Qdrant Academy Launches with Qdrant Essentials Course
Neil Kanungo
·October 23, 2025
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Today, we’re proud to launch Qdrant Academy, a new learning site designed to help developers, data scientists, and engineers build real-world vector search systems.
This started with a mission: to make learning more accessible, scalable, and frictionless for practitioners around the world. And today, we crossed the first milestone in our mission by launching our first course, Qdrant Essentials.
With Qdrant Essentials, you get a free, self-paced, structured learning course that teaches the fundamentals of vector search, embeddings, and productionalizing AI systems using Qdrant. You’ll learn not just what vector search is, but how to build, query, and optimize search with real projects, exercises, and examples.
Why Take the Essentials Course
Vector search has become a core part of modern AI systems, from retrieval-augmented generation (RAG) and semantic search to recommendation engines and multimodal retrieval.
But implementing it correctly takes more than just embeddings. You need to understand indexing, filtering, hybrid search, and scaling.
Qdrant Essentials helps you bridge the gap between theory and practice. You’ll learn how to:
- Store and query embeddings efficiently
- Combine filters and vector search for precise results.
- Optimize for performance at scale.
- Apply these concepts to real-world use cases.
This course helps developers by:
- Reducing onboarding time for new team members.
- Improving search architecture and embedding design quality.
- Accelerating time-to-value for AI projects.
- Prepares teams for scalable, production-ready retrieval systems.
We believe that learning comes in many forms, and sometimes a comprehensive end-to-end structure is exactly what you need. The Qdrant Essentials Course seeks to do exactly that, giving you robust beginner-to-intermediate knowledge of the Qdrant platform.
About the Course
Qdrant Essentials is designed as a hands-on learning experience that you can take at your own pace. Each module is organized into “Days” and includes videos, step-by-step guides, code examples, and exercises that build on each other.
What You’ll Learn:
- Vector Fundamentals: How embeddings, similarity metrics, and chunking works.
- Indexing & Performance: Indexing fundamentals, filtering, and tuning
- Hybrid Search: Sparse vectors, inverted indexes, score fusion, and more
- Optimization & Scaling: Vector quantizations, rescoring, high throughput ingestion
- Advanced APIs: Multivectors and late interaction models, the Universal Query API
- Real-World Use Cases: Apply what you learn to build a search engine
Every concept is paired with video walkthroughs and real code examples so you can understand and feel vector search in action.
Ecosystem Partners
We’re proud to offer a growing list of collaborative lessons from our partner ecosystems integrations. These videos have been created directly by experts from each partner organization, illustrating on how to use specific technologies with Qdrant.
Our current partner content tutorials include:
We’re super grateful for our amazing and collaborative partners for contributing to the Qdrant Essentials Course and Qdrant Academy.
How to Get Started
Getting started with Qdrant Essentials is simple:
- Go to the Qdrant Essentials course page.
- Create a free Qdrant Cloud account and spin up a free tier cluster.
- Follow the guided lessons: each includes code samples, exercises, and practical checkpoints.
- Share what you build with in our Discord Community
- Come back and get certified!
The only prerequisite: curiosity. Everything else, from setup to vector search, you’ll learn as you go.
And That’s Not All…
Qdrant Essentials is only the first step in our broader Qdrant Academy. Visit the Academy site to see an overview of future courses we are planning, and register your interest in courses you think we should prioritize.
Don’t see something you think is important? Email devrel@qdrant.com and we’ll consider your request!
Special Thanks
A huge thank-you to everyone who helped make Qdrant Academy and Qdrant Essentials possible!
A special shoutout to Sabrina Aquino for spearheading the entire Essentials project, and to Evgeniya Sukhodolskaya and Kirstin Taufertshoefer for taking over the project later on. Shoutouts to the entire Qdrant DevRel Team for the content, Andrey Vasnetsov for the Academy site, and to our amazing partner contributors who made this course possible.
And as always, thank you for being part of our community. Your feedback and enthusiasm continue to shape how we bring vector search to the world.
