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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
Free plan gets 3x more capacity with serverless upgrade
Gibbs Cullen · 2024-04-17 · via Pinecone

The Pinecone Starter (free) plan has become integral to the AI journey for hundreds of thousands of developers looking to build remarkably better AI applications. Starting today, the free plan gets 3x more capacity with Pinecone serverless.

More capacity and more indexes

Here are the upgrades to the new free plan:

  • 3x more capacity: Increased efficiencies from the new serverless architecture and algorithms enable us to support an even bigger free plan. You get 2GB of storage (up to ~300k records* vs. ~100k records before serverless), 2M Write Units (WU) (~300k writes), and 1M Read Units (RU) (~100k reads) per month. WU and RU quotas are reset each month. *1536 dim vectors w/ 500 bytes of metadata
  • Up to 5 indexes: Free users can now have up to 5 indexes, with up to 100 namespaces per index. Easily experiment with your multi-tenant use case before moving to production.
  • Easier to upgrade: When you’re ready to scale beyond the free plan limits, you can easily upgrade and continue building without needing to create a new index or project. Note: Usage on free serverless indexes will start incurring charges immediately after upgrade.

Getting started is a breeze — just name your index, load your data, and start querying through the API or one of our clients. Existing free plan users can easily convert ‘gcp-starter’ indexes to serverless. Support for migrating free pod-based indexes to serverless is coming soon.

Start building today

The serverless free plan is now available. Follow the steps in our quickstart to test it out.