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.
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
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
Using Pinecone on production - 1 year later
What do you like best about the product?
Pinecone was our primary choice and we have not considered changing since.
- High performance (upsert and search in the ms)
- Simple integration via API and deployment and now after their recent release of serverless indexes it's very simple to maintain and scale (it's autoscaling).
- Low price (relative to the number of vectors) and free limited indexes. Free indexes are great to run development environment data. For a while it was impossible to upgrade a free index to a paying one, but this is now addressed.
- Incredible support (we had an issue and was not expecting getting this quality of support without paying the usual business support fees of an AWS for example)
- The ability to assign metadata is very useful (we still maintain a traditional db to keep track of the vectors)
- The single stage query vector/metadata is very useful and saves the headache of over-querying
- One feature we have meant to use is the use of sparse vectors in combination with the dense vectors. So, can't really comment yet
- High performance (upsert and search in the ms)
- Simple integration via API and deployment and now after their recent release of serverless indexes it's very simple to maintain and scale (it's autoscaling).
- Low price (relative to the number of vectors) and free limited indexes. Free indexes are great to run development environment data. For a while it was impossible to upgrade a free index to a paying one, but this is now addressed.
- Incredible support (we had an issue and was not expecting getting this quality of support without paying the usual business support fees of an AWS for example)
- The ability to assign metadata is very useful (we still maintain a traditional db to keep track of the vectors)
- The single stage query vector/metadata is very useful and saves the headache of over-querying
- One feature we have meant to use is the use of sparse vectors in combination with the dense vectors. So, can't really comment yet
What do you dislike about the product?
Love most of it as is
- The documentation using metadata and single stage queries is a bit light
- They have a smart bot to help answer support questions. On the great side, it seems they use their own technology for RAG type of application, but on the other it often misses the mark. ChatGPT or Perplexity are surprisingly more effective.
- There has been a few down times, but they are very communicative about them and maintain a server health page for each endpoint. It's usually related to a specific infrastructure (AWS or GCP) they run on.
- They have been growing and improving the technology, and like with other player, sometimes to update their python library or the way to reference to the indexes. But each time it's been toward simplification, and I suspect it will stabilize.
- The documentation using metadata and single stage queries is a bit light
- They have a smart bot to help answer support questions. On the great side, it seems they use their own technology for RAG type of application, but on the other it often misses the mark. ChatGPT or Perplexity are surprisingly more effective.
- There has been a few down times, but they are very communicative about them and maintain a server health page for each endpoint. It's usually related to a specific infrastructure (AWS or GCP) they run on.
- They have been growing and improving the technology, and like with other player, sometimes to update their python library or the way to reference to the indexes. But each time it's been toward simplification, and I suspect it will stabilize.
What problems is the product solving and how is that benefiting you?
Semantic matching
Provides a private local host feature and is easy for new users to learn
What is our primary use case?
I've used Pinecone to streamline token generation for my chatbot's functionality. Specifically, I used it for the OpenNeeam Building.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have used Pinecone for the past three months.
Which solution did I use previously and why did I switch?
Before Pinecone, I used Corner DB.
How was the initial setup?
The installation of Pinecone was straightforward.
What's my experience with pricing, setup cost, and licensing?
I think Pinecone is cheaper to use than other options I've explored. However, I also remember that they offer a paid version.
Which other solutions did I evaluate?
I decided to use Pinecone after researching and finding it the best option for our project.
What other advice do I have?
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.
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.
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.
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