I have used Pinecone for the last five years, when I started my career in generative AI. It is very useful for creating POCs. I created more than 15 POCs on Pinecone because it is very useful for use and implementation.

I have created many POCs using Pinecone. Let's suppose we have some documents in PDF format. We are getting the data from the text format, chunking and embedding it, and storing it in Pinecone. This is something we do in many applications, mostly in the POCs, because the client is not allowing it to be used on the production server. Mostly we are using the Oracle vector database on the production server. That is the issue from the client side.

I have not used Pinecone in my organization. In most cases, I use Pinecone for small projects as well as POCs. In the small projects, I use private servers for implementation and deployment.

I have not used large data. I use Pinecone for small projects, mostly single files. The file contains more than 100 pages, and it is performing well. There is nothing I'm seeing, such as drawbacks or lagging somewhere. It is working fine for us.

I use it mostly for AI applications, primarily in RAG applications. For the implementation, for the embedding, storing the embedding, and getting the data later, Pinecone works well.

Pinecone is very easy to use and it's very easy to make the connection. I use both cloud-based and local Pinecone, and the performance is much better as compared to other tools for embedding.

Faster retrieval and low latency are significant advantages. The results are mostly correct in most cases.

With Pinecone's features, we can use it both locally and in the cloud. It is a good feature because sometimes we are unable to install Pinecone on a local machine, so we can use the cloud. Pinecone provides credentials so we can directly connect to Pinecone using our script. It is a good feature, so I appreciate what Pinecone company has provided.

It is very fast and it saves us a lot of time for implementation.

Data privacy is important, and there are many layers of security provided by Pinecone.

Pinecone needs to be upgraded because many companies are not using Pinecone for production. I don't know why, but it is very useful for us because my team and I use Pinecone in many POCs. This is very useful for us, but on the production server, the client is not allowing us to use it.

Pinecone should be made ready for production servers. Many companies are not using Pinecone in production. I don't know the reason. We need to work on understanding why companies are not adopting it for production servers.

It would be better to provide better documentation on how to use it, and also provide some videos, because most of the time we are using videos for implementation and use. The documentation is also helpful, but videos are a good option for us.

I have used Pinecone for the last five years, when I started my career in generative AI.

Pinecone is good for POCs and small projects because it's very easy to implement and very easy to use. This is very good for us. I would rate this product a 10 out of 10.

My main use case with Pinecone involved building a RAG application where we used it for indexing. In our RAG application using Pinecone for indexing, we convert whatever text chunks are coming in into vector embeddings, which might be of different dimensions. We store these embeddings in Pinecone, and at query time, we perform a nearest neighbor search to find the most relevant text. For example, if you're asking a question about the different OPD expenses, then the embeddings most semantically similar to OPD expenses will come up. It's used for similarity search on text.

The best features that Pinecone offers include the separation via namespaces, which was really good. Another feature is the ability to deploy within different cloud servers of our choice, which was also advantageous, along with the out-of-the-box option that allows you to just use an API key and start using it without needing to set up anything.

Out of the features I mentioned, the most valuable in my workflow is the easy setup because it helps in prototyping easily. There can be different RAG use cases, and for each of these use cases, you just do not have to install a vector database or go through all of that; you can just use the API key.

Pinecone has positively impacted our organization by helping us to land a few clients or at least give a demo of how our application works, becoming a part of the solution we developed.

Pinecone can be improved by having the ability to have multi-modal embeddings out of the box as a good idea. A major reason we did not use Pinecone is that the serverless region was only in the United States; if it were available in India with serverless out-of-the-box implementation, we would have definitely used Pinecone.

Regarding needed improvements, I would like to see more regional endpoints, particularly serverless regional endpoints, as that's the most important one, along with multi-modality support.

I used the solution for about three to six months.

Pinecone is stable in my experience.

Pinecone is highly scalable compared to others, and it's out-of-the-box, although the drawback is the pricing.

Regarding how we set things up with Pinecone, we created different namespaces and within these namespaces, we created the embeddings for different catalogs.

We currently use PG vector with Postgres; it's not something I previously used, but it's beneficial because it operates on disk rather than in memory, which saves a lot of money.

Before choosing Pinecone, I evaluated options including PG vector with Postgres, Qdrant, and Milvus.

My advice for others looking into using Pinecone is to optimize all the embeddings before using it because if you are using it in a highly scalable manner, the pricing can get very high, so you have to be careful.

Pinecone was one of the earliest vector databases I came to know about, and it's the go-to option; I suggest it for anyone new to or learning about vector databases because it's very easy to start and work with without needing complex setups.

For future interviews, before asking deep questions, it's good to clarify things first because there is focus on cloud deployments, but we did not dive deeply into that; we only used Pinecone during the proof of concept and had to switch back due to data residency issues. I rate this product an 8 out of 10.

