Easy and Dependable Vector Database
What do you like best about the product?
I really appreciate how Pinecone makes it easy to integrate vector search into applications. Its cloud-native setup and simple API mean I don't have to worry about infrastructure issues. Also, the performance is fantastic, even with massive amounts of data, and the low latency is a huge plus.
What do you dislike about the product?
Being relatively new, it lacks some features and integrations compared to more established databases. And, there's a bit of a learning curve to fully leverage its capabilities. Additionally, there are some limitations regarding customization and exportability of vectors outside of Pinecone.
What problems is the product solving and how is that benefiting you?
Semantic Search: Pinecone excels in understanding the context and meaning of queries, which is essential for accurately retrieving relevant information during meetings.
Recommendation Systems: Its ability to handle complex data makes it suitable for suggesting relevant topics or actions based on the meeting's context.
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.
Fast & Reliable
The Pinecone pay as you go marketplace offering made it easy for us to quickly get up and running to do our evaluation of the tool. The pricing link to their website in the marketplace description is easy to understand and accurate based on our usage of the tool.
Great dev experience
What do you like best about the product?
Easy to use
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.
Pinecone fails to give accuare results
What do you like best about the product?
Pinecone is fast and fully managed. It also allows you to duplicate your index and create a new one. It was well suited for us.
What do you dislike about the product?
It provides inaccurate search results even for simple semantic search.
What problems is the product solving and how is that benefiting you?
We use it to build a conversational chatbot over users documents. A user can upload thousands of documents and we can build a chatbot for them using 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
Ease to use and implementation
What do you like best about the product?
Quick to signup and implement and use it as daily basis. Performance is stable and very good.
What do you dislike about the product?
I don't have anything bad about Pinecone.
What problems is the product solving and how is that benefiting you?
We are building the RAG application.
Great for prototype to production
I started using Pinecone with a free account when prototyping a new use case for vector search. Pinecone proved easy to work with and simple to get something working off the ground into the hands of my company. I was quite pleased with the setup process moving to the AWS marketplace as we put this prototype into production. It was simple to add to our existing AWS account, the Pinecone UI connects nicely to our test account so I can switch between production and test with ease - really useful for local development and debugging. Overall found the experience working with Pinecone to be great. There are certain areas that would be great to improve, the Node library was a bit funky when I first started using it (but that was a pre 1.0 version), the dashboard in the UI could add a few additional features to navigate and visualize what is stored in the DB, but those are all minor nice to haves that I'm sure will come. The search quality and reliability has been great and that was most critical to our needs right now.l
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
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
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.
