EC2 instance and ran it from there since Supabase is open-source software.

I have seen a clear return on investment; we saved money because RDS is pretty costly. Supabase includes all functions we were using, and we previously took backups manually, which was acceptable. The dashboard's management made access straightforward for users and super easy to maintain, resulting in very few errors. We completed what we initially estimated as a fifteen-day job in just three days, thanks to the Supabase dashboards and triggers.

We evaluated other options, specifically AWS RDS, and chose it based on its pricing and maintenance restrictions. We needed something that allowed proper developer access to the database without unnecessary security limitations, which Supabase Vector provides.

My advice for those considering using Supabase Vector is that they should definitely start with it; it is super easy and very easy to maintain. A backup setup with a Python script placed in an S3 bucket would also be good and save a lot of costs.

Supabase Vector is pretty good software that saves a lot of time and is super easy to use. I would rate this review a ten overall.

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 have been comparing many different similar products for storing embedded vectors, and I would say one advantage of Supabase Vector is that pgvector is embedded into Postgres, which makes it convenient for business use cases because we do not need to manage another data source.

Having pgvector embedded into Postgres helps our business use cases specifically as it is a very convenient way. We have Supabase basically as the host of most of our business relational database and user data, so since the client's applications are migrating to language model-empowered features, it is very useful, and we do not need to register for other database types.

Supabase Vector has positively impacted our organization as it is very convenient since our business databases are already hosted in Supabase, making integration easy. This convenience has led to measurable outcomes, as we also tried to use Pinecone and Weaviate, where we need to facilitate extra costs for storing the vectors. Now, since our business databases are already in Supabase, we only need to deal with one bill.

One improvement I feel Supabase Vector could benefit from is that Supabase SDK stands out when comparing with a conventional Postgres SDK, and it would be even nicer if we could have a more direct way for access.

I wish that there was a convenient way to make it compatible with the general Postgres database SDK, but this is actually not about the vector database; it is about the general Supabase SDK.

I have been using Supabase Vector for almost three years.

Supabase Vector is stable, and I have no complaints.

Supabase Vector's scalability works fine so far in our scale of applications, but I cannot tell what will happen if it scales out even more.

We usually just check the documentation or ask artificial intelligence for tech support, which is why we do not have much experience with customer support.

In my previous experience, we used Weaviate and Pinecone as the vector store, and the choice depends on the client's project requirement. For recent clients, the search was not dedicated for the Retrieval Augmented Generation pipeline for developers themselves, and we were using the Azure Search service, meaning we no longer directly used the low-level chunking and embedding technologies anymore.

I have seen a return on investment, as it obviously saves us a few hundred dollars every month compared with the approach of deploying the vector database on other providers.

I do not feel anything special about the pricing, setup cost, and licensing; we just regularly pay whatever we need, and I do not feel much difference.

Before choosing Supabase Vector, I evaluated Weaviate, Pinecone, and I also had a short experience with LanceDB, which is a local one similar to SQLite.

I do not have much to add about the features of Supabase Vector; I think so far this is good.

I would advise others looking into using Supabase Vector that if your business databases are already hosted on Supabase, pgvector on Supabase is definitely worth trying because it can save you a lot of time when you are bootstrapping and exploring this domain before making a decision to perhaps try using other solutions.

I rate this product an 8 out of 10.

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.

Supabase Vector is easy to set up and cost-effective because the alternative is Firebase, which requires a credit card. Most people don't want to use their credit card, so with Supabase Vector you can use the product without entering your card details. The services are straightforward and the database support is PostgreSQL, which is beneficial.

It's easy to set up and it has a free tier with PostgreSQL extensions with Supabase Vector. Every product I built has a free tier, so sometimes users can use the free tier. Some companies are still using the free trial because they didn't exceed their limit.

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 little difficult. If you are naming a table, the guidance is sometimes not clear. It's a very simple feature to add sometimes but the process is very difficult. When I'm in Supabase Vector, there is a feature where I have to create a table. At the start, for newcomers, it's difficult, and then it becomes hard. You have to look for the documentation and YouTube videos to understand how it works.

Supabase Vector stopped working in India due to some government guidelines, so I have to switch with something else now.

I have been working with Supabase Vector for Postgres for around one to two years.

I would initially give Supabase Vector a seven for stability, but I would revise that to a five because there is currently downtime going on in India.

For the scalability part, I can give Supabase Vector an eight.

I recommend Supabase Vector to other users.

I previously used MongoDB instead of evaluating vector embeddings in Supabase Vector.

The setup for Supabase Vector is easy. I'm a developer, so I have set up the product myself. I have set up Supabase Vector by myself.

I purchase Supabase Vector directly from the vendor.

I compare Supabase Vector with Firebase. Supabase Vector is SQL and Firebase is NoSQL, so it's a bit different, but Firebase has more features than Supabase Vector.

I don't know about the efficiency of Supabase Vector in the case of high-dimensional data, and I currently don't have an idea of that part. It can be a bit cheaper. I would give this product an overall rating of eight.

We have long PDF files we need to process, and then we need to ask questions and get answers on those files. My main usage is RAG for chats on PDFs, because models have limited memory in context, so less token usage and processing a large amount of data with question and answering capability.

I have been using Supabase Vector mostly for RAG. I have been using it for when a user is doing a lot of chats back and forth, and it is not useful to put all of the chat into the context of the LLM. I use it for chats on PDFs, and where I do not want to process and waste tokens.

In the same database I can utilize the embeddings and vectors provided by PG Vector. Additionally, Supabase Vector is a managed service, so I do not need to worry about scaling the database and managing the infrastructure. I can be up and running with Supabase Vector within a few seconds. The other valuable feature is semantic search, which I can do via PG Vector.

