Pinecone

IYDKNYK If you're building RAG or autonomous agents on @awscloud, you know the struggle: balancing speed, freshness, and scale without drowning in infrastructure management. That is exactly why we built Pinecone with a serverless architecture. In this video, we break down how our "slab" architecture transforms proprietary data into trusted AI. 👇 The Core Architecture: We separated storage from compute. By using Amazon S3 for cost-effective storage and intelligent caching for retrieval, we created a system that writes data to immutable files called "slabs." The Result? Instant availability and durability. True Serverless Scaling: Forget manual sharding. Our system handles writes in parallel and scales elastically. Whether you are indexing 1k vectors or 1B+, the system adapts without blocking queries. Which Deployment Fits You? There are two distinct paths depending on your workload: 1️⃣ On-Demand Best for: RAG agents and bursty traffic. How it works: Usage-based pricing with intelligent caching. Why use it: You have millions of namespaces and need flexibility. 2️⃣ Dedicated Read Nodes Best for: Recommendation engines and high-QPS search. How it works: Isolated infrastructure where data is always warm in memory. Why use it: You have strict SLOs and operate at a billion-vector scale. Check our our e-book with @AWS_Partners that explains exactly how to achieve zero-infrastructure management while maintaining data freshness: pinecone.io/learn/aws-eboo…

🔗 pinecone.io/blog/byoc/

ICYMI Pinecone BYOC is available in public preview. 🔒 Security review is where production AI goes to die. If “no vendor access” is the bar, managed services won’t pass. Self-hosting passes, but the ops get dumped on you. 🚀 Pinecone BYOC runs Pinecone in your AWS, GCP, or Azure account with a zero-access operating model: ✓ Vectors + queries stay in your VPC ✓ Outbound-only, pull-based ops ✓ Auditable ops via k8s resources ✓ Public endpoint or private-only via AWS PrivateLink/GCP PSC/Azure Private Link ✓ Same managed Pinecone workflow you're used to

Most AI assistants fail at retrieval, not generation. I added Pinecone semantic search to my business database and Gemini Embeddings 2 "What did I promise this client?" now actually works. 200 lines of TypeScript. Free Pinecone tier. The Memory Layer.

There are two @Pinecone nodes in @n8n_io, and picking the wrong one for your situation costs real time. The Pinecone Assistant node is a managed RAG pipeline. Chunking, embedding, query planning, reranking — handled automatically. You add documents, query the assistant, get context back. Two nodes instead of five-plus. Good default for knowledge search use cases like customer support, internal docs, FAQ systems or where you don't want to manage it yourself. The Pinecone Vector Store node is full pipeline control. You choose the chunking strategy, the embedding model, the reranker and configure nodes and API keys for each. More flexibility, more to maintain, more to debug. Worth it when the pipeline details genuinely matter for your use case. The question that separates them: do you need custom control over chunking, embeddings, or retrieval? For most knowledge search use cases in n8n, the Pinecone Assistant node gets you to a working system faster — and Pinecone keeps improving it automatically. The Pinecone Vector Store node makes sense when the pipeline details genuinely affect your outcomes: specialized content with unusual retrieval needs (legal documents, multilingual content, technical docs with code), a specific embedding model required for your domain or for compliance, or advanced techniques like hybrid search. Full breakdown of when each node is the right call — including the decision framework — on the blog. pinecone.io/learn/pinecone…

Building AI for B2B? Trust is your biggest scaling factor. 🔐 Senior VP of Product and R&D, Oded Sagie, at our customer @Aquant_ai breaks down the lessons learned from scaling AI with @Microsoft and Pinecone: 📍 Governance ≠ Friction: Data boundaries and accountability are what allow enterprises to say "Yes" to your product. 📍 Cost Management: Infrastructure choices made on Day 1 compound. Don't get trapped in a high-cost stack as usage grows. 📍 Build vs. Buy vs. Blend: Choose your battles. Spend your engineering "heroics" on your core value, not the plumbing. Full episode of the discussion between Oded, our Sr. Dir. of Field Eng, Perry Krug, and Microsoft Generate Now! podcast host James Caton: youtube.com/watch?v=P8yusC….

