Managed Vector Similarity Search
Search through billions of vector embeddings for similar matches, in milliseconds.
It’s the next generation of search, an API call away.
Want a demo or have questions? Contact us.
Vector similarity search use cases
Launch a distributed similarity search service in a few lines of code.
Create a service and start making API calls — leave the infrastructure and ops to us.
Each service is persistent, consistent, sharded, and replicated across many nodes.
Dynamically load and index billions of vector embeddings.
Load vector embeddings in streams or batches from models, data lakes, or feature stores.
Update from anywhere. New and updated items become searchable in milliseconds.
Run similarity search in Python or Java applications, or notebooks.
Latency of <50ms, even with billions of items and thousands of queries per second.
Our fast search algorithms find more accurate results than open-source options.
Pinecone runs on hardened AWS infrastructure. Data is stored in isolated containers and encrypted in transit.
Data persistence, eventual consistency, automatic node recovery, replication, sharding, and more. All done for you.
Only pay for what you use, as you go. The efficiency of our vector index leads to lower operating costs.
Ask us about VPC deployments on AWS or GCP.