# www.pinecone.io

> AI-optimized mirror of www.pinecone.io containing 51 pages totalling 17,009 words of clean markdown content, structured data, and semantic HTML. Original source: https://www.pinecone.io/. Last updated: 2026-04-27T14:46:48.000Z.

Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG).

## Homepage

- [The vector database for scale in production](/site-root.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (472 words)

## Articles & Blog Posts

- [Pinecone Vector Database](/product/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (419 words)
- [Secure by design](/security/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (346 words)
- [how-pinecone-works/index.html](/how-pinecone-works/index.html) (1 words)
- [company/index.html](/company/index.html) (1 words)
- [Pinecone supercharges AI for the world’s leading companies](/customers/index.html): Learn how leading companies are using Pinecone to build AI-powered applications. (677 words)
- [Pinecone Pioneers](/community/pinecone-pioneers/index.html): Pinecone Pioneers are developers, educators, and collaborators working to make AI more knowledgeable—using real-world vector search, RAG, and retrieval. Applications for the 2025 cohort are now open. Learn how Pioneers build, share, and support the Pinecone community—and how you can get involved.

 (895 words)
- [Learn to love vectors](/learn/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (292 words)
- [Cookie Policy](/cookies/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (935 words)
- [community/events/index.html](/community/events/index.html) (1 words)
- [Engage with the Pinecone Community](/community/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (322 words)
- [Build the future of Vector Databases](/careers/index.html): Join us in building the database for machine learning. We're looking for talented individuals to help us push the boundaries of vector search technology. (402 words)
- [Build an AI Application in Typescript](/learn/crash-course/typescript-ai-app/index.html): Master the basics of vector search and build AI applications — all in your favorite language. (344 words)
- [Contact HIPAA](/contact/hipaa/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (108 words)
- [Building real-time AI applications with Pinecone and Confluent Cloud](/confluent-integration/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (320 words)
- [Optimizing and accelerating data classification with Pinecone and AWS](/learn/aws-classification/index.html): Classification is a crucial component of machine learning (ML) and artificial intelligence (AI),  (251 words)
- [Building remarkable multimodal search applications with Pinecone and AWS](/learn/multi-modal-search/index.html): Multimodal search with a vector database like Pinecone enhances information retrieval by integrating  (200 words)
- [Contact our team](/contact/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (181 words)
- [blog/optimizing-retrieval-inference/index.html](/blog/optimizing-retrieval-inference/index.html) (1 words)
- [Bring reliable GenAI applications to market with Amazon Bedrock and Pinecone](/amazon-bedrock/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (314 words)
- [learn/aws-reference-architecture/index.html](/learn/aws-reference-architecture/index.html) (1 words)
- [customers/deep-talk/index.html](/customers/deep-talk/index.html) (1 words)
- [learn/series/langchain/langchain-prompt-templates/index.html](/learn/series/langchain/langchain-prompt-templates/index.html) (1 words)
- [learn/scaling-pinecone-serverless/index.html](/learn/scaling-pinecone-serverless/index.html) (1 words)
- [blog/vectors-as-ai-data-primitives/index.html](/blog/vectors-as-ai-data-primitives/index.html) (1 words)
- [learn/sparse-retrieval/index.html](/learn/sparse-retrieval/index.html) (1 words)
- [legal/data-processing-addendum/index.html](/legal/data-processing-addendum/index.html) (1 words)
- [learn/privacy-aware-software/index.html](/learn/privacy-aware-software/index.html) (1 words)
- [Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse](/blog/millions-at-stake-melange/index.html): Melange, a patent analytics company, chose Pinecone's serverless vector database to overcome scalability and reliability issues with their previous self-hosted Milvus cluster. Melange's core business relies on "high-recall retrieval" to accurately find 6-10 critical "prior art" documents out of hundreds of millions of global patents to win patent litigation cases. The "cost of inaccuracy" is not just legal risk for their clients (with cases averaging $2.3M-$4M in cost and $24M in damages awarded), but also operational risk and engineering cost for Melange. Pinecone provided the required 99% recall at massive scale (now over 600M vectors) and simplified operations, eliminating the need for database maintenance and saving Melange an estimated ~$75,000 per year, allowing their engineers to focus on product and embedding strategies instead of infrastructure. This reliability is a competitive advantage in the high-stakes legal industry. (1,222 words)
- [Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness](/blog/cascading-retrieval-with-multi-vector-representations.html): This blog post explores how multi-vector retrieval improves search accuracy by capturing rich query-document interactions, while addressing its scalability challenges. It introduces a practical, staged retrieval pipeline that balances speed and effectiveness, starting with fast retrieval, refining with multi-vector embeddings, and finishing with cross-encoder reranking. The post highlights ConstBERT, a constant-space multi-vector model co-developed by Pinecone and academic collaborators, and shows how to integrate it into Pinecone to build efficient, scalable, and accurate search systems. ConstBERT is now available in open source. (2,973 words)
- [Pinpoint references faster with citation highlights in Pinecone Assistant](/learn/pinecone-assistant-citation-highlights/index.html): With citation highlights, Pinecone Assistant can now pinpoint the exact section or sentence used to generate a response. This technical guide shows you how to get started with Pinecone Assistant and leverage citation highlights. (1,110 words)
- [Blog | Pinecone](/blog/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (188 words)
- [Migration Guide](/lp/pods-vs-serverless/index.html): Pinecone's pod-based architecture is legacy infrastructure. Learn the differences between pods and serverless, why serverless is the recommended path forward, and how to migrate your pod-based indexes. (1,421 words)
- [Introducing the Pinecone Partner Program: Integrate and Grow with Pinecone](/blog/introducing-the-pinecone-partner-program/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (963 words)
- [Roy Miara](/author/roy-miara/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (130 words)
- [Antonio Mallia](/author/antonio-mallia/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (177 words)
- [Arjun Patel](/author/arjun-patel/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (204 words)
- [Intro to Cascading Retrieval: Boost RAG and search precision by up to 48%](/community/events/intro-to-cascading-retrieval/index.html): Join us on Tuesday, December 17, for an introductory workshop on Cascading Retrieval, an enhanced retrieval path that seamlessly combines dense, sparse, and reranking into a unified search pipeline for unparalleled precision, performance, and ease of use. In fact, our research shows this approach yields up to 48% better performance than dense retrieval alone. 