The managed service that Supabase Vector offers makes my workflow easier because I do not need to worry about managing the infrastructure or scaling the database. If I have an app idea and come up with a great app idea and I need to prototype and then push that idea into the market, I do not have to care about the infrastructure or scaling. In an instant, I get the managed Supabase Vector service available via the APIs, and then I just need to focus on my product.

The semantic search feature helps me specifically when working with my PDFs and chat use case because it is one of the core techniques behind modern AI chat systems and knowledge base assistants. Traditional keyword search may fail when words are different even though the meaning is nearly identical. For example, imagine a PRD that states the platform must support employee shift swapping with manager approval. When a user asks can workers exchange shifts, a keyword search may fail because exchange is not equal to swap and workers is not equal to employees. Instead of storing words, Supabase Vector stores embeddings, a vector.

An embedding is a list of numbers representing the meaning of text. When a user asks can workers exchange shifts, the system converts the question into the embedding and searches for nearby embeddings to find the PRD section about shift swapping and returns it to the LLM. The LLM now answers using the relevant section. If I have a 200-page PRD for my workforce management SaaS, chunking the document and generating the embeddings and then finding the closest chunk automatically gives me the meaning. It automatically searches for employees even though the query asks for workers. It really captures meaning instead of straightforward word-to-word search.

Supabase Vector positively impacts my organization by reducing the cost of the LLMs. Prior to using vector and RAG, I was giving whole chats to the LLM, and the LLM was processing the whole context and maintaining context. There is a limit in the LLMs of 1 million to 200,000 tokens I can process. With Supabase Vector, I have really reduced the cost and token consumption as well.

I do not have anything in my mind right now, but I can get back with this answer.

I do not have anything in my mind, but I will get back when I have more information.

My typical use cases for Supabase Vector include earlier using it for a production app in my company, and currently, I am building my own SaaS, my own software as a service. It's an AI product where vector searches in the database are really helpful, so that's the main primary use right now.

I utilize vector embeddings in Supabase Vector, and that is required when I'm working with AI products.

Supabase Vector rapidly increases the speed and efficiency with which I search through a database, helping with my data analysis tasks.

I have experienced multiple benefits from PostgreSQL extensions in Supabase Vector, which are add-ons that help my database become more scalable or efficient, or when I want to have different functions deployed, such as Edge Functions.

I notice that the schema visualizer can be improved. Additionally, the internal AI assistant powered by GPT can also be improved.

I have been using Supabase Vector in production apps for almost more than two years now.

I do not face any challenges during implementation.

I can tell you how effectively Supabase Vector handles high-dimensional data, but I don't have much time right now for that.

I have never had to contact the technical support for Supabase Vector as it was not really required.

I find that the pricing of Supabase Vector is justified for the value it provides, so I'm overall satisfied with the licensing cost. I give this review a rating of 9.

We use the product primarily to handle data models and API integrations. It simplifies security setup, API documentation generation, and database management. 

The product's feature for ease of setting up security and the automatic generation of API documentation has been invaluable. These features expedite development and ensure robust security. The performance reports and real-time notifications also benefit database management and performance tuning.

One area for the solution improvement is the inclusion of more sample code in various programming languages, particularly PHP. Expanding the support for complex transactional queries and enhancing scalability features for advanced use cases would also be beneficial.

I started using Supabase Vector around July of last year.

The stability is excellent. I rate it a nine or even higher, as the maintenance and support have been very reliable.

We anticipate around 2,000 Supabase Vector users for our new project. 

I rate scalability as a six.

We had an issue during development, and the support team was responsive and helpful.

Positive

I have used Firebase and Azure before. We switched to Supabase based on positive community feedback about its development lifecycle benefits and rapid deployment capabilities.

The initial setup is straightforward compared to other products.

Deployment took less than ten minutes, and configuration and setup took about 15 to 20 minutes. It was even quicker for cloud services as it involved subscribing and commissioning the service.

I rate the process an eight and a half.

For smaller projects, one person is sufficient to handle implementation. However, a team of four may be needed to manage backend activities effectively for larger projects with thousands of users, like the one we are currently working on.

Supabase has saved us significant costs and development time, which is valuable for startups and existing companies alike.

I rate it a nine in terms of ROI. 

The solution's cost is reasonable compared to other solutions. We currently use the standard plan, which costs about $480 annually, and may switch to a full data plan depending on project needs.

I rate the pricing a three out of ten. 

The platform's role-level security feature is quite effective for spatial data management. It allows you to define and manage access controls efficiently. Another useful feature is the performance and analytics reporting, which helps you monitor and optimize your database performance. The AI prompts for query assistance are also beneficial, although I haven't used the real-time notifications feature extensively.

The features that have improved data analysis are the performance and tuning tips that Supabase provides. These tips help you optimize queries and functions, which is crucial for handling complex data operations. However, I've noticed that security settings are only sometimes visible in the data definition views, which can be challenging when recreating objects or debugging.

Overall, I rate it an eight. 

I am exploring Supabase for my project on UMKM.

Supabase is very easy to integrate. It is very clear and intuitive.

The UI really makes them the best. Their choice to use Postgres opens up many possibilities, such as for a Vector Database and more.

I think there are still many Postgres features that can be developed further by the Supabase team.

I have been using Supabase for 2 months.

I used another solution previously, but it was hard to understand. Supabase enables us to lower the skill floor while keeping the ceiling high.

The price is good.

I considered Postgres as an alternative solution.

Keep up the good work.