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Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow
Pinecone reinvents the vector database to let companies build knowledgeable AI
2024-01-17 · via Pinecone

Breakthrough serverless architecture delivers up to 50x cost reduction opening the path to dramatically better GenAI applications.

NEW YORK, Jan. 16, 2024 /PRNewswire/ -- Pinecone, the leading vector database company, announced a revolutionary vector database that lets companies build more knowledgeable AI applications: Pinecone serverless. Multiple innovations including a first-of-its-kind architecture and a truly serverless experience deliver up to 50x cost reductions and eliminate infrastructure hassles, allowing companies to bring remarkably better GenAI applications to market faster.

One of the keys to success is providing large amounts of data on-demand to the Large Language Models (LLMs) inside GenAI applications. Research from Pinecone found that simply making more data available for context retrieval reduces the frequency of unhelpful answers from GPT-4 by 50%[1], even on information it was trained on. The effect is even greater for questions related to private company data. Additionally, the research found the same level of answer quality can be achieved with other LLMs, as long as enough data is made available. This means companies can significantly improve the quality of their GenAI applications and have a choice of LLMs just by making more data (or "knowledge") available to the LLM. Yet storing and searching through sufficient amounts of vector data on-demand can be prohibitively expensive even with a purpose-built vector database, and practically impossible using relational or NoSQL databases.

Pinecone serverless is an industry-changing vector database that lets companies add practically unlimited knowledge to their GenAI applications. Since it is truly serverless, it completely eliminates the need for developers to provision or manage infrastructure and allows them to build GenAI applications more easily and bring them to market much faster. As a result, developers with use cases of any size can build more reliable, effective, and impactful GenAI applications with any LLM of their choice, leading to an imminent wave of incredible GenAI applications reaching the market. This wave has already started with companies like Notion, CS Disco, Gong and over a hundred others already using Pinecone Serverless.

"To make our newest Notion AI products available to tens of millions of users worldwide we needed to support RAG over billions of documents while meeting strict performance, security, cost, and operational requirements," said Akshay Kothari, Co-Founder of Notion. "This simply wouldn't be possible without Pinecone."

Key innovations in the breakthrough architecture of Pinecone Serverless include:

  • Separation of reads, writes, and storage significantly reduces costs for all types and sizes of workloads.
  • Industry-first architecture with vector clustering on top of blob storage provides low-latency, always fresh vector search over practically unlimited data sizes at a low cost.
  • Industry-first indexing and retrieval algorithms built from scratch to enable fast and memory-efficient vector search from blob storage without sacrificing retrieval quality.
  • Multi-tenant compute layer provides a powerful and efficient retrieval for thousands of users, on demand. This enables a serverless experience in which developers don't need to provision, manage, or even think about infrastructure, as well as usage-based billing that lets companies pay only for what they use.

"From the beginning, our mission has been to help every developer build remarkably better applications through the magic of vector search," said Edo Liberty, Founder & CEO of Pinecone. "After creating the first and today's most popular vector database, we're taking another leap forward in making the vector database even more affordable and completely hassle-free."

To extend the ease of use that made Pinecone a developer favorite, Pinecone Serverless is launching with integrations to other best-in-class solutions in the GenAI technology stack, including Anthropic, Anyscale, Cohere, Confluent, Langchain, Pulumi, Vercel, and others to be announced soon.

"Vercel's mission is to help the world ship the best products, and in the age of GenAI that requires Pinecone as the vector database component," said Guillermo Rauch, CEO and Founder of Vercel. "That's why we are announcing that all Vercel users can now add Pinecone Serverless to their applications in just a few clicks, with more exciting capabilities to come."

"We've seen tremendous demand from our customers to connect Confluent to Pinecone in order to fuel real-time GenAI applications," said Jay Kreps, CEO of Confluent. "Our Pinecone Sink Connector (Preview) allows organizations to send continuously enriched data streams from across the business to Pinecone so developers can build and scale real-time GenAI applications faster."

Pinecone Serverless is available in public preview today in AWS cloud regions, and will be available thereafter on Azure and GCP. Try Pinecone for free, learn more about this release in the announcement blog post, and dive deep into the architecture and performance in the technical post.

About Pinecone:

Pinecone created the vector database to help engineers build and scale remarkable AI applications. Vector databases have become a core component of GenAI applications, and Pinecone is the market-leading solution with over 5,000 customers of all types and sizes across all industries. Pinecone has raised $138M in funding from leading investors Andreessen Horowitz, ICONIQ Growth, Menlo Ventures, and Wing Venture Capital, and operates in New York, San Francisco, and Tel Aviv.

Notes:

[1] "Helpfulness" or "Faithfulness" measures the frequency of generated answers that correctly answer the question (or prompt) without hallucinating or replying "I don't know."