Pinecone Vector Database - Annual Commit

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.

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.

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.

I have been using Pinecone with code for one and a half years.

Pinecone is very stable.

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.

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.

Positive

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.

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.

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.

Before choosing Pinecone, I evaluated a few options, including Weaviate.

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

What do you like best about the product?

Pinecone excels in providing a seamless, high-performance vector search experience. Its ease of use, combined with powerful features like real-time updates and scalability, makes it a go-to solution for managing complex vector data. The ability to effortlessly integrate with existing workflows and its top-notch customer support are definite highlights.

What do you dislike about the product?

While Pinecone is robust, the pricing can be a bit steep for smaller projects or startups. Additionally, more granular control over indexing options would enhance customization for advanced users. However, the benefits far outweigh these minor drawbacks.

What problems is the product solving and how is that benefiting you?

Pinecone is solving the complex challenge of efficient and scalable vector search. In an era where managing large volumes of high-dimensional data is critical, Pinecone's ability to index, search, and retrieve vectors quickly and accurately is a game-changer. For us, this means faster query responses, enhanced data retrieval accuracy, and the ability to focus on building better products rather than managing infrastructure. Pinecone's solution has drastically reduced the time and effort required to manage and search vector data, allowing our team to be more productive and innovative.

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

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.

What problems is the product solving and how is that benefiting you?

Semantic matching

What do you like best about the product?

Pinecode offers a simple API and lean management interface for a completely low maintenance vector storage and query solution.

What do you dislike about the product?

I started using Pinecone when it was new and had some rough edges. But support was proactive and smart. In the last year I can say there is nothing to not like. It has been awesome.

What problems is the product solving and how is that benefiting you?

We use Pinecone's serverless platform (on AWS) for vector search. Our vector dimension is 3072. Part of our use is user queries. The performance has been excellent and scalability is automatic. We also use the query capability in other parts of our stack where performance is not so important but reliability is a factor.

I've used Pinecone to streamline token generation for my chatbot's functionality. Specifically, I used it for the OpenNeeam Building.

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.

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.

I have used Pinecone for the past three months.

Before Pinecone, I used Corner DB.

The installation of Pinecone was straightforward.

I think Pinecone is cheaper to use than other options I've explored. However, I also remember that they offer a paid version.

I decided to use Pinecone after researching and finding it the best option for our project.

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.

Public Cloud