---
title: "The vector database to build knowledgeable AI | Pinecone"
url: https://stacklist.com/card/82122c38-b952-45c6-a0f1-da5e351f41cb
source_url: "https://www.pinecone.io/"
stack: https://stacklist.com/c/technology/stack/eb493977-1bb5-4f31-b9b3-d90743949f5b
summary: "Pinecone is a fully managed vector database built for AI, offering fast retrieval, automatic indexing, and consistent query performance at any scale. The platform supports use cases including RAG pipelines, agent memory with namespaces, semantic search over billions of vectors, and real-time recommendations with inline metadata filtering."
tags: "vector-database, pinecone, rag, semantic-search, ai-agents, embeddings, managed-infrastructure"
key_entities: "Pinecone (organization), vector database (technology), RAG (concept), semantic search (concept), Claude Code (technology), Cursor (technology), Copilot (technology), Gemini (technology), namespaces (concept), AWS (organization), recommendations (concept)"
classification: "reference"
content_hash: "sha256:4bddc6972a19d527c7cc855c78a9641aee75b20a0fcad64416fc586cb32562db"
acp_version: "0.2"
token_counts_approximate: 1154
visibility: public
agent_accessible: true
status: "final"
---

# The vector database to build knowledgeable AI | Pinecone

Build Knowledgeable AI Give agents knowledge Fast retrieval. Accurate results. Lower costs. Start in seconds. Start Building Get a Demo GET STARTED {Claude Code} {Cursor} {Copilot} {Codex} {Gemini} {CLI} {MCP} View Docs $ claude plugin install pinecone Copy Install: Run in terminal. Auth: Set PINECONE_API_KEY . Run: Use /pinecone:help to start. Cost-performance at any scale Estimate the cost of your workload. See full pricing I'm building a RAG pipeline for a passion project Get an estimate Your indexes, always visible Monitor performance, explore your data, and manage indexes from a clean, fast console — or stay in the terminal. Your call. app.pinecone.io Get started Database Quickstart Indexes (3) Backups Assistant Inference API keys Manage Indexes + Create index Name Status Records Region Type Dimensions s-cache Ready 55,611 aws us-east-1 Dense 1,536 ··· product-search Ready 2,418,302 aws us-west-2 Dense 3,072 ··· user-profiles Ready 847,211 aws eu-west-1 Dense 1,024 ··· s-cache Record count 55,611 Region aws us-east-1 Type Dense Host s-cache-vjxlb1i.svc.aped-4627-b74a… ··· Connect Browser Metrics Namespaces (4) Imports Configuration Records Upsert record Namespace cache_bench-test ▾ Operation Search by ID ▾ ID entry_01KQJG0RTKR8SF2KHY97XYQAK9 Top K 10 Filter Rerank Search Search: 10 results (top_k=10) 1 RUs ⓘ 1 Score 0.9997 ··· _id: entry_01KQJG0RTKR8SF2KHY97XYQAK9 cached_at: 1777663501 expires_at: 1777749901 hit_count: 0 last_hit_at: 0 query: “. what is a corporation?” s-cache Record count 55,611 Region aws us-east-1 Type Dense Host s-cache-a1b2c3d.svc.pinecone.io ··· Connect Browser Metrics Namespaces (4) Imports Configuration Metrics All metrics are represented in your local timezone. Updated less than a minute ago. Longer time ranges use lower resolution. 15m 4hrs 12hrs 1d 2d 1w Read units Write units Requests per second Query Upsert Update Delete Fetch List Request latency Query Upsert Update Delete Fetch List p50 p95 p99 Storage size Record count Architecture How Pinecone works Read the whitepaper Pinecone is a fully managed vector database built for AI. Writes are instantly searchable, indexing is automatic, and queries stay fast at any scale. 01 · WRITE <100ms acknowledgment Acknowledged in under 100ms, searchable within seconds. vector throughput, streaming in 02 · INDEX Automatic no tuning required Algorithms selected per data size, upgraded in the background automatically. index continuously rebalancing 03 · QUERY Consistent at any scale All data searched in parallel. Speed holds steady regardless of scale. p99 latency, improving with scale Use cases What teams build with Pinecone {agents} Every agent gets its own namespace. Millions of contexts, no ops. Agents need memory that scales to millions of users. Namespaces give every agent its own isolated context without separate indexes. Millions of agents, zero ops overhead — the infra disappears so your product can ship. Agents need memory that scales to millions of users. Namespaces give every agent its own isolated context without separate indexes. Millions of agents, zero ops overhead — the infra disappears so your product can ship. 1.7M namespaces · 400 QPS {rag} New content is queryable in seconds — no re-index jobs, no pipeline to maintain. Drop in new documents and they're searchable immediately. Teams building knowledge bases, support bots, and doc search ship in days instead of months. 30M writes/day {search} New content is queryable in seconds — no re-index jobs, no pipeline to maintain. Semantic search that actually scales. One index handles billions of vectors with no manual sharding, no tuning, and no ops overhead. Recall stays high whether you have 10M or 10B records. 150ms P90 · 2.8B vectors {recommendations} Filters run inline with search — no post-processing, no added latency. Recommendations need speed and accuracy together. Pinecone applies metadata filters inline during search, so relevance and business rules run in one pass — no extra round trips, no latency tax. 12ms P50 with filters Enterprise Building for your organization? Explore Enterprise Meet the compliance, security, and scale requirements to bring enterprise AI to market faster. Secure Encryption at rest and in transit, SSO, RBAC, CMEK, private networking. Compliant SOC 2 Type II, HIPAA, GDPR, and ISO 27001 certified. Reliable Uptime SLAs, support SLAs, and dedicated customer success built in. Start building knowledgeable AI today Create your first index for free, then pay as you go when you're ready to scale. Start Building Get a Demo Subscribe to Pinecone Subscribe