Reviews from AWS Marketplace
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
External reviews are not included in the AWS star rating for the product.
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.
- Leave a Comment |
- Mark review as helpful
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.
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.
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.
The fastest in production VectorDB yet
What do you like best about the product?
The speed. Hands down. QPS and the throughput is just the best in the industry. Easiest to get started with. Good support for parallel processing and batching.
What do you dislike about the product?
Nothing, just could release more complex document related retrieval systems.
What problems is the product solving and how is that benefiting you?
Semantic search is hands down a new way to search which is extremely efficient. Pinecone does a great job at not only providing the vector DBMS but giving the oppurtunity for scale.
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.
A Reliable and Consistent Performance
What do you like best about the product?
Pinecone has been a game-changer for our company, especially in the realm of vector embeddings. What stands out the most is its robust performance and reliability. Over the six months of our usage, we have not encountered any downtime, which is crucial for our operations. The consistency in performance has been remarkable, ensuring that our data-driven processes run smoothly and efficiently. Its seamless integration have made it an indispensable tool in our tech stack.
What do you dislike about the product?
As of now, we haven't encountered any significant issues or drawbacks with Pinecone. It has met all our expectations and requirements efficiently. However, we are always on the lookout for new features and improvements that can further enhance our experience and capabilities with the platform.
What problems is the product solving and how is that benefiting you?
Pinecone has been instrumental in efficiently managing vector embeddings, a critical component in our applications like similarity search and recommendation systems. Its scalability and consistent performance, coupled with zero downtime, have significantly improved our operational efficiency and user experience. By simplifying infrastructure management and enabling rapid integration, Pinecone has allowed us to focus on core business functions, accelerating development cycles and enhancing overall service quality. This reliability and efficiency have been key to maintaining high service levels and staying competitive in our market.
showing 11 - 20