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.
Ask us about VPC deployments on AWS or GCP.
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.
Use cases for similarity search
Search for most-liked content or products by similar customers.
Index image catalogs and search for similar or related images.
Search for the most similar questions and their answers.
Personalize experiences most similar to a user's preferences.
Index audio catalogs and search for similar or related audio.
Find nearly identical records to any specific item.
Index documents and search for semantically similar content.
Classify or label items based on similar, already-classified items.
Compare a user's behavior with fraudulent patterns.
Index events, then check for dis-similarity with expected events.
Index candidates and search for closest matches to some profile.