How CustomGPT.ai Uses Pinecone to Serve 10,000+ Customers with Sub-20ms RAG
CustomGPT.ai built a RAG-as-a-Service platform on Pinecone storing over 400M vectors, achieving sub-20ms query latency and the #1 ranking in an independent RAG accuracy benchmark.
Impact
>400M
Vectors stored
<20ms
Query latency P50
#1
RAG accuracy benchmark ranking
99.95%+
Uptime
10,000+
Paying customers
Challenge
Scaling a RAG-as-a-Service platform to thousands of customers required vector infrastructure that wouldn't distract engineers from core product development.
Solution
Adopted Pinecone as a fully managed vector database, enabling sub-20ms retrieval at scale without operational overhead.
Tools & Technologies
What Leaders Say
“Pinecone lets us focus on innovation and delivering customer value through our RAG-as-a-Service – without getting bogged down with vector database issues.”
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Full Story
CustomGPT.ai lets businesses build domain-specific AI agents using their own data, without writing code. Scaling this to thousands of paying customers required vector infrastructure that could match their product's pace — reliable, fast, and invisible to their engineering team.
Managing a vector database in-house would have meant constant infrastructure work, pulling engineers away from RAG pipeline improvements, no-code interfaces, and new integrations. Every hour spent on ops was an hour not spent on product.
CustomGPT.ai adopted Pinecone as their fully managed vector database, taking advantage of its API-first design, regional failover, and sub-second data update latency. The platform now stores over 400M vectors across 10,000+ customer accounts.
Query latency sits at under 20ms at P50. Uptime exceeds 99.95%. And in an independent RAG accuracy benchmark by Tonic.ai, CustomGPT.ai ranked #1 — a result their team attributes in part to Pinecone's retrieval quality.
AWS