Supabase Vector Reviews

My main use case for Supabase Vector is converting PDF files into chunks and then creating embeddings for those chunks and storing them in Supabase Vector.

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.

The best features that Supabase Vector offers include native vector storage in PostgreSQL. I do not need to switch entirely to a vector database because in the PostgreSQL database, I can do other things as well, such as storing relational data and other tables.

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 metrics in my mind right now, but I can look into the metrics and provide an answer.

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.

I have been using Supabase Vector for roughly two years.

I do not have additional thoughts. My overall review rating for Supabase Vector is 8 out of 10.

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.

Public Cloud

Other

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.