My main use case for Pinecone is creating vector indexes for GenAI applications.

A specific example of how I use Pinecone in one of my projects is utilizing a RAG pipeline where I take text from PDF documents, convert those into chunks, ingest those into the Pinecone vector database, and then have a frontend UI that uses LLMs to query the vector database and retrieve answers.

What I appreciate about Pinecone is that it provides reranking and other features, and it's a SaaS-based solution that is serverless.

Pinecone's reranking aspect works by taking a list of documents from the indexes and organizing them based on the ranking that is relevant to the question being asked by the user, ensuring that if reranking is applied, the user gets the most relevant answers as LLMs understand them, providing near-perfect answers versus when not using reranking, where the LLM takes all output from the vector index, which won't be quite that perfect.

Pinecone's serverless aspect is valuable because I don't have to manage the infrastructure myself, as Pinecone takes care of that.

Pinecone has positively impacted my organization by helping people in needle-in-a-haystack situations, as previously they had to grind through PDF documents, PowerPoint documents, and websites, but now with Pinecone, they can ask questions and receive references to documents along with the page numbers where that information exists, so they can use it as a reference or backtrack, especially for things such as FDA approvals where they can quote the exact page number from PDF documents, eliminating hallucination and providing real-time data that relies on an external vector database with enough guardrails to ensure it won't provide information not in the vector database, confining it to the information present in the indexes.

Pinecone has helped full-time employees rely less on contractors to find information, enabling them to access data at their fingertips and reducing the turnaround time to generate reports.

I give Pinecone a nine out of ten because I hope it provides an end-to-end agentic solution, but currently, it doesn't have those agentic capabilities, meaning I have to create a Streamlit application and manage it to communicate with Pinecone. If Pinecone could provide those kinds of web apps out of the box, I would give it a perfect ten.

Nothing else is needed since Pinecone provides APIs for integration, making it not a hurdle, and I am happy with what I have.

Pinecone is good as it is, but had it been on AWS infrastructure, we wouldn't experience some network lags because it's outside AWS. However, when we started two years ago, there weren't any vector databases on AWS, making Pinecone a pioneer in the field.

I have been using Pinecone for the last two years.

Pinecone is stable.

Pinecone is scalable.

I have not needed customer support yet, as everything works seamlessly.

There was no solution before Pinecone, as the vector database gained traction about two years ago, and Pinecone were the pioneers in this field, which is why we picked them.

I have seen a return on investment with Pinecone, as the application we built received positive feedback from internal stakeholders about how much it's helping them make business decisions and access information quickly at their fingertips.

The experience with pricing, setup cost, and licensing for Pinecone is not in my area, as I am a developer who uses the tools.

No other options were evaluated before choosing Pinecone.

Pinecone perfectly fits my organization's needs based on our use case. The market for vector databases is broad right now, offering many options; however, I don't have experience with other tools and technologies. I would give Pinecone a rating of nine out of ten overall.

I've used Pinecone to streamline token generation for my chatbot's functionality. Specifically, I used it for the OpenNeeam Building.

The best thing about Pinecone is its private local host feature. It displays all the maintenance parameters and lets us view the data sent to the database. We can also see the status of the CD and which application it corresponds to.

I want to suggest that Pinecone requires a login and API key, but I would prefer not to have a login system and to use the environment directly.

I have used Pinecone for the past three months.

Before Pinecone, I used Corner DB.

The installation of Pinecone was straightforward.

I think Pinecone is cheaper to use than other options I've explored. However, I also remember that they offer a paid version.

I decided to use Pinecone after researching and finding it the best option for our project.

Pinecone is easy for new users to learn, and I would rate it around eight out of ten. This is because other databases do not have a login system and are not as user-friendly.

I used Pinecone in collaboration with an Azure database. At that time, I needed to create a chatbot that could pull data from public media in specific fields. I used Pinecone to embed the publications, and after submitting the data, it was pushed into our data pipeline.

The most valuable feature of Pinecone is its managed service aspect. There are many vector databases available, but Pinecone stands out in the market. It is very flexible, allowing us to input any kind of data dimensions into the platform. This makes it easy to use for both technical and non-technical users.

Pinecone can be made more budget-friendly.

I have been using Pinecone for the past year and a half.

Pinecone is a stable product. Despite few errors, it's easy to use, especially when searching with endpoints. Compared to other databases, Pinecone is quite user-friendly.

Pinecone is a scalable product. We can easily add users and workload without any issues.

The installation, setup, and deployment of Pinecone is straightforward. We need to take a subscription from Pinecone and configure the endpoints into our applications. Before configuration, we need to install Pinecone libraries on the dev side. We put the tokens at the endpoints and connect Pinecone to our applications. After that, we push our metadata into the Pinecone endpoint database. Once the data is pushed, we can search the data we've entered. Pinecone supports various functions based on similarity and allows us to specify how many results we want, like the top five or top two results.

