Pinecone Bewertungen und Produktdetails

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,

I really appreciate how Pinecone makes it easy to integrate vector search into applications. Its cloud-native setup and simple API mean I don't have to worry about infrastructure issues. Also, the performance is fantastic, even with massive amounts of data, and the low latency is a huge plus.

Being relatively new, it lacks some features and integrations compared to more established databases. And, there's a bit of a learning curve to fully leverage its capabilities. Additionally, there are some limitations regarding customization and exportability of vectors outside of Pinecone.

Semantic Search: Pinecone excels in understanding the context and meaning of queries, which is essential for accurately retrieving relevant information during meetings. Recommendation Systems: Its ability to handle complex data makes it suitable for suggesting relevant topics or actions based on the meeting's context.

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

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.

Nothing, just could release more complex document related retrieval systems.

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.

Quick to signup and implement and use it as daily basis. Performance is stable and very good.

I don't have anything bad about Pinecone.

We are building the RAG application.

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.

I recently started using Pinecone and was impressed with how user-friendly it is, especially for someone new to vector databases. Its standout feature is its focus on doing one thing exceptionally well. The documentation is clear and easy to follow, making the setup process smooth. Both indexing and query times are impressively fast, which significantly enhances efficiency. I chose Pinecone over other options because it supports larger vector sizes, a key requirement for my needs. Highly recommend Pinecone for its simplicity, speed, and capabilities.

There are a couple of areas where Pinecone could improve. First, the options for datacenter hosting are limited. For instance, if using AWS, it currently only supports the us-east-1 region, which can be restrictive. Second, the console lacks robust security measures for critical actions. Adding a Multi-Factor Authentication (MFA) verification for deleting indexes and projects would enhance security and prevent accidental data loss.

Pinecone plays a crucial role in our workflow by efficiently storing vectors from OpenAI Embeddings. This capability allows us to effectively identify and link related content across various features of our platform. The result is a more cohesive and intuitive user experience, as we can seamlessly connect relevant information and offerings. This not only enhances our platform's functionality but also significantly improves user engagement and satisfaction.

I have a Pinecone index that I've had to double in size three times now to handle the nearly 10 million vectors I have stored. Despite the increase in size, the search speed has remained constant, and upsert speed has actually increased.

This may not be unique to Pinecone, but you need to make sure you figure out your data schema up front because it requires some work to change records at scale if you want to add or modify metadata.

Fast speed and fully managed. I don't have to worry about anything other than paying the bill.

- 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.

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

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.

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.