We are using Pinecone because we have a good amount of documents which we use on a daily basis from our vendors. Based on those documents, we need to provide information to the end customer for that particular company. We have a UI where customers can ask any questions related to anything in the financial domain. We need to provide the latest information, so we are dynamically doing the chunking with the help of an OpenAI LLM model and then inserting into Pinecone. In Pinecone, we are using a very high dimension vector space, almost more than 3K dimension size. We then perform similarity search and provide the final response to the UI. For our RAG system implementation, we are using Pinecone.

There are multiple factors that impressed me while using Pinecone. I had an option to use Milvus as well, but I preferred Pinecone. The first is the UI. Pinecone's UI is really strong. If I need to do some debugging on the backend side, I simply log into the UI and can perform operations based on my demand. This is a valuable UI feature.

Second is the scalability option. I can either define my own workers or use the auto-scaling feature. From an enterprise application and scalability perspective, this is very useful. We had an incident during a Black Friday sale and other occasional events that directly impacted our product traffic. Because we selected the auto-scaling feature in Pinecone, it automatically handled all the traffic spikes and we did not face any performance issues.

Ease of troubleshooting is another valuable feature. If any transaction fails and we need to check and debug each transaction, we can perform a text search on the UI. Based on the text search, we get all the related vectors on the UI. The UI definitely helps us from a troubleshooting perspective. Selecting the infrastructure is also an important option. I can create multiple indexes based on demand so that it will not become messy for our enterprise application. Pinecone is the backbone of the entire system, helping us with cost and time savings.

I have a suggestion to expand the vector size in Pinecone. Right now, whatever the limit Pinecone is suggesting, I would recommend increasing that option. Currently, I believe it is around 3K, but if I want to go to 4K, 5K, or something higher, that would be beneficial. Embedding models are coming into the market and they are providing good amounts of vector sizes. Try to encapsulate all these features into Pinecone.

I have two main suggestions from my side. One is to increase the vector size. Currently, it is supporting only around 3K vector size, and I would recommend increasing that. The second suggestion involves creating a feature similar to LangSmith, which is a monitoring tool. In LangSmith, end-to-end API calls can be analyzed, showing what request came from the customer, what vector search was performed, what prompt was created, what call was given to the LLM, and what response was received from the LLM to the UI. The whole journey can be captured. I would appreciate if Pinecone could provide this capability from their side. I understand that Pinecone cannot capture the LLM call and everything. However, if it is possible, I could use the API key of Pinecone in my code where I can enable these feature logs and see all these things on the Pinecone dashboard.

The major improvement I am expecting from Pinecone is increased vector size. The second improvement would be to provide end-to-end debugging or the whole end-to-end call journey as a GenAI product, showing how the end-to-end journey works for a single request. If I am able to see the whole process on the Pinecone dashboard, it would be really valuable.

I am using Pinecone as an enterprise application in my organization for almost three years.

I do not have specific metrics, but I can give some high-level approximations. The task that was happening before developing this product was taking around one hour, but now it is done in hardly one or two minutes. So from 60 minutes to one or two minutes, you can assume how much cost savings we are achieving. Additionally, we are engaging customers much better through the UI. Previously, customers used to wait for 60 minutes. Now they get results within one or two minutes. We are definitely increasing our customer database.

Milvus is another contender that we considered while deciding on a vector database. I suggested Pinecone because of its good amount of quality compared to Milvus. However, Milvus has the capability to handle any vector size, which is missing in Pinecone.

The main use case for Pinecone is to build RAG applications, but I have also built an image search engine on Pinecone by storing image embeddings and searching those image embeddings on it.

The first important thing about Pinecone is that it's a managed vector database, so there is no DevOps involved; it handles scaling, backups, replicas, and other infrastructure concerns, which is really helpful to me.

The best outcome of using Pinecone is that we don't have to manage one more application or one more thing in the overall application architecture because the vector database is the heart of any AI agent. When it's on Pinecone, we are safe and we don't have to worry about it; we can just use it via API and that's done.

In terms of time saved with Pinecone, it's really a time-saving solution because we don't have to manage the infrastructure. It streamlines our workflow and helps us create a proof of concept much faster because it becomes very easy to interact with Pinecone. It's really helpful, time-saving, and a faster way to build AI applications.

Pinecone has capabilities way beyond RAG applications because it can be used for recommendation systems, image similarity, and audio similarity as well, so it would be best if they could market those capabilities as well.

If Pinecone could increase the free quota and not kill the free quota after seven days, that would be great.

I have been using Pinecone for three years and have been building RAG applications on top of it.

Pinecone is very stable.

Pinecone scales well.

I have not needed the customer support for Pinecone yet.

We were using a file-based vector database, but as I mentioned, it's not a good solution beyond a proof of concept. When scaling beyond proof of concept, it's not a viable solution, so we were forced to switch to a platform, and we found Pinecone very easy to use.

The pricing for Pinecone is fair, and setup is really easy. You just give an index name and a couple of other things such as the dimension you want to have, and then you are good to go with no hassle.

As I mentioned earlier, time is saved with Pinecone. Money-wise, I'm not certain, but on the employee side, fewer employees are needed. DevOps is relieved because they don't have to manage a vector database and security and all the things related to the vector database.

