We have a long history with MongoDB. Many years ago, when I was still writing code myself, NoSQL was a novel concept. I was sold on its simplicity and natural compatibility with the Node stack. I used Mongo for all my side projects. For work, we still preferred SQL DBs, though. Fast forward to the early days of Qdrant, MongoDB Ventures reached out, and we had several conversations; they considered investing in our seed round. Their product managers and engineers were reaching out and praising our solution. At some point, they revealed that they are going to add vectors to their Atlas search product just by using Lucene. So what, who cares? At Qdrant, we’re not retrofitting anything. We’re building a dedicated vector search engine - from the ground up. If someone wants to replicate what we do? Go ahead, we are #opensource. But here’s where it gets frustrating. We have put in the hard work building our community and attracting prominent people in the AI landscape to use our product and create content about it. We have built our Qdrant Stars program to engage with the community even closely. qdrant.tech/stars So, what does it have to do with Mongo? Well, they just made a clever shortcut and reached out to our Qdrant Stars program members and influencers from our community, making them an offer they could not decline. I do not blame the content creators, it is their daily work and it is an excellent opportunity for them, guys, I can totally understand you, no worries. But let's call it what it is: A multi-billion-dollar company is now trying to buy its way into a community built by an early-stage startup, not them. We don’t fear competition. But when the incumbents start copying your strategy and poaching your community, it says one thing loud and clear: We’re making the right people nervous. We’ll never outspend Mongo, and we don’t need to. We have something they can’t copy or buy: a fast-growing, mission-driven, technically superior open platform - and a community that sees through the noise. So to the engineers, researchers, and creators who believe in open innovation, performance that doesn’t compromise, and communities that are earned, not acquired: you know where to find us. And to the MongoDB marketing geniuses: take on Elastic, take on Redis, take on Oracle, and others of your calibre, but leave Qdrant and other startups like us alone. | 61 comments on LinkedIn
Thoughts on Algolia (vs Solr & Elasticsearch) - OpenSource Connections
In this blog, we discuss our thoughts on Algolia versus Solr and Elasticsearch.
How Notion Cut Millions from Their Vector DB Bill Without Sacrificing Search Quality
The Marketing Behind MongoDB
Countless NoSQL databases competed to be the database of choice. MongoDB's marketing strategy helped it become the winner.
Keynote: The Death of the Browser - Rachel-Lee Nabors, AgentQL
The Great Open Source Shake-up
The past year and a half has seen a number of open source and open source-ish companies like Elastic, Confluent, and MongoDB change licenses on certain products, moving away from traditional open s…
Wikidata Launches Free Vector Database as Open Alternative to Closed AI Systems
Wikidata has launched something big.
AWS, MongoDB, and the Economic Realities of Open Source
Amazon’s latest offering highlights the economic challenges facing open source companies — and Amazon should pay attention.
Open Source or Open Core: Why Should You Care?
Open source software has gained its significant foothold because the community development model works everywhere. Its momentum has moved it into the underlying infrastructure of some of the most sophisticated web scale companies, like Facebook and Google. Open source underpinnings now drive many of the most significant advances in the worlds of cloud and big data.
Open Source or Open Core: Why Should You Care?
Open source software has gained its significant foothold because the community development model works everywhere. Its momentum has moved it into the underlying infrastructure of some of the most sophisticated web scale companies, like Facebook and Google. Open source underpinnings now drive many of the most significant advances in the worlds of cloud and big data.
Is Misinformation More Open? A Study of robots.txt Gatekeeping on the Web
Web scraping, the automated process of extracting information from websites, has long played a foundational role in the Internet ecosystem (Gray, 1995). It supports services such as search engine indexing, price comparison tools, and competitive intelligence. More recently, it has become a core component in the development of large-scale generative AI models. These Large Language Models (LLMs) require enormous volumes of training data, often in the terabyte range (Kaplan et al., 2020; Lehane, 2025), and the public web remains a low-cost, attractive source. Major model developers, including those behind OpenAI’s Chat-GPT (OpenAI, 2025), Google’s Bard (now known as Gemini) & Vertex AI (Romain, Danielle, 2023), and Anthropic’s Claude (Romain, Danielle, 2025), openly acknowledge the use of web scraping to construct their training corpora (Abdin et al., 2024; Brown et al., 2020; Chowdhery et al., 2023; Grattafiori et al., 2024; Team et al., 2024; Touvron et al., 2023).
Leaving technical preview | GraphQL
After over a year of being open sourced we’re bringing GraphQL out of “technical preview” and relaunching graphql.org.
Renaissance Philanthropy Fellow: Dario Taraborelli
Lacuna: A Research Map for Machine Learning
Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research directions, and research proposals. Each item keeps links to the primary source records and papers that support it. We release the map with web, markdown, and MCP interfaces. Across LitSearch, Multi-XScience-CS/ML, and ScholarQA-CS-ML, Lacuna outperforms OpenScholar with the strongest gains on LitSearch retrieval (Recall@10 0.538 vs. 0.424 for OpenScholar v3). We also evaluate Lacuna Deep Research, a multi-stage report agent over the map, on 25 ReportBench-ML survey tasks: Lacuna Deep Research reaches 0.052 citation F1, 0.339 citation precision, 99 expert-reference hits, and 7.82/10 RACE report quality, while GPT-Researcher reaches 0.039 F1, 0.290 precision, 72 hits, and 5.24/10 RACE.
Episode 1 - Mongo DB Is Web Scale