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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
Introducing the Pinecone Partner Program: Integrate and Grow with Pinecone
Chuck Fontana, Anne Colbeck · 2024-04-08 · via Pinecone

Reduce friction for developers with Pinecone

Pinecone is excited to announce the Pinecone Partner Program, designed to streamline the developer experience. This program allows for seamless integration, enabling developers to access Pinecone with just three clicks from our partners' platforms. The new integration represents a significant step toward reducing friction and increasing productivity for developers building knowledgeable AI applications.

The Pinecone Partner Program is a new addition to a growing list of existing integrations between Pinecone and the AI stack, including observability platforms, data sources, model providers, and frameworks. With this new program and integration, partners can help developers dramatically accelerate successful deployments of AI. The integration will occur in two phases. The first phase, starting on April 17, 2024, lets developers sign up for or log into Pinecone via any integrated partner. The second phase, launching in May 2024, will allow developers to go beyond signing up or connecting to Pinecone. They will be able to fully use Pinecone without leaving their preferred tools.

Level of engagement and program tiering

There are three engagement tiers: Select, Premier, and Elite. The different tiers represent increasing sales, marketing, and technical support levels. Criteria such as signups, active users, usage metrics, and partners agreements determine program tiers.

3-click integration with Pinecone inside partner’s product

As part of the 3-click integration, Pinecone provides partners with a frictionless signup flow and the capability to manage Pinecone resources. Pinecone provides a Connect widget that partners can embed into their applications to attain a frictionless signup flow. This widget allows their end users to create and connect to Pinecone for a seamless signup and login process. Through the widget, the end user can sign up for or log in to Pinecone, select an organization and project, and generate an API key for the project.

Two ways to embed a widget:

  • Script that renders the widget in apps.
  • Python library (pinecone-notebooks) that renders the widget in Colab notebooks.

Script Example

Python Example

In addition to the frictionless signup flow, partners can manage Pinecone resources from their applications on behalf of end users starting mid-May. From the developer's perspective, these capabilities dramatically remove friction in the development process. Developers can now seamlessly access the market-leading vector database without leaving their preferred tools. And partners can easily set up the integration and monitor usage for continuous improvement.

Industry-leading companies are joining the program

The program is launching with industry-leading and rapidly rising companies, including Anyscale, Confluent, LangChain, Mistral, Monte Carlo, Nexla, Pulumi, Qwak, Together.ai, Vectorize, and Unstructured, with more to be announced. These founding members are collectively known as “the RAG Pack.”

“As companies invest in AI to drive innovation and maintain their competitive edge, the unstructured data powering their models has to be accurate and reliable,” said Lior Gavish, co-founder and CTO of Monte Carlo. “In 2023, we announced our Pinecone integration to bring data observability to vector databases, and now, we couldn’t be more excited to partner with Pinecone as a founding member of their RAG-pack. This partnership further cements our leading position in the GenAI stack and signals our continued investment in helping companies achieve reliable data and AI across the business.”

“We're thrilled to be a founding member of the Pinecone Partner Program and part of the RAG Pack,” said Bruce Holbert, Vice President of Partnerships at Nexla, “This initiative aligns perfectly with our mission to empower developers with seamless integration across structured and unstructured data sources across the enterprise. Together, we're committed to significantly accelerating GenAI applications and driving innovation in AI development.”

"We're excited to team up with Pinecone and leading innovators in the GenAI domain to empower developers," said Isaac Harris, Head of Business Development at Pulumi. "Our Pinecone Provider simplifies workflows and expedites the development of GenAI applications by bringing Pinecone's capabilities to Pulumi’s Infrastructure as Code platform. This integration enhances the developer experience by using modern programming languages and facilitates the creation of scalable, market-ready GenAI solutions."

Ran Romano Co-founder and CPO at Qwak "We at Qwak are thrilled to be foundational members of the Pinecone Partner Program, known as the 'RAG Pack.' This collaboration marks a significant milestone in making GenAI development more accessible and efficient for developers worldwide. The three-click integration with Pinecone aligns perfectly with our mission to simplify complex AI Infra operations. It empowers our users to leverage the cutting-edge capabilities of Pinecone's vector database directly within our platform, significantly enhancing their GenAI projects' speed, accuracy, and overall impact. We're excited about the possibilities this partnership opens up for our community and the broader ecosystem."

"As part of Pinecone's RAG Pack, Vectorize can now simplify the experience of building RAG-optimized vector indexes on Pinecone by leveraging their partner SDKs and APIs. This means less friction for the developer and faster time to value,” said Chris Latimer, Co-Founder of Vectorize, “With Pinecone and partners like Vectorize, organizations can unlock the full potential of their data, providing accurate and relevant context to power their generative AI applications. We're excited to be a founding member of Pinecone's Partner Program and working with Pinecone to empower developers to build transformative AI solutions."

Build an ecosystem with Pinecone

Apply for the Pinecone Partner Program today to provide a better experience for your customers building AI applications.