Weaviate

Europe’s AI scene is heating up 🔥 We’re honored to be named one of Europe’s most promising AI startups for 2025 in Sifted’s first AI 100, backed by N47. A big thank you to our team and community for helping make this happen. 💚 Check out the full report and see Europe’s rising AI stars: https://lnkd.in/gZnEB97

We had such a great time at the AWS Startup Partner Summit 2025! 🎉 Jobi George explains how Weaviate integrates with Amazon Web Services (AWS) Bedrock and SageMaker to simplify deploying advanced AI solutions, including hybrid search, RAG, and agentic AI applications. Huge thanks to the AWS team for the energy and all the great conversations 💚 Learn more about our partnership here: https://lnkd.in/drY2WDsK #StartupPartnerSummit2025

Real-time, context-aware agents are here ⚡️ Confluent and Weaviate are helping developers build intelligent, event-driven AI systems that reason and act on live data — not stale snapshots. Learn how Streaming Agents on Confluent Cloud and Weaviate’s vector database work together to bring real-time context and semantic understanding to agentic AI. Read the blog: https://lnkd.in/dwCpWi9m

Big update from the Weaviate Cloud team! We’ve rolled out a 𝗻𝗲𝘄 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 to make it easier to understand what you’re paying for and ensure pricing scales with how you actually use the platform - no matter if you're testing ideas or running production workloads: Here's what's new: - New names: Serverless Cloud → 𝗦𝗵𝗮𝗿𝗲𝗱 𝗖𝗹𝗼𝘂𝗱, Enterprise Cloud → 𝗗𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗖𝗹𝗼𝘂𝗱 - New pricing dimensions: 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀, 𝘀𝘁𝗼𝗿𝗮𝗴𝗲, 𝗯𝗮𝗰𝗸𝘂𝗽𝘀 - New plans: 𝗙𝗹𝗲𝘅, 𝗣𝗹𝘂𝘀, and 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 - Real-time cost visibility with a new pricing calculator The result: transparent, predictable pricing that scales with your actual usage 💚 🔬 Read the full breakdown in our launch blog: https://lnkd.in/dpcS_5ev 🔖 See the details on our pricing page: https://lnkd.in/dAwrSyaS

We’re excited to join the upcoming n8n Business Lab Rhein-Main, hosted by n8n, AOE, and Avanai on November 13th in Wiesbaden, Germany! This event brings together business leaders, automation professionals, and innovators to explore the power of workflow automation for enterprises. What’s on the agenda: 🧠 Morning: Hands-on Masterclasses for all skill levels 🎤 Afternoon: Inspiring talks, real business cases & networking across the Rhein-Main region Seats are limited to 100 (invite or request only). Apply here: https://n8n-rheinmain.de

This one sheet could straight-up 10x your vector search game. From spinning up the Weaviate Typescript client connection to running hybrid search, inserts, and managing collections… this sheet got it all! Perfect for devs engineering advanced search tools, agents, RAG pipelines, or simply want to dodge constant doc hunts. So, save it, use it (or even print it). But yeah, keep the full docs handy too… just in case: https://lnkd.in/ewJwAtYf

Your RAG is probably committing fraud. Not “hallucinating”. Not "confused". Committing fraud, because you asked a multi-step question, and it did a single blind vector search, grabbed the Top-5 nearest neighbors, and called it a day. Your query "affordable eco-friendly smartphones under $500 with high ratings" returns phones that mention "eco-friendly" in some or the other way, but cost $800, or cheap phones with 2-star reviews, because the RAG pipeline never filtered, aggregated, or reasoned… It just nearest-neighbor'd its way to mediocre results. This is the difference between naive vector retrieval and agentic retrieval: • 𝗡𝗮𝗶𝘃𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: encode query → top-k results → dump into LLM context → done. • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: decompose the query → route to & search multiple collections → apply required filters (price < $500) → aggregate (average rating > 4) → generate a grounded, cited answer. Query Agent does exactly this. It's a pre-built agentic service that analyzes your natural language query, understands collection schemas, determines which searches to run (semantic, aggregations, or both), queries multiple data collections in parallel, and synthesizes and re-ranks results, along with returning the relevant objects. You don’t even need to do any manual setup gymnastics, and the Query Agent just figures out its way to the perfect response. Want to try it out yourself? It's generally available now in Weaviate Cloud (free sandbox included) and can be set up in minutes with your data: https://lnkd.in/esk_26qF

I'm incredibly excited to be hosting an Enterprise AI Agents Hack Day at Amazon Web Services (AWS) Builder Loft in San Francisco tomorrow. Weaviate is obviously the vector search memory layer of choice for your enterprise AI Agents. LlamaIndex will be doing an exciting product launch for a nice new swagger tool that lets you build your AI Agents with ease in a visual editor. And FriendliAI is here to give you the power of fast and cheap inference with any open source model you can think of on Hugging Face which includes beefy highly performant models like GLM 4.6 on NVIDIA Blackwell Chips! What are we doing exactly? We're building AI Agents for enterprise use cases that are going to disrupt the landscape of tech in various industries from healthcare intake forms & FAQ agents to Q&A Agents for HR and Internal Tools and even general enterprise intelligence platforms to empower people to do their best work. The morning starts out with a few short workshops showcasing the power behind LlamaIndex, CrewAI, and Weaviate. We build. And then we come back together for demos at the end of the day. This is going to be a super fun event, with almost $2000 in prizes, and we've also got a great line up of judges who are YC Start Up founders, a data scientist from OpenAI, and more! https://lnkd.in/gmUwuYMF Grateful for the teams and people supporting this developer engagement and building for use cases that shape the future of how enterprises do business. Tuana Çelik Alexander Campos Sang Won Lee Bob van Luijt Philip Vollet Byung-Gon Chun Edward Schmuhl Scott Askinosie M.S. Ph.D. Yunmo Koo Jerry Liu Tony Le Jobi George Darren Hsieh Oliane Piana Omar Valle