God of creating embeddings
What do you like best about the product?
when iam creating embeddings,compared to other products,it feels hassle free& cheap.
What do you dislike about the product?
I am the beta tester of pinecone AI assiatant,it is not production ready so it feels like only for testing,i am expecting for the production ready version.
What problems is the product solving and how is that benefiting you?
hassle free functions and embeddings data sets
quite good and easy to implement.
What do you like best about the product?
it is good in search of similarity. also managing vectors.
What do you dislike about the product?
i had difficulty to manage metadata for my vectors.
What problems is the product solving and how is that benefiting you?
we are storing vetors pf our data into the pine cone. so previously we were using sql to store cobntents. now by using the pinecone we can easily extracts soimilar content throughout the applications.
A great option for Vector databases
What do you like best about the product?
The ease of use to get integrated with Pinecone was pretty incredible. We were up and running with a vector database in no time.
What do you dislike about the product?
At first, the UI lacked some features that seemed like a must, but they've added a lot of what we were looking for and seem to be actively developing it.
What problems is the product solving and how is that benefiting you?
To perform semantic search on our documents.
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.
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
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
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.
Fast VectorDB and Easy to Use
We are using pinecone since a couple of months, we found its fast, easy to use, it has great client library. Ingestion and retrieval speed is great. Collections are great way to backup vector dbs .
Easy, powerful and so far unique
We've been using Pinecone as primary store for sparse / dense vectors in several business cases since some months.
We also did a direct comparison with other solutions but so far Pinecone stands as our selection of choice.
PROs
- Cloud-base managed "as a service" solution: zero installation/maintenance effort
- Easy to implement thanks to outstanding documentation
- Best in class performances
- So far "unique" hybrid search proposition (dense=semantic and sparse=keyword vectors support)
CONs
- Management tools still not mature/complete and evolving
- Basic access control / profiling mechanisms
- Basic logging tools
- Base price for smallest index still a little high for small business cases
Generally speaking you cannot expect to find in a vector DB (which is a quite recent class of tools) the same maturity of a RDBMS, in terms of completeness of the solutions and availability of tools.
Said that, Pinecone sticks to its core job and does it smoothly, no complains so far from our side.
A pleasant plus is the huge and very comprehensive documentation available, which covers a lot of background theory as well.
We are not giving the 5th star since the product is still young, but the direction it the correct one!
Nice and easy to use
I am using Pinecone for my LLM-based app and switching to the Pay As You Go worked seamlessly. It's actually cheaper than hosting the solution.
