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One of the most convenient way for you to build a LLM-based Application
What do you like best about the product?
You can deploy pinecone very fast without caring about the backend things like docker,storage etc. with an account you can directly building your app with the offical API and python code.
What do you dislike about the product?
the price is relatively high comparing to some opensourced alternative.
What problems is the product solving and how is that benefiting you?
We are building a LLM-based Application.
Pinecone is the essential part of RAG solution.
Pinecone is the essential part of RAG solution.
Easy to use and powerful vector database
What do you like best about the product?
It is very easy to integrate the Pinecone API with a text generation application using LLM. Semantic search is very fast and allows more complex queries using metadata and namespace. I also like the comprehensive documentation.
What do you dislike about the product?
For organizations that need only a little more capacity than is available in a single free pod, the pricing may be more favorable.
What problems is the product solving and how is that benefiting you?
We use Pinecone as a vector database containing almost 150,000 of decisions of the Supreme Court of the Czech Republic and approximately 50 legal statutes. Pinecone serves as the backbone for the knowledge retrieval (RAG) of our legal research application.
Great dev experience
What do you like best about the product?
Easy to use
Good documentation
Easy to implement
Good documentation
Easy to implement
What do you dislike about the product?
Couldn't delete an entire vector within a namespace
What problems is the product solving and how is that benefiting you?
Vector index storage provider. We store embedded indices on Pinecone.
User-friendly enterprise grade vector database
What do you like best about the product?
We started using Pinecone pretty early on. I like the light UI on top of an API-first approach. We have been using it now for millions of daily queries, and it has rarely, if ever, gone down or giving us trouble. Highly recommended!
What do you dislike about the product?
Not sure what to say here. It's been a good experience overall. If I had to say something, the pricing was tricky to groc.
What problems is the product solving and how is that benefiting you?
Fast retrieval of multi-modal search queries
fast and easy to setup vector database
What do you like best about the product?
The things I mostly like are:
- that is easy to set up by following the docs
- fast for loading and updating embeddings in the index
- easy to scale if needed
- that is easy to set up by following the docs
- fast for loading and updating embeddings in the index
- easy to scale if needed
What do you dislike about the product?
- that is not open source
- I cannot query the full list of ids from an index (I needed to build a database and a script to track what products I have inside the index)
- customer support by mail takes too much time
- I cannot query the full list of ids from an index (I needed to build a database and a script to track what products I have inside the index)
- customer support by mail takes too much time
What problems is the product solving and how is that benefiting you?
I built a deep learning model for product matching in the ecommerce industry. One of the steps for the system is to find candidates that are potential matches for the searched product. Becase of this, I needed a vector database to store the embeddings (texts and image) for the products for doing a similarity search as a first step of the product matching system.
GWI on Pinecone
What do you like best about the product?
Easy of use and metadata filtering. Pinecone is one of the few products out there that is performant with a query that contains metadata filtering.
What do you dislike about the product?
The pricing doesn't scale well for companies with millions of vectors, especially for p indexes. We experimented with pgvector to move our vectors in a postgres but the metadata filtering performance was not acceptable with the current indexes it supports.
What problems is the product solving and how is that benefiting you?
Semantic search for now.
Production-ready vector database to get you started quickly
What do you like best about the product?
- Good documentation and usage examples
- Easy-to-use Python SDK
- Production-ready with low latency at our scale (10-20M vectors)
- Good integration with the AI/LLM ecosystem
- Easy-to-use Python SDK
- Production-ready with low latency at our scale (10-20M vectors)
- Good integration with the AI/LLM ecosystem
What do you dislike about the product?
- did not find an easy way to export all vectors that we needed for data science/cleaning
- will get expensive when hosting 100s of millions of vectors
- will get expensive when hosting 100s of millions of vectors
What problems is the product solving and how is that benefiting you?
We use Pinecone as a vector database for retrieval augmented generation using LLMs.
Solid Hosted Vector DB
What do you like best about the product?
Ease of deployment! It takes just a few minutes to get an index set up and deployed.
What do you dislike about the product?
The web-based API console could be improved, for example for experiments with metric (cosine vs dotproduct vs euclidean).
What problems is the product solving and how is that benefiting you?
Storing embeddings for RAG.
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