Pinecone Reviews & Product Details

It is a fast and efficient vector database.

The web-interface leaves many features to be desired. It is quite a bit on the pricier side.

We use it to hold educational material

- Its retrieval process is good compared to other vector DB - We can visualize it in UI

It could have been open source which can make it easily usable with high demand.

Doc search and embedding storage and text retrival

Pinecone used for indexing or searching of duplicate documents or similarity search score with our query. It helps to detect the anamolies easily. Mostly i liked this database to store my data as a vector form.

Pinecone premium subscription for various indexes and pods control.

Helps me to easily upsert vectorized data to pinecone vector Db.

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.

the price is relatively high comparing to some opensourced alternative.

We are building a LLM-based Application. Pinecone is the essential part of RAG solution.

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.

For organizations that need only a little more capacity than is available in a single free pod, the pricing may be more favorable.

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.

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 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 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.

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!

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.

Fast retrieval of multi-modal search queries

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.

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.

Semantic search for now.

- 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

- 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

We use Pinecone as a vector database for retrieval augmented generation using LLMs.

Easy to use Good documentation Easy to implement

Couldn't delete an entire vector within a namespace

Vector index storage provider. We store embedded indices on Pinecone.