Pinecone is not cheap; it's actually quite expensive. We find that using Pinecone can raise our budget significantly. On the other hand, using open-source options is more budget-friendly.

We chose Pinecone because other vector databases, like ProMID or Azure, don't have UI-rich components or tools. Pinecone offers a better UI and allows us to create any kind of application and handle a large amount of data easily. It is a managed service, making it more convenient for us.

As per my advice, assess your data requirements. If you're working with PDF files and do not have much data, you could use other databases because they are similar to Pinecone. However, if you have a huge amount of data, I would suggest using Pinecone as it handles large datasets more efficiently. Pinecone offers a rich UI and managed services, making it easy to use and visualize data, which is a big advantage. However, if the client has a limited budget, I would recommend open-source models and databases instead.

I would rate Pinecone an eight out of ten because of its functionality and ease of use despite the cost.

Pinecone is a vector database. We use it to retrieve data using semantic search. We use vector DB only for chatbots and AI applications. Currently, I am using the tool to make a chatbot.

The semantic search capability is very good. We store data and embed numeric values. If I want to search for something, I get the right data 90% of the time.

Suppose I want to delete a vector from Pinecone or a multi-vector from a single document. Pinecone does not provide feedback on whether a document is deleted or not. In SQL and NoSQL databases, if we delete something, we get a response that it is deleted. The tool does not confirm whether a file is deleted or not. I have raised the issue with support.

If we have 10,000 vectors in our index and do not use a metadata tag, it will take one to three seconds to complete a search. When I try to search using a metadata tag, the speed is still the same. The search speed must be much faster because I specify which vectors I need the data from.

I have been using Pinecone for almost one year.

I face some breakdowns. However, it happens rarely. Sometimes, the server crashes when we retrieve data from it.

We have a SaaS project, and Pinecone is a database for that project. All the developers who work on the project use the solution. Currently, six to seven of us use the solution. We recently moved to serverless DB. It is easy to create metadata fields. If we have a certain template for our database, we can change the database very easily. It will not show any errors. We just have to put an extra key in the metadata fields.

I was unable to delete the data using IDs and metadata. I raised a query for it. I got the response in less than 24 hours, and it was resolved. The support team is very good. They provide quick responses.

The solution is deployed in the cloud. The tool is very easy to install. There are commands to install the tool. The product is very easy to install and integrate on our machine.

Initially, the product was expensive. My company used to pay $70 per index. Now, we can pay according to our needs. It is a pay-as-you-go model. For the same use case, we are currently paying $4. The solution is relatively cheaper than other vector DBs in the market. It is worth the money.

We also use Weaviate for some projects. It is also a vector DB. We also use an SQL database called PlanetScale. Before installing Pinecone, we compared its performance against vector databases like Weaviate and ChromaDB. Pinecone and Weaviate emerged as the top choices.

Pinecone and Weaviate are both good choices. If we want to use the solution, we must know how a vector DB works theoretically. Then, we will be able to work with it easily. If we do not know how vector DBs work, we must refer to the documents to insert and get data. Having a basic understanding of vector DBs is helpful. If a beginner goes through the documents, it is very easy to use the product.

Overall, I rate the product an eight out of ten.

The product is good. When I tried to deploy the product for the first time, I liked Pinecone's approach, and it was one of the major reasons why I decided to continue with the product.

I mostly use the solution in my company for data storage.

I think Pinecone provides good features, and I feel that the product gives out some free space during the starting stages, just like how Fortinet and some other tools do, so that users can learn to use the solution. It is a good thing that the product supports research among its users. The product also offers support, especially when they are supposed to interact with the servers of the users.

There aren't any problems with the product, and I feel it is a good solution. Users also need to consider the different sources and options in the market and, at their own discretion, should decide whether to go with Pinecone or some other solution. In Pinecone, there are a lot of changes to be made to meet your requirements. Even though Pinecone is a good tool, I haven't used it much.

For testing purposes, the product should offer support locally as it is one area where the tool has shortcomings. A person needs to learn everything and figure out how the product works. If, as users, we get to know how to use the product properly, then we can use it for our specific use cases, making the product more user-friendly for all. The product can be made more user-friendly.

I have been using Pinecone for one to two years. I am a user of the tool.

In my company, it was me who was using the product initially, after which we tried to integrate it with other tools.

My company selected another solution over Pinecone. I don't know much about Pinecone, and I don't know much about its deployment. I only know how to use the solution and interact with its UI. I don't have much information about the platform.

The product was installed on Pinecone's server. The product's setup phase was easy.

I have experience with the tool's free version.

Everything is good in the solution, including its user interface. Pinecone provides its best facilities for beginners to be able to learn the product, so I think it is an easy and good product to use.

I would recommend the product to others, and I would also suggest that it is very important to learn on how things work in Pinecone, especially areas like automation, integrations and secrets detection engine.

It is easy to learn about the product since all the information related to the solution is provided to users. Users just need to read the information provided by Pinecone and implement them.

I rate the tool an eight out of ten.