It is very flexible, allowing us to input any kind of data dimensions into the platform
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
Pinecone can be made more budget-friendly.
For how long have I used the solution?
I have been using Pinecone for the past year and a half.
What do I think about the stability of the solution?
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.
What do I think about the scalability of the solution?
Pinecone is a scalable product. We can easily add users and workload without any issues.
Which solution did I use previously and why did I switch?
How was the initial setup?
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.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
On-premises
Helps retrieve data, relatively cheaper, and provides useful documentation
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Pinecone for almost one year.
What do I think about the stability of the solution?
I face some breakdowns. However, it happens rarely. Sometimes, the server crashes when we retrieve data from it.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
How was the initial setup?
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.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Offers a free version and is easy to understand and learn
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Pinecone for one to two years. I am a user of the tool.
What do I think about the scalability of the solution?
In my company, it was me who was using the product initially, after which we tried to integrate it with other tools.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
The product was installed on Pinecone's server. The product's setup phase was easy.
What's my experience with pricing, setup cost, and licensing?
I have experience with the tool's free version.
What other advice do I have?
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.
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.
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.
What problems is the product solving and how is that benefiting you?
Vector DB for multi-tenant system.