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.
A reliable cloud solution for building an ERP dashboard
What is our primary use case?
We've been building an ERP dashboard using generative UI. We needed a vector database to retrieve and implement augmentation, so we opted to use Pinecone.
We chose Pinecone because it covers most of the use cases. Also, Pinecone is stable and reliable.
How has it helped my organization?
We are using Pinecone for retrieval. Pinecone did a really great job in marketing and perfecting its adoption. That was very helpful because we could find resources if we got stuck on a problem. The only reason we are not using Quadrant, despite its promising features and reliable performance, is its limited resources. Pinecone community has been around for a lot longer than the Quadrant community.
We chose Python so that any new feature we could add could be implemented easily. Since Python has been around for a while, plenty of options are available. Some tutorials and resources, such as blog posts, provide references for implementing new features. We haven't utilized anything specific to Pinecone that only Pinecone offers.
What is most valuable?
The tool collects data, adds it to the database, and retrieves it using its SDK.
What needs improvement?
Onboarding could be better and smoother. Navigation is difficult because most of us rely on watching tutorials on YouTube to understand how to use this software. The onboarding journey should explain more topics.
For how long have I used the solution?
We are currently using it.
What do I think about the stability of the solution?
The solution is stable. We use it for enterprise purposes. It's reliable for our use case. We haven't experienced any downtime or significant latency issues.
What do I think about the scalability of the solution?
We use the tool for a single project with 10-15 people.
Which solution did I use previously and why did I switch?
We worked with PG vector. We were using Sophos and a free directory plugin. We used it for testing and building the prototype. When I built it, we opted for more widely adopted services. We chose a database geared towards storing and retrieving active data, especially in augmenting generation.
How was the initial setup?
The initial setup is great.
What was our ROI?
The scope of the project was really small to opt for positives with the PG vector plug-in. We opted for Pinecone since it is popular, and has a better use case.
What other advice do I have?
The main issue arises when our team members join, and we must guide them, especially those unfamiliar with Pinecone. We assign them a small project to explore the software independently. This helps them overcome any hurdles and gain a deeper understanding of how to utilize Pinecone effectively. However, despite its overall positive aspects, there's room for improvement, particularly in making it more minimalistic and simplifying access to various options. Like many SaaS products, setting it up can be time-consuming. It should provide clear instructions or a step-by-step guide for undertaking small projects independently.
Real-time data retrieval is good. However, it used to drop in a while. Overall, it was reliable.
We don't require a lot of maintenance on the project. It's a small-scale project, and the scope is specific and small. There haven't been any issues. Two to Three people are enough for the solution's maintenance.
I recommend the solution and advise you to explore the documentation and tutorials. It's easy to pick up and integrate.
Overall, I rate the solution an eight out of ten.
A tool that offers its users multiple search options for retrieval purposes
What is our primary use case?
In my company, we store our industry documents in Pinecone. My company stores the PDF files in Pinecone to use for the RAG application.
What is most valuable?
The most valuable features of the solution are similarity search and maximal marginal relevance search for retrieval purposes.
What needs improvement?
The product fails to offer a serverless type of storage capacity. From an improvement perspective, the storage capacity of the tool should not be pod-based.
For how long have I used the solution?
I have been using Pinecone for years. I am an end user of the solution.
What do I think about the stability of the solution?
It is a stable solution. Stability-wise, I rate the solution an eight out of ten.
What do I think about the scalability of the solution?
Around four or five people in my company use the product.
The solution is used on a daily basis in my company.
Which solution did I use previously and why did I switch?
I don't have any previous experience with any other solutions other than Pinecone.
How was the initial setup?
The solution is deployed on an on-premises model.
The solution can be deployed in a day.
Which other solutions did I evaluate?
My company is currently evaluating Elasticsearch against Pinecone.
What other advice do I have?
My company has integrated Pinecone into our machine-learning workflow by using LangChain. My company also uses an OCR feature to detect PDF files, which we added to Pinecone.
A chatbot application is the specific AI application for which Pinecone is used in our organization since it provides us with a source of knowledge through RAG.
I am unsure if Pinecone's similar search capabilities have enhanced our data analysis since my company is still in the middle of the tool's production phase.
If I measure Pinecone's impact on our company's system performance and scalability, I would rate it at an eight on a scale of one to ten.
I rate the overall tool an eight out of ten.
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.
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.