Most builders don't get stuck on the model — they get stuck on the pipeline. There's a version of you that ships a RAG-powered product this week. There's another version that's still debating chunking strategies and embedding models. Every week spent on those debates is a week you're not in production. The Pinecone Assistant node in n8n exists to make you the first version. We wrote a guide breaking down exactly when to use each of the Pinecone n8n nodes, and the decision is simpler than you think: Pinecone Assistant node → managed RAG pipeline. 1-2 nodes, one API key, production-ready without becoming a RAG expert. Pinecone Vector Store node → full pipeline control. 5+ nodes, maximum flexibility, real tradeoffs. ✅ Assistant node is right for: building straightforward document search where the complexity of managing chunking strategies and embeddings isn't adding value to your use case, or you need to get up and running quickly. ✅ Vector Store node is right for: specialized scenarios where the details of the retrieval pipeline matter — you need control over embedding models, customized chunking strategies, hybrid search, or multi-lingual content. Here's the mental model: the Pinecone Assistant node is a building block. The Pinecone Vector Store node is a pipeline you own and maintain. For most builders, the question isn't which node is more powerful. It's which one gets you shipping. The Pinecone Assistant n8n node is the faster path to your first AI application in production, without the pipeline complexity. What are you building with n8n + Pinecone? Let us know in the replies 👇 pinecone.io/learn/pinecone…

The difference between an API connector and an AI automation engineer? Context. 🧠 Good look at how @n8n_io + GPT-4 + Pinecone can turn a basic lead form into a fully-briefed, ready-to-send outreach machine. Nice work, @DivTalent !

An LLM is a reasoning engine, NOT a knowledge base. It knows how the world works, but it doesn't know YOUR contracts, specs, or policies. The RAG Fix: ✅ Grounding: Tie the model to actual facts. ✅ Accuracy: Stop hallucinations before they start. ✅ Context: Turn a genius mind into a knowledgeable expert. "An LLM without a vector database is like a genius with short-term amnesia." 🧠💨 Watch the full DM Radio episode with our CEO @ashashutosh and host @eric_kavanagh here: youtube.com/watch?v=cuDj53…

One knowledge base for multiple domains = your AI confidently giving the wrong answer. The fix: a multi-domain RAG system with Pinecone Assistant + @n8n_io: - Separate Assistants per domain (think property, franchise, client, or context) - Semantic search bridges human language and your docs - Routing logic to direct queries to the right Assistant Check out our step-by-step tutorial and an n8n workflow 👉 🔗 blog.n8n.io/build-multi-do…

Jenna Pederson (@jennapederson) and the n8n team (@n8n_io) show how to build multi-domain RAG systems with specialized knowledge bases using Pinecone for smarter AI assistants.(10/21) bit.ly/4upvimd

Prototyping AI is like cooking at home. 🍳 It’s okay if it’s just "decent." But if you’re charging customers, they expect a restaurant-quality experience. Our Sr. Dir. of Field Eng Perry Krug breaks down why "Restaurant Quality" AI requires a different mindset: ✅ Prototyping: Open-source libs & DIY hooks. 🚀 Production: Reliability, service quality, & scale. "When you need something you’re going to build your business around... pay someone else to focus on their core competencies." Don't be the chef, the dishwasher, and the landlord at the same time. Focus on your code. 🛠️ Full episode of the discussion between Aquant Senior VP of Product and R&D, Oded Sagie, Perry, and @Microsoft Generate Now! podcast host James Caton: youtube.com/watch?v=P8yusC….

