Pinecone

Pinecone is a managed, serverless vector database for developers building semantic search, RAG and AI agents, with hosted embedding and reranking models.

Pinecone gives AI applications somewhere to keep their embeddings and to pull back the closest matches when a query arrives. It runs as a managed cloud service, so a team calls an API instead of operating database servers. Pinecone Systems, Inc., founded in 2019 by Edo Liberty and based in New York, builds and operates it. The database is the core of a wider knowledge platform sold under the same name. Pinecone Database holds dense, sparse and full-text indexes on an architecture backed by object storage. Namespaces split one index by tenant, metadata filters apply inside the search itself, and hybrid search blends keyword and semantic signals in one query. Document-schema indexes keep several dense and sparse fields on a single record, with the ranking signal picked per query. Pinecone Inference hosts embedding and reranking models, so plain text is embedded on write and at search time and a reranker reorders the top candidates. Pinecone Assistant handles chunking, embedding and retrieval for answers drawn from uploaded files, while Dedicated Read Nodes add provisioned shards and replicas, and Pinecone Nexus prepares knowledge for agents whenever data changes. The platform suits developers building retrieval-augmented generation, semantic search or recommendation features who would rather not size and run a vector cluster. SaaS companies use namespaces to keep each customer's data apart inside one index. Larger engineering organisations get SAML single sign-on, SCIM, audit logs, private endpoints and customer-managed encryption keys for production work. Teams under SOC 2, ISO 27001 or HIPAA obligations are covered, and those whose data must stay in their own cloud account can run managed Pinecone there through Bring Your Own Cloud. Buyers already committed to AWS, Google Cloud or Microsoft can also procure it through those cloud marketplaces. Typical jobs are grounding a chatbot's answers in company documents, recommending similar items and giving agents access to what an organisation knows. Vectors usually arrive through an existing data pipeline, and Databricks, Snowflake Openflow, Confluent Kafka and Airbyte can each feed an index. Platform engineers declare indexes in Terraform or Pulumi and watch them in Prometheus or Datadog. Large loads come in from Parquet files in object storage, and backups let a team restore an index under a new name. In each of these builds Pinecone does the retrieval step, returning the records an application or a language model needs for its next answer.