Pinecone Breaks at REAL Production Scale. We Don’t
Compared to Pinecone, Zilliz Cloud powers billion-scale production AI with AI-native search, 10× more cost efficiency, up to 3× faster performance, and 70% lower infrastructure costs. Join the innovators using the world’s most powerful vector database.
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Why Zilliz Cloud is the Top Chioce for Enterprise-Level Vector Search and AI Workloads.
The Industry Leader in scalability, speed and cost
Zilliz Cloud crushes Pinecone with 10× higher QP$, 3× faster performance, and up to 70% lower infrastructure costs — all at billion-scale.
Discover Why Zilliz LeadsDon't just take our word for it
Zilliz is named a Leader in the Forrester Wave™ Vector Database Report
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See what our customers are saying
We were running a multilingual RAG system at scale, indexing all of Wikipedia into tens of millions of high-dimensional vectors. Latency targets were tight. We needed a system that could handle real-time retrieval over millions of knowledge vectors without breaking under load. Zilliz gave us that. It freed up engineering cycles and let us focus on improving reasoning on the model side, not managing infrastructure. That reliability mattered because we were operating at 3,500+ dimensions per vector, across a fast-growing corpus, with sub-300ms latency requirements in production. "
Dr. Pratyush Kumar
Co-Founder of Sarvam
I appreciated using the open standard evaluation benchmarks for machine learning in general; this is also true for vector databases. The ones that Zilliz often publicizes have been beneficial, and the fact that they are open is significant. "
When we identify a need for specific data, we can often find the required data in our database the same day using text or image search with Milvus. This greatly improves our data processing efficiency and has a positive effect on our business operations."
Zilliz is a very integral part of our workflow. If we just swapped Zilliz Cloud with something else, the kind of loops that we get might not make sense, which means the end composition might not sound very nice or might not be very accurate to the text prompt you had given."
We've got millions of monthly active users and all of the underlying data when we're trying to go find related conversations, find updates to an action item, find referenced documents...Milvus serves as the central repository and powers our information retrieval among billions of records."
We were running a multilingual RAG system at scale, indexing all of Wikipedia into tens of millions of high-dimensional vectors. Latency targets were tight. We needed a system that could handle real-time retrieval over millions of knowledge vectors without breaking under load. Zilliz gave us that. It freed up engineering cycles and let us focus on improving reasoning on the model side, not managing infrastructure. That reliability mattered because we were operating at 3,500+ dimensions per vector, across a fast-growing corpus, with sub-300ms latency requirements in production. "
Dr. Pratyush Kumar
Co-Founder of Sarvam
I appreciated using the open standard evaluation benchmarks for machine learning in general; this is also true for vector databases. The ones that Zilliz often publicizes have been beneficial, and the fact that they are open is significant. "
