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Best and affordable vector database
What do you like best about the product?
Pinecone's new serverless pricing is very affordable for small startups. It support large embeddings size, sparse & dense embedding and fast queries. It suited my needs.
What do you dislike about the product?
It has 10,000 namespace limit on serverless instance. It should be increased.
What problems is the product solving and how is that benefiting you?
I use it to store embeddings of PDF files and then ask questions using LLM models.
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First and Last Stop for a Vector Database
What do you like best about the product?
Excellent user interface, excellent supporting materials and literature to learn, very easy to use, improving quite quickly. It is quite easy to implement it in integration with our existing workflow. I use it for all vector database operations.
What do you dislike about the product?
I have some very technical questions, like: will hybrid search ALWAYS be limited to dot product? But these are quite few.
What problems is the product solving and how is that benefiting you?
Making it easy to implement a vector database for semantic search in RAG applications
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.
Effortless Vector Storage to Give Your AI App Infinite Intelligence
What do you like best about the product?
Pinecone is great for super simple vector storage, and with the new serverless option the choice is really a no-brainer. I've been using them for over a year now in production, and their Sparse-Dense offering made a huge impact on the quality of retrieval (domain-heavy lexicon). The tutorials and content on site are both extremely well-thought out and presented, and the one or two times I reached out to support they cleared up my misunderstandings in a courteous and quick manner. But seriously, with serverless now, I'm able to offer insane features to users that were cost-prohibitive before.
What do you dislike about the product?
I can tell you what used to be challenging: which was cost monitoring and the web interface, both issues which have been drastically improved in the recent months. The web interface is still a bit cumbersome to use, but that's only because vector storage/search is not what you would expect coming from other "content" management systems. There isn't a lot of hand-holding like you might find elsewhere, but really—if you're in this space, you do have to do a lot of work on your own anyways. Hard to find something to dislike when it "just works."
What problems is the product solving and how is that benefiting you?
My app leverages decades of internal and external content around the business of writing great stories. Pinecone's vector database makes it easy to store all of this knowledge in a way that is easily and QUICKLY recovered based on semantic meaning. And now with serverless (and its wild affordability), I can now extend that knowledgebase to ALL of my user's stories and creations such that everyone can have their own expert assistant tailored to their particular style.
A fast service that allowed us to implement RAGs in a brink
What do you like best about the product?
I like their pace of innovation because they allowed us to start testing RAGs since the beginning and they have been enabling new use cases since. This is a team that grows with our platform and that keeps us up to date.
What do you dislike about the product?
One thing we had to do is add additional destinations to our internal systems, and building the syncronization flows was the most difficult part of it.
What problems is the product solving and how is that benefiting you?
Allows us to build semantic search and recommendation products.
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.
I really like the product and satisfied from the ease-of-use and performance
What do you like best about the product?
I like the ease-of-use. Super easy to build index, populate with data and test it.
What do you dislike about the product?
Some security-related features are missing.
We need VPC peering in GCP, in order to unlock deals with companies that require this feature.
Also, Serverless in GCP is missing.
We need VPC peering in GCP, in order to unlock deals with companies that require this feature.
Also, Serverless in GCP is missing.
What problems is the product solving and how is that benefiting you?
Vector DB for multi-tenant system.
Using Pinecone for Semantic Search
What do you like best about the product?
Pinecone made it easy for my team to significantly accelerate our AI services through vector search. While vector databases have become more commonplace, they continue to introduce new features to stay on the cutting edge and add support new applications. The service is easy to setup and maintain. Theirservice is faster and more stable than some open-source alternatives that we considered.
What do you dislike about the product?
While Pinecone can be hosted on both GCP and AWS, it would be great if they also suppoted Azure. We have tested both and had the highest uptime when running PineCone on AWS.
What problems is the product solving and how is that benefiting you?
We use PineCone to accelerate vector search and cachine for nearly all our AI services. It reduces both speed and cost by reducing the need to recompute embeddings,
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.
Great Results!
We recently made the switch to Pinecone database for our vector search needs, and we couldn't be happier with the results! The latencies are lower than we expected, making it a fast and reliable solution for us. Additionally, the metadata filtering works out of the box which is crucial for e-commerce. Overall, we highly recommend Pinecone to anyone in need of an efficient and user-friendly vector search solution.
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