AI/ML

Pinecone Product Plan

Managed vector database for semantic search, RAG, recommendation systems, and similarity matching.

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Open the editable plan instantly, or carry its requirements into a generated system Architecture.

Vector storage and search

Serverless indexes

Metadata filtering

Namespace isolation

Hybrid search (dense + sparse)

Real-time upserts

Batch operations

Collections (snapshots)

REST and gRPC API

SDKs (Python, Node, Go)

As a developer, I can store and query vector embeddings

As a developer, I can build RAG pipelines with semantic search

As a team, I can scale to billions of vectors serverlessly

As a developer, I can filter by metadata alongside vector search

As a developer, I can use namespaces to isolate tenants

RustGoPythongRPCKubernetesAWSFaissHNSWS3

Category	Requirement	Target
Performance	Query latency	< 50ms P99 for top-K queries on billions of vectors
Scalability	Index size	Billions of vectors per serverless index
Availability	Database	99.95%: RAG pipelines depend on vector search
Security	Isolation	SOC2, namespace-level tenant isolation, encryption at rest

MVPVector DB

Upsert/query vectorsTop-K similarity searchREST APIPython SDK

v1.0Scale

Metadata filteringNamespacesCollectionsBatch operationsgRPC API

v2.0Serverless

Serverless indexesHybrid searchInference APIIntegrations (LangChain)Assistant plugin

Query latency P99: < 50ms

Vectors stored: > 1T across all customers

Developer adoption: > 100K active indexes

Serverless cost savings: > 50% vs pod-based

Team messaging platform with channels, direct messages, file sharing, integrations, and real-time search.

Payment processing platform with subscriptions, invoicing, fraud detection, and multi-currency support.

Property rental marketplace with search, booking, payments, reviews, and host management.

Open the editable Product Plan first, then carry it into Architecture and Plan & Ship when it is ready.