We're using Pinecone to build our RAG pipeline. We need a vector database, and we have a lot of options in the market. RAG is the biggest use case for us.
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RAG workflows have become cost‑efficient and integrate seamlessly with existing cloud tools
What is our primary use case?
What is most valuable?
The first thing is that we've always been using AWS. AWS provides OpenSearch serverless out of the box, but OpenSearch happens to be pretty expensive because you have to pay per hour of use if you want to have an OpenSearch server alive. It's billed as the number of OCUs. Pinecone, on the other hand, is pay-as-you-go on the number of queries. You only pay for the queries that you hit.
Pinecone's integration with AWS was seamless. All we had to do was take one of the API keys and upload it to AWS's Key Management Service, and then configure that through it, and then it starts working seamlessly. When you're building a production system for RAG, Pinecone gives you the vector search, but you still have a lot of pieces that have to come with it, including embeddings, chunking, pre-processing the query, and security. Pinecone doesn't provide that out of the box. AWS has the infrastructure for it. When you're using Bedrock with Pinecone, it becomes a good combination because Bedrock itself is free. They only ask you to pay for the model invocations.
Pinecone is flexible. They give you a bunch of options. One of the good features is that they also provide embeddings within Pinecone, which is a neat feature. You can essentially choose your embedding sizes and things like that. So you do have some control over it. It's easy to set up, and we felt like it's not that expensive for us in comparison to serverless. That's why we took it.
What needs improvement?
If Pinecone gave us RAG as a service, we'd be more than happy to use that. Then we wouldn't have to go to something like AWS again.
For how long have I used the solution?
We've been using Pinecone for a little over four months.
What do I think about the scalability of the solution?
So far we haven't scaled it to that extent. We're just building a beta version of it. For the beta version, at least so far, it's been good. We're demoing this to a few people, and then we'll possibly scale up if needed. But so far, it's looking good.
We've rolled out the early version as a beta access to a few, maybe twenty to thirty customers. So far, there haven't been that many complaints, but also it hasn't been really stress-tested for say, ten thousand requests per minute or something like that. We haven't really put it to the test. But for these demos for our clients to use, it's working fine so far.
How are customer service and support?
I have not personally engaged with customer service, as there are people above me who are making those decisions. I work as a developer and am just integrating everything. I haven't needed support because the documentation is good enough to help developers get up to speed.
The documentation is great. Plus, they have a chatbot that can help you answer all the questions about documentation, which I find helpful. I would say it's even better than AWS's documentation because AWS's SDK documentation is just not as helpful.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We weren't really sure about Pinecone security, and that's why we're using AWS for it. AWS is going to handle that whole pipeline of security and making sure that everything is passing through correctly. Pinecone comes in at just one of the stages, where it has to either at inference give you the most similar vectors or store your embedded chunks into a vector database. It's just one small piece in this. Most of the heavy lifting is done by our back-end plus AWS.
We were also using S3 Vectors, but it's still in preview. They haven't released it for all regions. It works in the US East, but in Europe West, it's not live yet. So we weren't able to go ahead with S3 Vectors. Pinecone was available though, and that's what we're using right now.
How was the initial setup?
We're using Pinecone as a vector database over OpenSearch.
What about the implementation team?
We're in education.
What other advice do I have?
As a standalone vector database, I think Pinecone gets the job done. I would give it an eight out of ten. Overall, I rate this product an eight.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Effortless Integration and Fast Queries with Pincone
What do you like best about the product?
The service is self-managed by Pincone, so there is no need for separate billing; it can be handled directly through your cloud service provider, such as the AWS Marketplace. Defining and creating a vector instance according to the dimensions and parameters of your embedding models is straightforward. I found it quite simple to integrate with both AWS Bedrock and GCP Vertex AI services. In my experience, querying data is faster compared to other services I have used so far. This service is in our daily use as a backbone for our AI services.
What do you dislike about the product?
If you are using the trial version, you are required to create your instance in the US only. However, since I work in banking, this presents a compliance issue regarding data location. They should offer trial access in other countries as well, or consider implementing different limitations instead of restricting by region.
What problems is the product solving and how is that benefiting you?
We needed to implement a vector database for our question-answer RAG system, as well as for generating Credit Access Memos. At first, we used AWS OpenSearch, but found it to be very expensive. To cut costs, we switched to the Pinecone vector database for storing our documents.
RAG workflows have transformed document research and now provide precise answers with citations
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Pinecone for the last two years.
What do I think about the stability of the solution?
Pinecone is stable.
What do I think about the scalability of the solution?
Pinecone is scalable.
How are customer service and support?
I have not needed customer support yet, as everything works seamlessly.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
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.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
The experience with pricing, setup cost, and licensing for Pinecone is not in my area, as I am a developer who uses the tools.
Which other solutions did I evaluate?
No other options were evaluated before choosing Pinecone.
What other advice do I have?
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.
Faced challenges with metadata filtering but have achieved reliable long-term memory for chat applications
What is our primary use case?
My main use case for Pinecone involves storage of chat data, specifically chat transcripts, and retrieval of matched chat messages.
We store chat transcripts as vectors in Pinecone. When we have a new chat message, we utilize a retrieval mechanism to match and find the last five messages so that it can act as a memory. Essentially, Pinecone serves as a long-term memory for our application, while we use Redis for our short-term memory.