Semantic similarity ≠ Relevance. 🚩 You can have a document that’s "close in meaning" but totally useless for the user's query. Senior Developer Advocate Arjun Patel explains the retrieval workflow every AI engineer should be using: 1️⃣ Retrieve: Use a vector DB to grab the top 100-200 candidates (fast). 2️⃣ Rerank: Use a cross-encoder to score those 100 for actual relevance (precise). 3️⃣ Deliver: Pass the top 10 to your LLM. The result? Massive jump in precision without re-indexing your entire vector store. 🛠️ Check out the full conversation with @MikeBirdTech on @ToolUsePodcast that covers sparse/dense embeddings, reranking, Pinecone’s Claude Code plugin, and more: youtube.com/watch?v=36FDCi…

The "POC Trap" is real. 🪤 Oded Sagie, SVP of Product and R&D at our customer @Aquant_ai explains why so many AI projects die after a successful demo. It’s the "Silent Tax" of building DIY infra. You aren't just building a feature; you’re signing up for: 🔹 Uptime 🔹 Security 🔹 Compliance 🔹 Endless fine-tuning "It doesn't show up on day one, but it compounds quietly over time." 📉 Don't let maintenance kill your innovation velocity. Use experts like Pinecone for the heavy lifting so you can focus on shipping what actually matters. Full episode of the discussion between Oded, Pinecone Senior Director of Field Engineering, Perry Krug, and @Microsoft Generate Now! podcast host James Caton: youtube.com/watch?v=P8yusC….

Build smarter, not harder. You don't need to spin up new vector indexes for every user. If you're building a multi-tenant RAG app, you need to master namespaces. Senior Developer Advocate Arjun Patel explains why you should partition your index to scale: 🔹 Isolation: User A queries User A's data. Period. 🔹 Efficiency: No more "server sprawl." One index, thousands of tenants. 🔹 Performance: Pinecone scales writes/queries independently so latency stays low. Check out the full conversation with @MikeBirdTech on @ToolUsePodcast that covers sparse/dense embeddings, reranking, Pinecone’s Claude Code plugin, and more: youtube.com/watch?v=36FDCi…

Built Janitor: safe garbage collection for Pinecone's immutable blob storage Identify → verify → delete with full auditability 3 deletion modes for different failure scenarios Property-based tests compress 30-day windows into seconds 90% storage cost reduction in 18 months

The "Homer Simpson" AI Strategy. 🏡🌳 Why did @Aquant_ai SVP Oded Sagie move his team to Pinecone? Because he wanted his vector search to "fade into the background." The reality of DIY vector infra: ❌ Constant index tuning ❌ Latency firefighting ❌ Infrastructure "heroics" The goal of the "invisible" stack: ✅ Managed scaling ✅ Boring reliability ✅ 100% focus on shipping GenAI value "When that happened, the teams could focus on anything else that we've been working on." If your database is the main character of your sprint, you're doing it wrong. Let the infra disappear. 🕵️‍♂️ Full episode of the discussion between Oded, Pinecone Senior Director of Field Engineering, Perry Krug, and @Microsoft Generate Now! podcast host James Caton: youtube.com/watch?v=P8yusC….

4,500 startups build AI on @pinecone . We need someone to supercharge that. We're hiring a Staff Developer Advocate in SF: own the community, run hackathons, build demos, and help the fastest-moving AI startups in the world scale on @pinecone . This isn't a sidelines role. It's the arena. You know what to do → pinecone.io/careers/4433b3… #Pinecone #Hiring #SanFrancisco #SF #AI #AgenticAI #TrustedKnowledgeInfra

The age of AI agents is here. They need memory. They need knowledge. They need @pinecone And @pinecone needs you, the ace builders. We built the vector database category. Now we're building the knowledge infrastructure for the agentic era. We're hiring 10 roles across R&D, Product, Marketing & GTM — from Senior Research Scientists to Staff Engineers to Product leaders. If you want to shape how AI agents think, remember, and know → pinecone.io/careers #AgenticAI #Hiring #Pinecone #TrustedKnowledgeInfra

Stop over-complicating your RAG chunking. 🛑 If you’re spending days on custom agentic chunking logic before trying a simple paragraph split, you’re doing it wrong. Senior Developer Advocate Arjun Patel breaks down the "Keep It Simple" framework for data prep: 📍 Wikipedia? Paragraphs. 📍 Textbooks? Sub-chapters. 📍 Multimodal PDFs? Page-by-page. Check out the full conversation with @MikeBirdTech on @ToolUsePodcast that covers sparse/dense embeddings, reranking, Pinecone’s Claude Code plugin, and more: youtube.com/watch?v=36FDCi…