Gareth Jones (Staff Product Manager) and Antonio Mallia (Senior Research Scientist) will walk you through:
- An overview of cascading retrieval, and how it differs from hybrid
- Details behind our comparative benchmarking  
- A live demo incorporating our latest retrieval and inference capabilities

Not using sparse retrieval yet? No problem. We’ll cover how to get started if you already have an index with dense vectors– and we’ll take your questions live.

When: December 17, 2024, from 11-12 pm ET 
Where: Tune in live on Zoom

Save your spot today! Can't join live? Register anyway, and we'll send you the recording. (230 words)
- [Data Engineering Summit](/community/events/aug-21-2025-palo-alto/index.html): Data Engineering Summit, Aug 21, 6:00 pm in Palo Alto
with speaker from Pinecone.io (116 words)
- [Livestream: Memory, models, and Karpathy tweets](/community/events/04-22-come-build-with-pinecone-livestream/index.html): Come Build with Pinecone is back Wednesday, April 22 at 10am PT / 1pm ET / 5pm GMT.

Jenna Pederson, Arjun Patel, and Roie Schwaber-Cohen will chat about memory, models, and Andrej Karpathy's latest viral tweet. But if you caught our last stream, you know we take questions from chat, are ready for a good laugh, and we'll probably end up somewhere unexpected.

Join us on YouTube: https://www.youtube.com/@pinecone-io/live  (126 words)
- [Run Pinecone on Azure](/blog/azure/index.html): Pinecone on Azure is now available. Learn more and start building today.  (378 words)
- [The Magic of Multilingual Search with Pinecone Serverless and Inference](/community/events/multilingual-search-workshop/index.html): Join us for our upcoming workshop to learn about multilingual semantic search with Pinecone Serverless and Inference. Multilingual embedding models you to search across languages without any need for translation, but how do they work, and how can you leverage them in your search applications?

Led by Arjun Patel, Developer Advocate at Pinecone, you will learn about the strength of multilingual embedding models including:

- The workings of text embedding models and how they achieve multilingualism
- Benefits of using multilingual models and Pinecone Inference in your search applications
- A demo of a simple language learning example involving both cross-lingual and mono-lingual search with Pinecone Serverless and Inference

Attendees will gain better insight into the workings of multilingual search and how to use Pinecone Inference and Serverless to build multilingual applications on top of their data. (209 words)
- [Designing a RAG Pipeline (Interactive)](/learn/series/vector-databases-in-production-for-busy-engineers/rag-pipeline-design/index.html): 
Building a Retrieval-Augmented Generation (RAG) pipeline can be complex due to many interdependent factors. Our interactive questionnaire provides tailored recommendations to help you get started efficiently and effectively. (305 words)
- [LlamaIndex Agentic RAG-a-thon with Pinecone](/community/events/llama-index-pinecone-oct-2024/index.html): Enter the LlamaIndex Agentic RAG-a-thon in partnership with Pinecone in the heart of Silicon Valley this Fall. (154 words)
- [Vamshi Enabothala](/author/vamshi-enabothala/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (66 words)
- [Build production-grade chat and agent-based applications in minutes](/product/assistant/index.html): Assistant is an API service for answering complex questions about your proprietary data accurately and securely. It lets you focus on building your core product by managing all the infrastructure and operations needed for a Q&A system. Just add your company data, and it's ready to go. (196 words)
- [Building a RAG Pipeline with Appsmith & Pinecone](/community/events/webinar-appsmith-mar-27/index.html): Your RAG pipeline shouldn't need 47 different tools to work. 

Join us for a session where we'll dive into the essentials of Retrieval-Augmented Generation (RAG) pipelines featuring Bear Douglas, Pinecode's VP of DevRel and our Appsmith DevRel Team

RSVP Now!

 (110 words)
- [Founders Connect meetup](/community/events/feb-2025-lisbon-saas-founders/index.html): Pinecone is a sponsor of Founders Connect metup in Lisbon, Feb. 2025 (72 words)
- [Quiz: Vector Similarity](/learn/crash-course/typescript-ai-app/quiz/vector-similarity/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (63 words)
- [Service Specific Supplemental Terms](/legal/supplemental-terms/index.html): Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away. (44 words)
- [Purdue Catapult](/community/events/2024-spring-purdue-hackathon/index.html): Pinecone is a sponsor at Purdue's FIRST startup x AI hackathon, providing hundreds of students at Purdue with a unique opportunity to explore the capabilities of cutting-edge AI technologies and turn their innovative project ideas into reality (62 words)

## Resources

- [Full Page Index](/index.html): Browse all cached pages with rich metadata
- [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines
- [XML Sitemap](/content/sitemap.xml): Machine-readable sitemap for crawler discovery
- [Robots.txt](/content/robots.txt): Crawler directives