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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
Going Global: Building the Global Control Plane API
Gareth Jones, Jack Pertschuk · 2024-04-22 · via Pinecone

We built the Global Control Plane API to make it even easier to build and scale with Pinecone. It uses a global URL for all control plane operations (e.g., list indexes, create indexes, list collections), regardless of cloud environment.

Launched in preview alongside Pinecone serverless, we’ve continued to iterate and make improvements, and are excited to announce that the Global Control Plane API is now generally available (GA) to all users.

In this blog, we share the motivations and engineering efforts behind our transition from the regional (or legacy) control plane API to the new global API, and show you how to get started.

Note: The legacy version of the API, which requires regional URLs for control plane operations, is deprecated as of April 15, 2024 and will be removed in a future, to be announced, release. We recommend migrating to the new API as soon as possible.

Evolving the control plane

Users have a set of control plane endpoints to interact with and create instances of Pinecone. In the legacy API, each environment has its own endpoint for managing projects, indexes, collections, and API keys, independent of other environments. Because of this architecture, each project is restricted to a single environment and region. Any resources and data that the user creates or uploads are forever scoped to that environment, and users who need to manage resources in multiple environments have to create and keep track of separate projects and API keys for each region in which they use Pinecone.

In the legacy API, each project is restricted to a single environment and region.

We initially chose to build region-scoped APIs for fault tolerance. Since all the API calls go directly to the regional endpoint where the customer’s data lives, an outage in a single cloud or region wouldn't impact customers in other regions.

Over time, however, it became clear that region-scoped endpoints resulted in a poor user experience. Many of our users manage resources for different use cases such as development, staging, and production across many geographies. This makes it difficult for them to maintain visibility and control over all resources in Pinecone. Additionally, the regional scope of Pinecone’s legacy control plane made it difficult to implement commonly requested features such as multi-region indexes that involved sharing data and resources between geographies or environments.

We determined there was a critical demand for a more integrated and transparent approach to project and resource management across various environments and regions.

To address the challenge of managing resources across multiple Pinecone environments and projects, we developed a new global control plane API — api.pinecone.io. The global API eliminates the need for per environment endpoints and allows users to access resources from multiple environments with the same API key.

The new, global API uses a global URL for all control plane operations regardless of cloud environment.

Note: The legacy environment endpoints are now deprecated and will be removed in a future release, but will continue to be available until then. New features will only be supported by the new global control plane API.

Despite moving control plane operations to a single global URL, we wanted to maintain some advantages of the legacy regional API, such as:

  • Fault tolerance and reliable service
  • Secure credential storage
  • Low latency to clients globally

We needed to back our new API with a database that provided high availability and geographical replication out of the box. For this purpose, we chose Google Cloud Spanner, which also gives us the ACID-compliant transactions and durability required to enforce quotas, uniqueness constraints, and safely persist user metadata.

Improving the developer experience was another key objective. By deploying a globally replicated, highly available Rust API server backed by Cloud Spanner, we are able to serve traffic with minimal latency, no matter where users are located. The best part of this is that users don’t have to take any special steps — requests to api.pinecone.io are automatically routed to the nearest API server via Google Cloud’s global load balancer. We also made a handful of usability improvements to the new API, including:

  • Eliminating inconsistent naming conventions
  • Updating API response types to simplify new features
  • Implementing more accurate and standardized errors

Start Building Today

The Global API is currently supported within our Python, Node, and Java clients. View the quickstart to start building today, or check out the migration guide to upgrade to the new API.