Pinecone

ICYMI: We launched bulk data operations by metadata this year 🎯 Update, delete, and fetch records using metadata filters – no ID collection needed. If you're already using metadata filters in queries, you know the syntax. Same filters, more power. This was one of our most requested features, and with the latest API updates, even more is now available. Catch up on what you missed: https://lnkd.in/gXCbGHrB

My very first AWS Re:Invent lightning talk with Pinecone is now live on YouTube! Please take a look and let me know what you think! #AWSreInvent #Pinecone #agenticrag https://lnkd.in/gBfaFP67

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 helps you build knowledgeable AI at scale: https://lnkd.in/gH7pB3NY See you next year!

Your AI will never get to production if your retrieval stack collapses at scale. Pinecone + Amazon Web Services (AWS) were built so engineering leaders don’t have to compromise — accuracy, performance, and cost efficiency all at once. See how teams like Gong, Vanguard, and Delphi are doing it and read the ebook in the comment below👇

We made hamming distance scans ~30% faster. I wrote the first C/C++ implementation of hamming similarity approximation for v0 Pinecone in 2019. It was pretty fast. A couple of years later we (finally) replaced it with Rust. That was faster. The Rust compiler optimized the SIMD instructions better than I did. We still use hamming distance scans in places and we thought it could not get any faster. We were wrong. We recently revisited that code to explicitly invoke AVX-512 intrinsics with loop unrolling. It’s now ~30% faster… Bits | Previous (ns) | Unrolled AVX512 (ns) | Throughput Improvement 512 | 2.02 | 1.49 | 1.35× 1024 | 3.04 | 2.11 | 1.44× 2048 | 5.64 | 4.48 | 1.26×

ICYMI: Dedicated Read Nodes are now in Public Preview Need predictable performance for high-throughput vector workloads? Pinecone's newest feature gives you reserved capacity with: ✓ Hourly per-node pricing for cost predictability  ✓ Warm data paths (memory + SSD) for consistent low latency  ✓ Linear scaling: add replicas for throughput, shards for storage Perfect for billion-vector semantic search, recommendation systems, and mission-critical AI services with strict SLOs. Choose what fits your workload: → On-Demand for bursty, elastic traffic → DRN for always-on, high-QPS applications Learn more about Dedicated Read Nodes: https://lnkd.in/g8b5WCTm

Standing room only at Arjun Patel's lightning talk yesterday at re:Invent! His take? We need Agentic RAG for complex queries that require real reasoning and planning. The example that hit home: trying to find a restaurant for a couple with totally conflicting dietary needs and prefernces (traditional RAG just can't handle it). The solution is agents that can decompose queries, dynamically pull from multiple sources, and iterate on outputs before returning results.   

Good Morning from Day 3 of Amazon Web Services (AWS) re:Invent! If you want to find out how Pinecone powers billion-scale vector search come check us out at Booth #534! Book a demo with us here: https://lnkd.in/g_iWEXfH

The energy at Amazon Web Services (AWS) re:Invent is electric this year! It’s inspiring to spend time with the builders of the future: customers pushing the boundaries of AI, partners bringing innovative ideas to life, and developers turning ambitious concepts into real-world systems. I am incredibly proud to be here with the Pinecone team. In just a few months, I’ve seen the team's deep commitment to solving real problems for builders. This week is particularly significant for us. We are announcing Dedicated Read Nodes (DRN) in Public Preview! This represents a major step forward, fundamentally changing the economics of AI and Agentic apps at scale. DRN is a new deployment mode for our unique serverless “slab” architecture, optimized for workloads involving millions to multi-billions of vectors and predictable query rates. By combining DRN with our existing on-demand options, we ensure customers achieve the optimal cost-performance balance for any AI use case—from RAG and agents to search and recommenders. The next wave of knowledgeable AI is here, and Pinecone is purpose-built to support it. I look forward to the conversations ahead this week and all that we will continue to build with our community. See the full details on the DRN announcement here. 👇 #AWSreInvent #Pinecone #VectorDatabase #AI #GenerativeAI #RAG #AgenticAI Dedicated Read Nodes Announcement blog: https://lnkd.in/e72WDWh5

Pinecone's slab architecture delivers: ✓ Write data, query it instantly—no waiting for reindexing  ✓ Scale from thousands to billions of vectors without performance cliffs  ✓ High-accuracy retrieval that automatically optimizes as your data grows Two deployment options: On-demand → Elastic scaling for variable workloads. Pay for what you use. Dedicated Read Nodes → Isolated capacity with guaranteed low latency for sustained high-QPS applications. The sophistication is in the engineering. The simplicity is in the experience.