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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 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 Build Privacy-aware AI software using Pinecone
Bringing the leading vector database to your cloud
Ben Esh, Shimshon Zimmerman · 2025-02-20 · via Pinecone

We’re excited to announce early access for a Bring Your Own Cloud (BYOC) offering of the Pinecone vector database on AWS. This solution lets you deploy a privately managed Pinecone region within your cloud account, giving you the security and control of a self-hosted solution while retaining the seamless experience of a fully managed SaaS product. Your data is stored and processed exclusively within your cloud account, ensuring complete data sovereignty—while our team handles all operational aspects, from deployment and maintenance to monitoring and updates.

More ways to access Pinecone the way you want

Companies are eager to unlock the potential of their unstructured data to build new products and capabilities. Pinecone’s purpose-built vector database retrieves accurate, well-informed, and up-to-date insights for customers to build uniquely knowledgeable AI applications. Since launching Pinecone serverless, we've seen increasing demand from large enterprises and regulated industries that require strict data sovereignty—where storing or processing data outside their own cloud is not an option.

At Pinecone, we believe engineers should spend time building AI applications, not managing infrastructure. That’s why we designed a best-in-class vector database as a managed service, prioritizing ease of use from day one. Deploying Pinecone in your cloud account gives you the flexibility to build AI solutions in the way that works best for you and your requirements.

Key benefits:

  • Security and compliance: Pinecone ensures your sensitive data stays within your account so you can meet your data sovereignty requirements.
  • Enterprise-grade access controls: Control user access, set security and usage policies, and monitor users and workloads operating within their environment.
  • Private multi-tenant region: Use your private Pinecone environment to host multiple workloads for your company and enjoy the cost-performance tradeoffs of multi-tenant resource sharing.
  • Any AWS region: Pinecone can be deployed in any AWS region of your choice, ensuring low-latency communication with your applications.
  • Cost savings: Deploying a Pinecone service within your AWS account allows you to take advantage of your existing AWS discounts, saving plans, and commitments.

How it works

The Pinecone vector database is comprised of two main components:

  • The Control Plane is responsible for managing the index lifecycle, as well as operating all region-agnostic services such as user management, authentication, and billing. The control plane does not hold or process any records.
  • The Data Plane is responsible for storing and processing your records. It runs compute pods for executing vector similarity search queries and indexing operations, interacting with object storage to retrieve and persist index data.

When you create a serverless Pinecone index, all of its underlying data is stored in a Pinecone-managed Data Plane instance located within a region of your choice. This is the recommended offering for most users, as it provides the simplest way to use the Pinecone vector database.

However, for use cases with strict data sovereignty requirements, the Data Plane can be deployed to a dedicated VPC inside your AWS account, ensuring that all data is stored and processed locally and does not leave your organizational boundaries. Private Endpoints for AWS PrivateLink can be established as an additional security measure to protect your data plane API calls.

When setting up BYOC, Pinecone employs the principle of least privilege. We enforce role-based access control (RBAC) with specific IAM permissions and boundary policies to protect your assets. Access for maintenance and troubleshooting is secured through a customer-controlled VPN, with comprehensive logging and auditing.

Despite this split model, this is still a managed service: by operating a Pinecone agent service in each environment, we maintain communication with the global Pinecone-hosted control plane to allow ongoing management, health monitoring, and software updates. This design will enable us to provide the same service and reliability level as you would normally get with Pinecone while meeting enterprise security and compliance requirements.

Getting started

Pinecone’s BYOC offering for AWS is now in early access. To learn more, read the BYOC documentation or contact us.