What is most valuable?
We were looking at multiple options for a vector database, and we found Pinecone to be the easiest to integrate into our solution. Plus, it has a very generous free tier, which helps us as a startup.
The best features Pinecone offers are quick setup and good indexing for us. The retrieval mechanisms are fast, and the integration with Python as with JavaScript and TypeScript libraries that Pinecone provides are very robust. Authentication is also very good.
The namespaces feature allows us to break down or store data for each user separately, reducing interference and maintaining privacy as an important feature.
Pinecone has positively impacted our organization by enhancing efficiency for the team, and the long-term effect has been that the chats have become much more personalized due to the memory added through a vector database. We are seeing that the trainees getting trained on the platform are more satisfied with the results or messages generated by AI.
What needs improvement?
One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata. This can cause problems because while vector indexing or vector search is good, if you populate certain categories of messages or metadata into a vector database, searching through the data using the filter of metadata is not possible.
For our requirements, Pinecone is more than enough. If improvements are required, I would suggest taking a look at the embeddings and possibly improving the embedding sizes.
For how long have I used the solution?
I have been using Pinecone with code for one and a half years.
What do I think about the stability of the solution?
Pinecone is very stable.
What do I think about the scalability of the solution?
Pinecone's scalability is pretty decent for us, as we have not encountered issues. We are storing close to around 600K items or entries in the database, and our indexing and retrievals are within seconds, often in microseconds.
How are customer service and support?
The customer support of Pinecone is very good; you send an email and receive a response within a few hours, typically four to five hours. Additionally, you can set up a call if needed.
Since we are on the minimal plan, I would rate the customer support around 8 out of 10.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We previously tried setting up with Weaviate and another solution. During my research, we checked out a couple of options, including an on-prem solution that I tried to set up on my machine, but it was very painful, so we went with the cloud service provider because the setup was nearly nonexistent.
How was the initial setup?
The setup cost for us is nil, and the licensing and pricing are pretty decent. Pinecone works on the storage amount, so our bills are pretty low, and we are good.
What's my experience with pricing, setup cost, and licensing?
The setup cost for us is nil, and the licensing and pricing are pretty decent. Pinecone works on the storage amount, so our bills are pretty low, and we are good.
Which other solutions did I evaluate?
Before choosing Pinecone, I evaluated a few options, including Weaviate.
What other advice do I have?
I would suggest that Pinecone is one of the best options available. I would rank it in the top three for vector databases and qualify it as number one in the market. There are many others such as Weaviate and Milvus, but they come with certain issues such as lacking a free tier or having a very low one.
Moreover, solutions like Milvus and FAISS are on-prem, which makes setup and stability a pain, primarily catering to big enterprises. For startups, Pinecone is indeed the best.
We are just a client of Pinecone; we do not have any other business relationship.
Rating: 4/5
Nice vector db easy to use
What do you like best about the product?
its provide various of features and great vector db support
What do you dislike about the product?
may be it is close source and needed some features which are not there yet.
What problems is the product solving and how is that benefiting you?
The latency is very minimal and provide large search/retrieval with fully managed serverless infrastructure
complicated set-up
When I tried to use the Pinecone standard plan connected with AWS Marketplace, the setup process looped between Pinecone and AWS Marketplace. I am unable to start a standard plan. It still showing current plan as starter eventhough the pinecone documents says AWS Marketplace don't support it.
Apart from the chatbot, there is no help from the pinecone side. There has been no response to my sales query also.
ideal for machine learning, AI applications and similarity search
What do you like best about the product?
It is specialised in AI driven use cases with real time and low latency search giving seamless integration into machine learning workflows with scalable infrastruture optimized for unstructured and semi-structured data in AI applications.
What do you dislike about the product?
It has limited focus that is related only with the vector data with no major focus on Business intelligence in data transformation tool.
Also it's use case is little complex with lack of ecosystem integration.
Also it's use case is little complex with lack of ecosystem integration.
What problems is the product solving and how is that benefiting you?
It is solving the issue related with AI vector data generated from the app.
God of creating embeddings
What do you like best about the product?
when iam creating embeddings,compared to other products,it feels hassle free& cheap.
What do you dislike about the product?
I am the beta tester of pinecone AI assiatant,it is not production ready so it feels like only for testing,i am expecting for the production ready version.
What problems is the product solving and how is that benefiting you?
hassle free functions and embeddings data sets
Pinecone assistant beta user
What do you like best about the product?
I have been using pinecone for embeddings and it is cheaper and reliable compared to other embedding services.
What do you dislike about the product?
I dislike the overall feel which feels lightweighed for the product service documentation.
I love to see pinecone assistant in deployable version because it is powerful yet it is in the beta version only for testing not for production
I love to see pinecone assistant in deployable version because it is powerful yet it is in the beta version only for testing not for production
What problems is the product solving and how is that benefiting you?
Creating embeddings at ease without any big pricing.
Good support from team.
Good support from team.
Solid option for vector DB
What do you like best about the product?
Easy to use. very reliable and fast. Competitive price
What do you dislike about the product?
Maybe some extra features would be nice, and some more clarity into its AKNN algo, which is hidden from the user
What problems is the product solving and how is that benefiting you?
Finding scientific documents in very large volumes of Data.
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