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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
Opening up our free plan
2023-04-26 · via Pinecone

Since launching the Starter (free) plan two years ago, we’ve learned a lot about how people use it. Whether building a personal project or testing a prototype before upgrading, it turns out 99.1% of users utilize less than 20% of the capacity on their free account. In other words, while one p1 pod can store 500k 1536-dimensional embeddings, 99.1% of users store less than 100k embeddings (and 90.2% using less than 10k embeddings).

Meanwhile, demand for Pinecone has grown to extreme levels — with over 10k sign ups per day — and continues to climb. While we’re adding cloud capacity for free users, we’re also managing a quickly growing waitlist to minimize disruptions and keep Pinecone running as expected. We’ve always been committed to providing a free plan so developers can explore and build with vector databases, however, the unused capacity of the existing free plan is slowing down would-be users from doing just that. Unlocking this capacity would mean more users could access and start building with Pinecone.

We’ve also learned that users exploring vector databases for the first time don’t want to think about choosing a pod type (e.g. s1 or p1) or index parameters. They just want to start building. And once they experience the power of Pinecone and upgrade to a paid plan, they wish they could keep the free index as a sandbox to try out new ideas and features before pushing larger-scale projects to production.

Updates to the free plan

We’re making the following changes to the free plan effective today, April 26th. Existing projects created before today on free accounts will not be affected by these changes. Projects on paid plans are not affected.

  • Updating capacity for free plan: We’re adjusting the free plan’s capacity to match the way 99.1% of users interact and explore with Pinecone. Indexes in the free plan now support ~100k 1536-dimensional embeddings with metadata (capacity is proportional for other dimensionalities). While this is lower than the previous capacity, it’s more than the vast majority of our free users need.
  • Making free indexes available after upgrade: Continue testing with your free index after you upgrade. Paid users will also have access to a single free index through the newly created environment dedicated for free usage (us-west1-gcp-free).
  • Making it simpler to get started: With the free plan’s updated architecture, you don’t have to figure out and choose a pod type for your index. Just create the index and start building.
  • Accelerating waitlist admission: Thanks to recouping significant amounts of unused cloud capacity on the free plan, we’re able to let a lot more people in from the waitlist. Although we may still use the waitlist to deal with extreme spikes in demand, the waiting times will be much shorter.

The free plan will continue to support a single index and project. As before, inactive indexes on the free plan will be archived after 7 days of inactivity, and for applications that create ephemeral vector indexes in Pinecone (such as AutoGPT) without expecting them to persist, we will archive those indexes after 1 day of inactivity. Archived indexes are saved as collections, and users can recreate indexes from a collection within a few minutes.

We’re continuing to work on improvements to support the rapidly growing community of developers using our free plan. Whether you’re just starting to learn or you’re building a mission-critical application that uses billions of embeddings, we’re building Pinecone to be the first and only vector database you need. Start your journey now.