Pinecone
@pinecone
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Pinecone is the leading vector database for building accurate and performant AI applications at scale in production.
United States
Joined July 2020
Dedicated Read Nodes are now in public preview ‼️ If you're doing semantic search or recommendations at billions of vectors, this is built for you Real use's of the product today: ‣ 135M vectors, 600 QPS sustained, ~45ms P50 latency. ‣1.4B vectors (filtered), 5.7k QPS at
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Commvault, Pinecone Team on Enterprise AI Stack Resilience
channelinsider.com
Commvault and Pinecone partner to add enterprise-grade cyber resilience for AI and RAG workloads, delivering immutable backups and rapid recovery across clouds.
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Revisiting our chunking strategies guide 7 methods for breaking down text for RAG systems Fixed-size → semantic → contextual chunking with LLMs
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Knowledgeable AI is only as good as the data that powers it, and how well that data is protected. We’re teaming up with @Commvault to bring immutable backups and PITR to vector workloads. It's a new foundation of trust for enterprise AI use cases.
Commvault x Pinecone: pairing enterprise-grade resilience with the vector database powering RAG and AI. This is how organizations move from AI pilots to AI in production — safely and confidently. https://t.co/gg1ZcdXYkB
#CyberResilience #AI #RAG
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Day 4: Built a RAG AI agent on N8N using pinecone Also, shoutout to @pinecone uper beginner-friendly and surprisingly generous API limits. Made learning vector search way smoother.
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Agentic retrieval > traditional RAG Three key advantages: • Better query understanding through reasoning • Dynamic multi-source retrieval • Iterative generation for higher quality
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Vector workloads aren't one-size-fits-all, so Pinecone serverless now offers two modes: On-Demand: Pay per request, auto-scale Perfect for RAG, prototypes, bursty workloads DRN: Reserved nodes, predictable cost Built for billion-vector search with sub-100ms latency
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See how companies are building with Pinecone:
pinecone.io
Learn how leading companies are using Pinecone to build AI-powered applications.
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4 practical use cases for vector databases: • semantic search • hybrid search • recommendations • RAG which one are you shipping first?
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Back to basics to wrap up the year: We wrote a breakdown of what vector databases actually are and how they work. Still one of the best explanations of the infrastructure powering AI memory and semantic search.
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What makes pinecone-sparse-english-v0 work? ✓ Whole word tokenization for precise entity matching ✓ No query expansion = minimal latency overhead ✓ No document expansion = fast indexing without hallucination risk ✓ CoCondenser-based LM Encoder for superior contextual
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We use this analogy constantly (for good reason: it works). Play with the animation yourself within:
pinecone.io
Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.
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"Think of data as water flowing from a hose..." This is one of our favorite ways to explain compaction within our slab architecture: 🚰 The hose only fills small cups (L0 slabs) 🪣 Too many cups? They're poured into buckets (L1) 🛢️ Buckets pile up? They're poured into barrels
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Smart agentic retrieval pattern: while your AI generates recommendations, have it simultaneously create a checklist to verify it met all the criteria. Built-in quality control that catches what LLMs typically miss.
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ICYMI: bulk operations by metadata dropped last month update, delete, fetch – all by metadata filters instead of IDs same syntax you're already using in queries catch up: https://t.co/WPnPFdMycs
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That's a wrap on AWS re:Invent 2025! 🔥 Huge thanks to everyone who stopped by our booth – especially those who stayed for the fire tricks. Turns out our demos weren't the only thing bringing the heat. If you missed us this week, download our AWS ebook to learn how Pinecone
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