Pinecone
Unlock AI potential: serverless, scalable, real-time vector database.. [Freemium]
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What is Pinecone?
Pinecone is a fully managed vector database built specifically for AI applications. It stores and searches through billions of vector embeddings in milliseconds, enabling semantic search, recommendation systems, and retrieval-augmented generation (RAG) for AI agents and chat applications. Writes are instantly searchable within seconds, indexing is automatic with no tuning required, and queries maintain consistent speed at any scale.
Key features include automatic indexing with algorithms selected per data size and upgraded in the background, integrated embedding models that convert text to vectors automatically, hybrid search combining semantic and full-text search, metadata filtering for scoped queries, reranking for improved accuracy, and Pinecone Assistant for building agentic/chat knowledge systems with document upload and grounded responses with citations. The platform supports multiple clouds (AWS, Azure, GCP), offers SDKs for Python, JavaScript, Java, Go, and C#, includes a CLI tool, and provides enterprise security with encryption at rest/in transit, SSO, RBAC, CMEK, and private networking.
Pinecone is designed for machine learning engineers, data scientists, AI developers, and engineering teams building production AI applications like copilots, semantic search engines, smart agents, recommendation engines, and enterprise RAG systems. It serves organizations ranging from startups prototyping AI features to enterprises requiring SOC 2 Type II, HIPAA, GDPR, and ISO 27001 compliance with 99.95% uptime SLAs.
Pinecone pricing
Pricing model: Freemium
Pinecone offers 4 pricing tiers: Starter (free), Builder ($20/month), Standard (pay-as-you-go with $50/month minimum), and Enterprise ($500/month minimum). The Starter plan is free with 2GB storage, 2M write units/month, 1M read units/month, up to 5 indexes, limited to AWS us-east-1 region with community support. The Builder plan at $20/month offers higher quotas with predictable flat pricing and no usage overages, indexes must be in AWS us-east-1. Beyond plan minimums, usage-based pricing charges $4 per million write units, $16 per million read units, and $0.33 per GB per month for storage. The Standard plan includes multi-cloud support across AWS, Azure, and GCP. Enterprise starts at $500/month with 99.95% uptime SLA, HIPAA compliance, private networking via PrivateLink, customer-managed encryption keys, audit logs, and up to 200 indexes. BYOC (Bring Your Own Cloud) requires contacting sales for custom pricing and is designed for data sovereignty requirements. A 21-day Standard plan trial with $300 in credits is available.
Pinecone pros
- Fully managed service with no cluster management or Ops overhead
- Writes acknowledged in under 100ms and searchable within seconds
- Automatic indexing with no manual tuning required
- Consistent query speed at any scale with parallel searching
- Scales to billions of vectors with low latency real-time search
- Integrated embedding models eliminate manual vector generation
- Hybrid search combines semantic search with BM25 full-text search
- Metadata filtering on every query for scoped retrieval
- Native integrations with LangChain, LlamaIndex, AWS, and AI frameworks
- Pinecone Assistant for RAG chatbots with document upload and citations
- SDKs available for Python, JavaScript, Java, Go, and C#
- Enterprise security with SSO, VPC/PrivateLink, CMEK, and audit logs
- SOC 2 Type II, HIPAA, GDPR, and ISO 27001 certified compliance
- 99.95% uptime SLA and support SLAs for Enterprise plans
- Clean console for monitoring performance and managing indexes
- Serverless architecture with pay-per-request On-Demand pricing
- Dedicated Read Nodes for predictable cost and performance at high QPS
Pinecone cons
- Free Starter plan has limited 2GB storage and restricted to AWS us-east-1
- Costs can climb significantly at very large vector volumes
- No on-premise or self-hosted option for ultra-sensitive sectors
- Technical documentation can be demanding for beginners
- Limited querying functionality beyond similarity search
- No versioning feature for embeddings (workarounds required)
- Managed-only with no self-hosting alternative
- Pricing structure may concern users with massive data volumes
Frequently asked questions about Pinecone
What is Pinecone used for?
