Vector databases and RAG
Qdrant: review, pricing, data and alternatives
Qdrant lets teams working with vector databases store and index vectors. It combines semantic and keyword search, filters results using metadata, and provides collection management through a web interface. A free tier exists. Access and transparency score: 65/100, #6 of 7 in its category.
| Access | 15/30 | |
|---|---|---|
| Openness | 30/30 | |
| Trust | 20/40 |
What is Qdrant?
Qdrant lets teams working with vector databases store and index vectors. It combines semantic and keyword search, filters results using metadata, and provides collection management through a web interface.
According to its documentation, Qdrant lets you:
- store and index vectors
- search for content by similarity
- combine semantic search and keyword search
- filter results using metadata
- manage collections in a web interface
- back up and restore collections
We file it under Vector databases and RAG, the category of tools to connect your documents to an AI (RAG), and it also overlaps with developer tools and enterprise search. The category lists 7 verified tools on this site.
The publisher is Qdrant Solutions GmbH, based in Germany.
Who is it for?
It is mostly useful to these profiles: developers and data analysts. The need it covers is simple to state: connect your documents to an AI (RAG).
Qdrant is a good candidate if:
- you want to try before paying
- you plan to connect it to your tools or automations through its API
- you want to read the code or host it yourself
The best test is a concrete case from your daily work: if the tool does not beat your current method within a week, move on to an alternative.
How much does Qdrant cost?
Pricing model: open source (public code). Basic use costs nothing.
To estimate what the same work would cost done directly with an AI model, use our API price calculator. Prices change often: always check the official site before subscribing.
Data and privacy
According to its privacy policy, data is processed in several regions depending on the plan.
Exporting your content: yes (details on the official site). Being able to get your data back makes switching tools easier later.
Three habits apply to every AI tool:
- turn off the use of your content for training if the option exists
- use a work account separate from your personal account
- review everything the tool produces before publishing or sending it
Platforms, languages and API
Qdrant runs on: web browser and API.
Interface languages are not specified.
A public API lets you call it from your own programs or an automation tool, and plug it into an AI agent.
The source code is public: qdrantqdrant/qdrant.
How we scored it
Our 65/100 score does not judge output quality: it measures what can be publicly verified about access to the tool, its openness and how far the publisher can be trusted.
| Criterion | Points | What counts |
|---|---|---|
| Access | 15/30 | free tier, published price, French interface |
| Openness | 30/30 | API, data export, open code |
| Trust | 20/40 | identified publisher, declared hosting, age, presence in reference lists |
The full calculation is explained on our method page.
Comparison with alternatives
Qdrant against other tools in the vector databases and RAG category:
Verdict
Qdrant is a decent option; compare its price and terms with the category leaders.
Strengths
- Free tier to try it
- Public API
- Public source code
Alternatives
Categories and jobs
Learn more
Frequently asked questions
Is Qdrant free?
Yes, a free plan exists, with usage limits.
Where is Qdrant data stored?
According to the publisher, in several regions depending on the plan.
Does Qdrant have an API?
Yes, a public API is documented.
What are the alternatives to Qdrant?
In the same category: LlamaIndex, Pinecone, Weaviate, and Chroma.
Qdrant or LlamaIndex: which one to choose?
Our score is 65/100 for Qdrant and 95/100 for LlamaIndex. Try both on the same task before paying.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score