Pinecone

Founded by Edo Liberty in 2019

Store and search vector embeddings at scale for retrieval-augmented generation applications

Pricing model

Free Tier, Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Pinecone to store, index, and search high-dimensional vector embeddings for artificial intelligence applications. The system lets developers connect unstructured data to large language models through retrieval-augmented generation without managing underlying database infrastructure. You can create indexes across multiple cloud providers, upsert millions of data records with metadata, and execute similarity searches with low latency. Pinecone automatically manages index scaling and memory allocation, while providing monitoring dashboards to inspect query latencies, read units, and record counts across multiple project namespaces.

What Pinecone does

Create and configure vector indexes with custom dimensionsUpsert vector embeddings and associated metadata recordsRun low-latency similarity queries against indexed vectorsFilter query results by namespace and metadata tagsMonitor read and write throughput metrics via web consoleGenerate API keys for secure application accessInspect query latency percentiles across production workloadsSub-100 millisecond write acknowledgments for streaming vectorsAutomatic index scaling and background algorithm adjustmentsParallel similarity search execution across high-volume datasetsMetadata filtering combined with dense vector queriesConsole dashboard for monitoring request latency and read unitsRole-based access control and enterprise data encryptionDirect integrations with AI developer tools and code editors