Pinecone is used for semantic and hybrid search, recommendation systems, and retrieval-augmented generation (RAG) for AI agents. It enables building vector-based personalization, ranking, and search systems that are accurate, fast, and scalable. Common use cases include copilots, semantic search engines, smart agents, recommendation engines, chatbots with grounded responses from proprietary data, and enterprise knowledge systems.
How fast is Pinecone search?
Pinecone acknowledges writes in under 100ms and makes data searchable within seconds. Query latency is around 100ms for roughly 1 million vectors or 20ms for 100K vectors on the Free plan. Speed holds steady regardless of scale because all data is searched in parallel, with p99 latency improving as scale increases.
Does Pinecone have a free tier?
Yes, Pinecone's Starter plan is completely free and includes 2GB storage, 2M write units/month, 1M read units/month, and up to 5 indexes. You get the same production-ready, secure, fully managed vector database as paid plans. The limitation is that it's restricted to AWS us-east-1 region and includes only community support.
What is Pinecone Assistant?
Pinecone Assistant is an API service that uses retrieval-augmented generation (RAG) to enable users to upload documents, ask questions, and receive context-aware responses with citations. It supports PDF, JSON, Markdown, Text, and docx file types, handles document chunking, embedding, vector index management, and storage automatically. The Chat API returns generated grounded text with citations, the Context API returns retrieved snippets for programmatic use, and the Chat Completions API provides OpenAI-compatible endpoints.
Can I use my own embedding models with Pinecone?
Yes, Pinecone supports both integrated embedding (where Pinecone generates vectors automatically from text using hosted models like llama-text-embed-v2) and bring-your-own-vectors (where you generate dense or sparse vectors externally and upsert them directly). You can use external embedding models to convert data into dense vectors for semantic search or sparse vectors for lexical search with custom encoders.
What clouds and regions does Pinecone support?
Pinecone supports multi-cloud deployment across AWS, Azure, and GCP. The free Starter plan is limited to AWS us-east-1 region. The Standard plan and above allow you to choose indexes in any supported cloud and region. Enterprise plans include private networking via PrivateLink for secure cloud connectivity.
How does Pinecone handle indexing?
Pinecone uses automatic indexing with no tuning required. Algorithms are selected automatically based on data size and upgraded in the background without intervention. The index continuously rebalances as data grows. For indexes created after February 2022, the chance of getting throttled is greatly reduced. Recommended batch size is around 100-200 vectors per upsert, achieving up to 5K vectors/sec.
What security and compliance features does Pinecone offer?
Pinecone provides encryption at rest and in transit, SSO (Single Sign-On), RBAC (Role-Based Access Control), CMEK (Customer-Managed Encryption Keys), and private networking via PrivateLink. It is SOC 2 Type II, HIPAA, GDPR, and ISO 27001 certified. Enterprise plans include audit logs and dedicated customer success support.
What is the difference between dense and sparse vectors in Pinecone?
Dense vectors enable semantic search by capturing meaning and context from embedding models, allowing you to find similar matches even with different wording. Sparse vectors enable lexical search with custom encoders for precise keyword and phrase matching. Pinecone also supports indexes with a document schema that stores both dense and sparse vectors alongside BM25-indexed string fields for full-text search under one schema.
How do I get started with Pinecone?
Create a Pinecone account at app.pinecone.io and choose a plan (Starter free, Builder $20/month, or Standard trial with $300 credits). You'll receive an API key in the console. Install the SDK for your language (Python, JavaScript, Java, Go, or C#), create an index using create_index_for_model with an embedding model like llama-text-embed-v2, upsert your text data, and search with query text. Alternatively, use the Pinecone CLI by running 'brew install pinecone-io/tap/pinecone' and 'pc auth login'.