01
Serverless index sizing & cost control
Read-unit planning and index partitioning to keep vector hosting costs down.
Technologies — Databases & storage
Pinecone vector database architecture for enterprise AI
We engineer production-grade vector search and RAG retrieval pipelines powered by Pinecone's serverless vector database.
Core capabilities
Why we build with Pinecone
02
Metadata filtering & namespaces
Tenant isolation with namespaces, and metadata filters applied during the search itself.
03
Hybrid search
Combining sparse keyword vectors with dense embeddings for better relevance than either alone.
Use cases
Where Pinecone fits
Enterprise knowledge-base retrieval
RAG retrieval across large collections of corporate documents, wikis and Slack threads.
E-commerce semantic search
Visual and semantic product discovery matching natural language customer intent.
How we staff it
Pinecone engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
Pod-based indexes run on capacity you provision and pay for whether or not it is used. Serverless indexes separate storage from compute and bill for storage plus the reads and writes you make, which usually costs less for spiky or modest workloads.
Pinecone applies metadata filters as part of the search rather than after it, so filtered queries stay fast and still return the full number of results. Very selective filters are worth testing on your own data.
Ecosystem
Related technologies
Planning a Pinecone project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.