Faced challenges with metadata filtering but have achieved reliable long-term memory for chat applications
What is our primary use case?
My main use case for Pinecone involves storage of chat data, specifically chat transcripts, and retrieval of matched chat messages.
We store chat transcripts as vectors in Pinecone. When we have a new chat message, we utilize a retrieval mechanism to match and find the last five messages so that it can act as a memory. Essentially, Pinecone serves as a long-term memory for our application, while we use Redis for our short-term memory.
What is most valuable?
We were looking at multiple options for a vector database, and we found Pinecone to be the easiest to integrate into our solution. Plus, it has a very generous free tier, which helps us as a startup.
The best features Pinecone offers are quick setup and good indexing for us. The retrieval mechanisms are fast, and the integration with Python as with JavaScript and TypeScript libraries that Pinecone provides are very robust. Authentication is also very good.
The namespaces feature allows us to break down or store data for each user separately, reducing interference and maintaining privacy as an important feature.
Pinecone has positively impacted our organization by enhancing efficiency for the team, and the long-term effect has been that the chats have become much more personalized due to the memory added through a vector database. We are seeing that the trainees getting trained on the platform are more satisfied with the results or messages generated by AI.
What needs improvement?
One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata. This can cause problems because while vector indexing or vector search is good, if you populate certain categories of messages or metadata into a vector database, searching through the data using the filter of metadata is not possible.
For our requirements, Pinecone is more than enough. If improvements are required, I would suggest taking a look at the embeddings and possibly improving the embedding sizes.
For how long have I used the solution?
I have been using Pinecone with code for one and a half years.
What do I think about the stability of the solution?
What do I think about the scalability of the solution?
Pinecone's scalability is pretty decent for us, as we have not encountered issues. We are storing close to around 600K items or entries in the database, and our indexing and retrievals are within seconds, often in microseconds.
How are customer service and support?
The customer support of Pinecone is very good; you send an email and receive a response within a few hours, typically four to five hours. Additionally, you can set up a call if needed.
Since we are on the minimal plan, I would rate the customer support around 8 out of 10.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We previously tried setting up with Weaviate and another solution. During my research, we checked out a couple of options, including an on-prem solution that I tried to set up on my machine, but it was very painful, so we went with the cloud service provider because the setup was nearly nonexistent.
How was the initial setup?
The setup cost for us is nil, and the licensing and pricing are pretty decent. Pinecone works on the storage amount, so our bills are pretty low, and we are good.
What's my experience with pricing, setup cost, and licensing?
The setup cost for us is nil, and the licensing and pricing are pretty decent. Pinecone works on the storage amount, so our bills are pretty low, and we are good.
Which other solutions did I evaluate?
Before choosing Pinecone, I evaluated a few options, including Weaviate.
What other advice do I have?
I would suggest that Pinecone is one of the best options available. I would rank it in the top three for vector databases and qualify it as number one in the market. There are many others such as Weaviate and Milvus, but they come with certain issues such as lacking a free tier or having a very low one.
Moreover, solutions like Milvus and FAISS are on-prem, which makes setup and stability a pain, primarily catering to big enterprises. For startups, Pinecone is indeed the best.
We are just a client of Pinecone; we do not have any other business relationship.
Rating: 4/5
ideal for machine learning, AI applications and similarity search
What do you like best about the product?
It is specialised in AI driven use cases with real time and low latency search giving seamless integration into machine learning workflows with scalable infrastruture optimized for unstructured and semi-structured data in AI applications.
What do you dislike about the product?
It has limited focus that is related only with the vector data with no major focus on Business intelligence in data transformation tool.
Also it's use case is little complex with lack of ecosystem integration.
What problems is the product solving and how is that benefiting you?
It is solving the issue related with AI vector data generated from the app.
Pinecone assistant beta user
What do you like best about the product?
I have been using pinecone for embeddings and it is cheaper and reliable compared to other embedding services.
What do you dislike about the product?
I dislike the overall feel which feels lightweighed for the product service documentation.
I love to see pinecone assistant in deployable version because it is powerful yet it is in the beta version only for testing not for production
What problems is the product solving and how is that benefiting you?
Creating embeddings at ease without any big pricing.
Good support from team.
Solid option for vector DB
What do you like best about the product?
Easy to use. very reliable and fast. Competitive price
What do you dislike about the product?
Maybe some extra features would be nice, and some more clarity into its AKNN algo, which is hidden from the user
What problems is the product solving and how is that benefiting you?
