Pinecone Database

What it does

Pinecone Database takes in text or vector data, organized into indexes with declared fields for full-text, dense vector, and sparse vector search. It returns search results, ranked matches, and recommendations, and it can generate embeddings server-side when you upsert and query with text. Metadata filters narrow results, and reranking can reorder them for relevance.

Developers use it to power semantic search, recommendations, retrieval for RAG chatbots, and long-term memory for agents. Namespaces separate tenant data within one index, dedicated read nodes handle sustained high query volume, and backups plus bring-your-own-cloud deployment support production operation.

It suits developers and teams building retrieval-based AI features. Starting is free with an index on the Starter plan, and Builder has a flat monthly fee while Standard and Enterprise apply monthly usage minimums. Enterprise deployment is contact-based. Plan limits on indexes, namespaces, projects, and users apply, and Pricing varies by cloud and region.

What it can do

• Full-text, semantic, sparse-vector, and hybrid search from one index
• Integrated embedding that generates vectors from text server-side
• Metadata filtering and reranking of results
• Namespaces for tenant data separation within one index
• Dedicated read nodes for sustained high query volume
• Backup and restore, plus bring-your-own-cloud deployment

Works with

• LangChain
• LlamaIndex

Example workflows

Semantic search over a knowledge base

Illustrative example. Input: about 30K documents averaging 20KB each. Steps: create an index with integrated embedding, upsert document text, query with user questions, apply metadata filters, optionally rerank. Output: ranked matching documents for each search. Illustrative sizing from the pricing page, not an observed outcome.

Forum-answering retrieval bot (RAG)

Illustrative example. Input: a user forum with about 10 categories and 5KB average posts. Steps: index new posts into category-scoped namespaces, query the namespace matching each chat, return relevant context to the model. Output: scoped retrieved context for answering questions. Illustrative sizing only.

Before you choose

• Starter has storage, read/write, index and regional limits.
• Builder is a flat account plan; Standard and Enterprise have usage minimums.
• Database, Assistant and inference have separate allowances and meters within the same account.

Listed under

• Coding
• Sandboxes and infrastructure
• AI
• AI infrastructure
• Developers
• Embeddings
• Enterprise
• Enterprise search

Get started

Create a first index for free, then pay as you go when ready to scale.

1. Sign up at app.pinecone.io and create your first index on the Starter plan
2. Choose a search path and define index fields in the data model, then use the Quickstart
3. Connect via the API or SDK, or use the MCP server with an MCP-compatible agent

Pinecone plans apply to the account rather than creating separate fees for each product. Starter is free within its database caps. Builder is a flat $20/month with fixed allowances. Standard has a $50/month usage minimum and Enterprise a $500 minimum. Database storage, read and write usage have their own meters; Assistant and inference have separate allowances within the same account. A Standard trial offers $300 credit for three weeks. Check the selected cloud, region and current rate table before scaling.

21-day free trial.

Source: pinecone.io/pricing, checked 29 Oct 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Pinecone Products | Pinecone ↗www.pinecone.io
• Pricing | Pinecone ↗www.pinecone.io
• Pinecone Database - Pinecone Docs ↗docs.pinecone.io

Company website

pinecone.io: product, pricing and policy pages

YC directory mirror

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Web search

Confirms the official site

Social accounts

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Human review

Checked by a person

knowledge_base

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.