Pinecone Pricing Overview

Pinecone has helped our company, fevr.io, scale our semantic chat functionality across three key regional markets. The responsiveness and ease of implementation has been a huge plus for our developers. The documentation has been very helpful as well, especially in terms of integrations with products like OpenAI and Langchain. Add to that, the customer support has been tremendously useful.

While not necessarily a negative feedback, having even more research data on how different dimensions and pods affect various responses would be a helpful resource to have as a reference.

Storing embeddings of documents is quite costly and difficult to manage. Pinecone solves this with solutions that are easy to implement with OpenAI's API. It allows for rapid prototyping of custom chat models.

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.

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

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.

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.

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,

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.

We did a lot of research on vector databases at Refsee.com and tried many things: embedded db into the docker image served at AWS Lambda (believe me, that's not what you want), Milvus, Pinecone etc. We always had problems and necessity of extra tuning before, both with self-hosted OSS dbs and managed ones, but Pinecone really did the trick! It just works!

As usual, if you choose managed solution you get a vendor lock. Probably can be costly if you scale and no option for on-prem installation

We do vector search over our own datasets – basically a "google images" on our own data

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.