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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
Building AI apps on Azure with Pinecone just got a lot ea...
Tej Chilukuri, Vamshi Enabothala, Cory Waddingham · 2024-11-19 · via Pinecone

No developer wants a complicated and time-consuming workflow for building applications. At Pinecone, we pride ourselves on being simple to use (while delivering high-quality retrieval at scale, high availability, and enterprise-grade security). But we don’t let ease-of-use stop with our vector database. We look for ways to make the development process as straightforward as possible with our integrations too.

At Microsoft Ignite today, Pinecone announced the availability of its industry-leading vector database as part of Azure Native Integrations. This follows the addition of new AI App Templates from Pinecone on Microsoft Azure’s AI App Template Gallery at GitHub Universe. Why is this great news? In short, both of those updates mean it’s easier than ever for developers to build with Pinecone on Azure.

Azure Native Integration

Now that Pinecone is available through Azure Native Integrations, developers can easily provision, manage, and seamlessly use our database as part of their overall Azure solution.

Practically, that means the following:

  1. Integrated provisioning and management: Developers can create and manage their Pinecone organization directly through the Azure Portal.
  2. Identity integration and single sign-on: Identity integration using Microsoft Entra ID (previously Azure Active directory) and single sign-on for seamless navigation from Azure to Pinecone. No need to manage separate credentials.
  3. Unified billing: A single bill for all services consumed on Azure, including Pinecone. Billing goes via Azure Marketplace.

To enroll in the private preview, please sign up here.

Pinecone AI App Template Gallery

The integration of Pinecone with Azure AI App Template Gallery is set to simplify and accelerate deployment workflows. Using Azure Developer CLI (azd) templates, developers can now deploy Pinecone-powered applications within minutes, taking advantage of optimized Azure infrastructure. These templates streamline complex setup steps—handling everything from authentication to continuous integration pipelines—so developers can go directly into building and refining their applications. This integration leverages Pinecone’s scalable vector database with Azure’s robust cloud capabilities, making it a seamless, production-ready option for AI applications.

For more details, check out this Azure blog post.

Amanda Silver, Corporate Vice President, Developer Division, at Microsoft Corp said, “Pinecone allows companies to get the most value out of their data with meaningful and actionable insights. Now that Pinecone is an Azure Native Integration with support for new AI App Templates, it’s easier than ever for developers to create knowledgeable AI applications on Azure.”

How to run Pinecone on Azure

Pinecone is the go-to infrastructure solution for developers building accurate, secure, and scalable AI applications. With a streamlined setup process, you can get started in just three simple steps—in under 10 minutes!

  1. Subscribe: Find Pinecone on the Azure Marketplace and sign up with the convenient "Pay As You Go" plan.
  2. Create an Index: Create an index in Pinecone, complete with an API key to ensure secure access to your data.
  3. Download the SDK: Choose your preferred programming language, download the SDK, and you’re ready to load and query data instantly.

Pinecone simplifies getting started with no need for upfront capacity planning or complex infrastructure setup. Dive in quickly and focus on building your application, not the backend.