We evaluated Quadrant, but the managed version of Quadrant is not as robust as Pinecone, so we moved to Pinecone.

If I want to use any file-based vector database, it becomes really not possible to use because it cannot scale. You cannot connect or create multiple replicas on top of a single file-based vector database. In the context of managed instances, Pinecone comes to us very easily and it becomes very easy to scale workers on top of Pinecone.

Pinecone is a great platform; it's easy to use with clean SDKs, so it becomes always a go-to option when I think of a vector database.

One piece of advice I would like to give about Pinecone is to make sure you first clearly discuss what embedding size you want because it's not possible to change the embedding size after setup.

I would rate this review a ten out of ten.

I have been using Pinecone for two years, starting with agents and RAG models. My main use case for Pinecone is to build a RAG model to create chatbots for enterprise.

We created a chatbot and used Pinecone for storing the embeddings generated to create that RAG model. This chatbot helps people understand more about their documentation. Users can ask queries, and it retrieves the nearest vector embedding, passes it to the LLM, and comes back with the nearest possible available context.

Primarily, I am using Pinecone for chatbots only. However, there are some additional use cases. Pinecone helps with RAG, semantic search, and a combination of hybrid search with hybrid vector search plus semantic search. Some of my teammates are also using it for creating recommendation systems for our customers.

Pinecone offers fully managed infrastructure, so there is no need to manage servers, sharding, indexing, or scaling, which reduces DevOps overhead significantly. It has high performance and low latency.

Pinecone's high performance and low latency have made a difference for my team since I am able to drastically reduce the retrieval time. It provides millisecond-level similarity search across billions or millions of vectors and uses optimized approximate nearest neighbor algorithms to provide the results, which really reduces the overall response time.

The developer experience with Pinecone is also good, with very clear, well-maintained documentation and minimal setup required, and it is perfectly built for handling AI use cases.

Pinecone has positively impacted my organization by helping us build those RAG models. Those chatbots help because earlier the users and specialists used to go to the documentation and refer to it manually, but with Pinecone integration retrieval model, I am able to ask queries to the chatbot, and it provides the appropriate context text along with citations. This helps organizations transition from keyword-based systems to semantic systems.

Pinecone is not open-source. The cost can escalate based on the pay-as-you-go pricing, so when there are high volume large embeddings, the cost would automatically rise. Additionally, there is no on-premises application available; it is only cloud-based, which becomes a problem for industries that are highly regulated. Since it is into vector, there is no particular conversion for joins and structured queries, which becomes a problem. A system that could automatically convert into structured SQL queries would help increase overall acceptance.

I have been using Pinecone for two years, starting when I began working with agents and RAG models.

I have not faced any issues with Pinecone; the reliability factor is there. It is able to withstand the enormous data load and manage it effectively. Till now, I have not experienced any downtime. Pinecone is stable.

Pinecone handles scaling as my data grows by providing good response time even though I have enormous amounts of data. It uses horizontal scaling, which helps, and it also does automatic sharding; it splits vector data into shards, and each shard can be independently indexed and queried, helping with parallel query execution. I would rate Pinecone's scalability an eight or nine out of ten.

Pinecone's customer support is good. I would rate the customer support a nine out of ten.

I did not use a different solution before Pinecone; I started with Pinecone after getting reviews from Trustpilot and G2. I understood that it is designed to be very easy to use compared to FAISS and Weaviate.

The integration of Pinecone with my existing tech stack was a very good experience. The developer documents were up to the mark, clearly documented, and it exposes clean REST and SDK interfaces. The core operations of creating an index, upserting a vector, or querying a vector are minimal, making it a plug-and-play experience with the LLM ecosystem. It works seamlessly with LangChain, LlamaIndex, and other embeddings.

Overall, the time to go through the documentation has drastically reduced. I have achieved a 30 to 40% reduction in time to go through the documentation because now I can ask a query from the chatbot, and it provides the result with the appropriate source link. Pinecone helps me save about two to three hours daily because of the manual effort required to go through the documentation. Now it is fast; at my fingertips, I can get any information, allowing me to go through that guideline.

Pricing was handled by the procurement team, but it follows a usage-based pricing model, and I have to pay for storage, read operations, and write operations. Sometimes it just exceeds, so having a quota or limit would help.

I evaluated FAISS and Weaviate before choosing Pinecone.

If you are looking for a highly scalable, performance-oriented, highly reliable system, go for Pinecone. It is especially designed for handling AI use cases. I would give Pinecone a rating of seven out of ten.

I feel Pinecone is secure for most enterprise use cases, with strong controls around data isolation, encryption, and access management. It uses HTTPS and TLS encryption to protect data during API calls, and the data at rest is also encrypted. It follows the multi-tenant isolation model, which is managed through indexes on namespaces. The security posture is at the highest level, which is what I need from an enterprise point of view. The documentation is top-notch; it is highly quality, developer-friendly, and production-oriented, especially for different use cases like RAG and semantic search. It is designed to get teams from zero to working systems quickly, with clear starting guides, impact explanation, and strong code examples provided.