Finding scientific documents in very large volumes of Data.
Pinecone: The Backbone of Efficient Vector Search and Retrieval
What do you like best about the product?
Pinecone excels in providing a seamless, high-performance vector search experience. Its ease of use, combined with powerful features like real-time updates and scalability, makes it a go-to solution for managing complex vector data. The ability to effortlessly integrate with existing workflows and its top-notch customer support are definite highlights.
What do you dislike about the product?
While Pinecone is robust, the pricing can be a bit steep for smaller projects or startups. Additionally, more granular control over indexing options would enhance customization for advanced users. However, the benefits far outweigh these minor drawbacks.
What problems is the product solving and how is that benefiting you?
Pinecone is solving the complex challenge of efficient and scalable vector search. In an era where managing large volumes of high-dimensional data is critical, Pinecone's ability to index, search, and retrieve vectors quickly and accurately is a game-changer. For us, this means faster query responses, enhanced data retrieval accuracy, and the ability to focus on building better products rather than managing infrastructure. Pinecone's solution has drastically reduced the time and effort required to manage and search vector data, allowing our team to be more productive and innovative.
A great serverless DBaaS for vectors
What do you like best about the product?
Pinecode offers a simple API and lean management interface for a completely low maintenance vector storage and query solution.
What do you dislike about the product?
I started using Pinecone when it was new and had some rough edges. But support was proactive and smart. In the last year I can say there is nothing to not like. It has been awesome.
What problems is the product solving and how is that benefiting you?
We use Pinecone's serverless platform (on AWS) for vector search. Our vector dimension is 3072. Part of our use is user queries. The performance has been excellent and scalability is automatic. We also use the query capability in other parts of our stack where performance is not so important but reliability is a factor.
Very easy to use, minimal setup effort required
I decided to use Pinecone DB as the vector database for Amazon Bedrock Knowledge Bases. My application required that I use Retrieval Augmented Generation (RAG) to answer questions about PDF business documents that I have stored in an Amazon S3 bucket. Pinecone DB is incredibly high performance and also offers a free tier, along with centralized billing through AWS Marketplace. I would highly recommend using Pinecone DB!
Best and affordable vector database
What do you like best about the product?
Pinecone's new serverless pricing is very affordable for small startups. It support large embeddings size, sparse & dense embedding and fast queries. It suited my needs.
What do you dislike about the product?
It has 10,000 namespace limit on serverless instance. It should be increased.
What problems is the product solving and how is that benefiting you?
I use it to store embeddings of PDF files and then ask questions using LLM models.
First and Last Stop for a Vector Database
What do you like best about the product?
Excellent user interface, excellent supporting materials and literature to learn, very easy to use, improving quite quickly. It is quite easy to implement it in integration with our existing workflow. I use it for all vector database operations.
What do you dislike about the product?
I have some very technical questions, like: will hybrid search ALWAYS be limited to dot product? But these are quite few.
What problems is the product solving and how is that benefiting you?
Making it easy to implement a vector database for semantic search in RAG applications
Effortless Vector Storage to Give Your AI App Infinite Intelligence
What do you like best about the product?
Pinecone is great for super simple vector storage, and with the new serverless option the choice is really a no-brainer. I've been using them for over a year now in production, and their Sparse-Dense offering made a huge impact on the quality of retrieval (domain-heavy lexicon). The tutorials and content on site are both extremely well-thought out and presented, and the one or two times I reached out to support they cleared up my misunderstandings in a courteous and quick manner. But seriously, with serverless now, I'm able to offer insane features to users that were cost-prohibitive before.
What do you dislike about the product?
I can tell you what used to be challenging: which was cost monitoring and the web interface, both issues which have been drastically improved in the recent months. The web interface is still a bit cumbersome to use, but that's only because vector storage/search is not what you would expect coming from other "content" management systems. There isn't a lot of hand-holding like you might find elsewhere, but really—if you're in this space, you do have to do a lot of work on your own anyways. Hard to find something to dislike when it "just works."
What problems is the product solving and how is that benefiting you?
My app leverages decades of internal and external content around the business of writing great stories. Pinecone's vector database makes it easy to store all of this knowledge in a way that is easily and QUICKLY recovered based on semantic meaning. And now with serverless (and its wild affordability), I can now extend that knowledgebase to ALL of my user's stories and creations such that everyone can have their own expert assistant tailored to their particular style.
