Why is Pinecone Assistant so expensive? Any tips to reduce the cost?

General
1

I’ve been building an AI assistant using Pinecone and love the capabilities it offers, especially for RAG-based applications. However, the pricing for Pinecone Assistant is surprisingly high — much higher than I expected, especially for individual developers or small projects.

I understand performance comes at a cost, but it’s getting to a point where it’s hard to justify continuing with it unless I find a way to optimize or reduce the expense.

Is anyone else feeling the same?
Are there any tips, alternative setups, or best practices that could help lower the cost of using Pinecone Assistant?

Would really appreciate any advice. Thanks!

2

Welcome to the community forums @butiktakicom!

First of all, thank you for the kind words about the Assistant’s capabilities. I’m glad you like it and that it’s proven useful for your use case.

Are there any tips, alternative setups, or best practices that could help lower the cost of using Pinecone Assistant?

It’s tough to recommend something without knowing the exact use case. Are you in a position to provide details like the type and number of documents, as well as the number, frequency, and size of the requests? I’m asking because several factors contribute to the price, such as storage, underlying vector DB usage, LLM usage, infra costs, etc. So, it doesn’t have to be your data, but we need to establish a solid requirements/expectations base before comparing it to the alternatives.

3

Thanks a lot for the warm welcome and quick response!

Sure, I’d be happy to provide more context.

I’m building a web-based assistant for healthcare professionals in Turkey — specifically for private hospital managers and medical billing staff. The assistant answers questions about regulations, reimbursement rules, and contract details based on official healthcare legislation (like the SUT and related annexes).

Here are some rough figures:

Around 30-40 documents, mostly in PDF and DOCX format, totaling about 30-40 MB.

After chunking and embedding, I ended up with roughly 2500-3000 vectors.

I’m using Pinecone (Starter or Standard plan) + OpenAI for embeddings and responses.

User requests are relatively simple — mostly short queries, and I expect low concurrency, maybe a few hundred queries per week at most in the beginning.

That said, my monthly cost has been higher than expected, especially when combining Pinecone vector storage with the Assistant’s LLM-based processing. I just want to make sure I’m not overpaying for something that could be optimized.

Are there any best practices to reduce costs in this setup? Like:

Using a more storage-efficient vector DB?

Reducing redundancy in the chunks?

Running embeddings differently?

I’d also love to hear if anyone has used a hybrid setup (e.g., local embedding + external retrieval) to cut down expenses.

Thanks again!

4

why not to use Vertex AI Search of google it’s more cost-Effective for Your Scale