Pinecone

Every time a team says "performance regressed," one question should come first: which type? Infrastructure regressions are traditional DevOps problems — high latency, dropped throughput. Fix with monitoring and scaling. Result quality regressions require eval frameworks. Lower accuracy, more hallucinations. Fix by testing changes across the retrieval pipeline. Teams get stuck debugging latency when the real problem is chunking strategy. Or they overhaul retrieval when the issue is infrastructure capacity. Different scoreboards, different debugging approaches. Mixing them wastes time and ships broken products. https://lnkd.in/g7SpqvsE

Come build with Pinecone Join us to build, break, and learn together. Each session, we pick up a real project — building RAG pipelines, wiring up agents, building semantic search, or exploring what's possible with tools like Claude Code, Cursor, Gemini CLI, or n8n — and work through it live. We talk concepts, hit real challenges, find solutions, and take questions from chat as we go. Topics drop a couple days before each stream, so keep an eye out. Whether you're just getting started or deep in production, pull up a chair and build with us.

12 nodes. 3 API keys. Decisions you're probably second-guessing. That's what a typical RAG pipeline in n8n used to look like. With the new Pinecone Assistant node, your entire pipeline — chunking, embedding, search, reranking — is handled automatically. What used to take 12+ nodes now takes 3. This video from Cole Medin shows exactly how it works: upload docs with a Google Drive trigger (Slack, GitHub, or webhooks work too), query them through an AI Agent node, and get back grounded answers with cited sources. No embedding model to configure. No text splitter to fiddle with. No silent retrieval failures because you swapped models. Try out the new Pinecone Assistant node for n8n (link in the comments)👇

Vector databases have a freshness problem. Write a vector, query for it—will it show up immediately? #Pinecone makes writes queryable within seconds: A. Writes never wait for indexing Data lands in a memtable (in-memory buffer) and returns immediately. The memtable gets checked on every query. If your vector is there, it's found. B. Queries span everything Every query fans out to all slabs plus the memtable. We don't route to "the right place"—we search everywhere and merge results. New writes are immediately visible. C. Memtables flush continuously Periodically, memtables flush to small L0 slabs with lightweight indexing. Because queries already fan out, newly flushed slabs are immediately included. No coordination needed. D. Background compaction catches up L0→L1→L2 merging happens in the background while queries and writes continue normally. We separated the timeline of writes from optimization. Writes happen now. Indexing happens in the background. Queries see everything regardless of which stage it's in.

In the world of AI, we spend a lot of time talking about innovation. But according to Oded Sagie, SVP of Product & R&D at our customer Aquant, the real goal is to become "boringly reliable". We agree. When Aquant partnered with Microsoft Azure and us, they weren't just looking for features; they were looking for a foundation they could forget about. Why boring is the benchmark for success: ✅ Cognitive Offload: When your stack is boringly reliable, your engineering team stops firefighting and starts building the product features that drive adoption. ✅ User Trust: AI creates value only when users stop questioning the "how" and start relying on the "what" in their daily work. ✅ Scalability: You can’t scale a system that requires constant manual intervention. Reliability is the prerequisite for growth. If your customers (and your engineers) are still thinking about the technology, you haven’t reached maturity yet. True value is realized when the tech fades into the background. Watch the full discussion between Oded, Pinecone Senior Director of Field Engineering, Perry Krug, and Microsoft host James Caton: https://lnkd.in/g4Xbb4CT.

Come build with Pinecone Join us to build, break, and learn together. Each session, we pick up a real project — building RAG pipelines, wiring up agents, building semantic search, or exploring what's possible with tools like Claude Code, Cursor, Gemini CLI, or n8n — and work through it live. We talk concepts, hit real challenges, find solutions, and take questions from chat as we go. Topics drop a couple days before each stream, so keep an eye out. Whether you're just getting started or deep in production, pull up a chair and build with us.

From 20% accuracy to a 5x increase in revenue-generating features. 🚀 When your core feature is "blocked," your revenue is stalled. Leading food tech platform Allspice had a vision: Allow users to import recipes and instantly match ingredients to a structured database. But with 20% matching accuracy, the feature was un-launchable. The Bottleneck: Traditional search wasn't just a technical "bug"—it was a business blocker. It stalled: ❌ Publisher revenue ❌ Platform growth ❌ B2B revenue streams (grocery exports & affiliate commissions) The Breakthrough: Using Pinecone’s managed, serverless infrastructure, the team validated a working pipeline in one afternoon. They didn't spend months building infrastructure; they spent days shipping value. By reaching 97% accuracy, they unlocked: ✅ New B2B revenue streams ✅ Increased time on site via better engagement ✅ Production-ready recipe importing In the AI era, "Time to Validate" is critical. Choosing the right managed infrastructure allows your team to focus on the product logic that drives revenue, rather than the database maintenance that drains resources. 📖 Check out the case study: https://lnkd.in/gmKtFJ4W

🔴 We're going live tomorrow for another Come build with Pinecone session! We'll center around building with Claude Code + Pinecone, but as with every technical conversation, we'll head off on some fun tangents (Gemini Embedding 2, robots, AR). Join Jenna Pederson, Arjun Patel, and Roie Schwaber-Cohen to build, chat, and get your questions answered live. You can find us right here: 🗓️ Wednesday, March 25, 2026 ⏰ 10am PT / 1pm ET / 5pm GMT 💼 https://lnkd.in/ghh7YmmS Drop your questions in the comments or bring them to the stream.

A team cut their dataset by 40%. Throughput improved. Cost per query fell. The infra team celebrated the win. Then they ran quality evals. The new version underperformed. They had pruned the exact data chunks the system needed to return relevant answers. Fast index. Wrong answers. Without measuring both scoreboards independently, there was no way to see the tradeoff coming. Swipe for the full breakdown. https://lnkd.in/g7SpqvsE