Qdrant buying intent

Today we publicly release Qdrant Skills, along with qcloud-cli - a CLI for managing Qdrant Cloud. Why did it take us some time and we didn't rush to release skills with the first wave of hype? It took us some time to 1. understand what skills really are good for 2. prepare all the tooling, so agents won't need to burn tokens on huge markdowns So, here is a breakdown: Skills are not just another form of documentation. We already have documentation and it is even in markdown format already, so why not just convert all our tutorials into skills? This was our first instinct, but we quickly realized that it is not going to work. Tutorials are structured for granular understanding of a topic by humans, but agents don't need to be coached on what semantic search is. Agents need to be navigated to the exact spot in the documentation where the answer to their question is. After realizing this, we had to solve a few challenges: - How to structure skills in a way that would be useful for a "person" who is actually maintaining Qdrant, and not learning basic concepts of vector search? - How to point agents to specific places in the documentation without polluting the context? - How to make interactions with Qdrant platform more agent-friendly? And I think we have good answers to these questions now. - Our skills are structured as a handbook of a Solution Architect working on Qdrant. - We built a dedicated service which only responds with the exact section of a markdown document an agent requests - We have a special snippet-lookup skill which only returns relevant snippets for the specific SDK an agent asks about - We built qcloud-cli - a tool we are already using in internal scripting - We got rid of all the "Build a multimodal Retrieval-Augmented Generation (RAG) system" fluff agents don't really care about Give it a try and let us know what you think!

And again, Vector Search is not only for RAG. Meet the Molecular Similarity Search system using ChemBERTa, RDKit, and Qdrant engine. The idea is simple: Feed SMILES ((Simplified Molecular Input Line Entry System) strings into a transformer and get a meaningful vector representation to run a search on the resulting embeddings to recognize similar patterns in molecule structure. Details on Towards AI, Inc. https://lnkd.in/dUad8PrA

Wrapped up LangChain Interrupt yesterday and one thing in particular really stood out. The conversation around agents is getting much more operational. Less: “look at this cool thing the model can do” More: “How do you actually run this stuff in production?” A lot of discussion around: • identity • permissions • orchestration • evals • memory • governance • tool reliability • context management Cool seeing Arcade.dev announced by Harrison Chase during the keynote as the MCP/runtime layer powering tool access inside LangSmith Fleet. Also got to catch up with Brace Sproul and talk about how agentic workflows are changing everything from GTM to customer support. Feels like the industry is starting to move from isolated agent demos toward actual operational systems. Looking forward to seeing where things are by next Interrupt!

We have chosen Qdrant as a vector DB to give a context.

FAISS vs Qdrant: The Brutal Scaling Reality | by varun rao | Jun, 2026 | Medium Sign up 1. What These Tools Actually Are 2. Why FAISS Feels Incredible at First 3. The Prototype to Production Gap 4. Performance: Where the Nuance Lives 5. Metadata Filtering: The Real Dividing Line 6. Operational Complexity and What It Actually Costs 7. Why Benchmarks Mislead Engineers 8. Who Should Use What 9. The H

MediumPinecone vs Weaviate vs Qdrant vs Milvus | by Paolo Perrone | Data Science Collective | Medium Sign up ## Data Science Collective Advice, insights, and ideas from the Medium data science community Technology Artificial Intelligence Machine Learning Software Development Data Science # Pinecone vs Weaviate vs Qdrant vs Milvus ## Where do you put a billion vectors? 16 min read May 7, 2026 -- 1

ChromaDB vs Qdrant vs Weaviate vs pgvector: vector database shootout 2026 - DEV Community Posted on May 28 # ChromaDB vs Qdrant vs Weaviate vs pgvector: vector database shootout 2026 Every RAG pipeline I've reviewed this year hits the same decision point: which vector store do you actually ship? The wrong choice compounds — it shapes your architecture, your operational overhead, and how painful a