mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-29 16:08:32 +02:00
Andrey Vasnetsov
cae8456ce1
* use relative links instead of absolute * add trailing slashes to avoid 301 redirect * add trailing slashes to avoid 301 redirect * make link checker unhappy with local redirects * test if ci fails (should fail) * rollback: test if ci fails (should fail)
74 lines
4.2 KiB
Markdown
74 lines
4.2 KiB
Markdown
---
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title: "Qdrant Updated Benchmarks 2024"
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draft: false
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slug: qdrant-benchmarks-2024 # Change this slug to your page slug if needed
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short_description: Qdrant Updated Benchmarks 2024 # Change this
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description: We've compared how Qdrant performs against the other vector search engines to give you a thorough performance analysis # Change this
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preview_image: /benchmarks/social-preview.png # Change this
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categories:
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- News
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# social_preview_image: /blog/Article-Image.png # Optional image used for link previews
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# title_preview_image: /blog/Article-Image.png # Optional image used for blog post title
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# small_preview_image: /blog/Article-Image.png # Optional image used for small preview in the list of blog posts
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date: 2024-01-15T09:29:33-03:00
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author: Sabrina Aquino # Change this
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featured: false # if true, this post will be featured on the blog page
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tags: # Change this, related by tags posts will be shown on the blog page
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- qdrant
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- benchmarks
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- performance
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---
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It's time for an update to Qdrant's benchmarks!
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We've compared how Qdrant performs against the other vector search engines to give you a thorough performance analysis. Let's get into what's new and what remains the same in our approach.
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### What's Changed?
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#### All engines have improved
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Since the last time we ran our benchmarks, we received a bunch of suggestions on how to run other engines more efficiently, and we applied them.
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This has resulted in significant improvements across all engines. As a result, we have achieved an impressive improvement of nearly four times in certain cases. You can view the previous benchmark results [here](/benchmarks/single-node-speed-benchmark-2022/).
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#### Introducing a New Dataset
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To ensure our benchmark aligns with the requirements of serving RAG applications at scale, the current most common use-case of vector databases, we have introduced a new dataset consisting of 1 million OpenAI embeddings.
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#### Separation of Latency vs RPS Cases
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Different applications have distinct requirements when it comes to performance. To address this, we have made a clear separation between latency and requests-per-second (RPS) cases.
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For example, a self-driving car's object recognition system aims to process requests as quickly as possible, while a web server focuses on serving multiple clients simultaneously. By simulating both scenarios and allowing configurations for 1 or 100 parallel readers, our benchmark provides a more accurate evaluation of search engine performance.
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### What Hasn't Changed?
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#### Our Principles of Benchmarking
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At Qdrant all code stays open-source. We ensure our benchmarks are accessible for everyone, allowing you to run them on your own hardware. Your input matters to us, and contributions and sharing of best practices are welcome!
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Our benchmarks are strictly limited to open-source solutions, ensuring hardware parity and avoiding biases from external cloud components.
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We deliberately don't include libraries or algorithm implementations in our comparisons because our focus is squarely on vector databases.
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Why?
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Because libraries like FAISS, while useful for experiments, don’t fully address the complexities of real-world production environments. They lack features like real-time updates, CRUD operations, high availability, scalability, and concurrent access – essentials in production scenarios. A vector search engine is not only its indexing algorithm, but its overall performance in production.
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We use the same benchmark datasets as the [ann-benchmarks](https://github.com/erikbern/ann-benchmarks/#data-sets) project so you can compare our performance and accuracy against it.
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### Detailed Report and Access
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For an in-depth look at our latest benchmark results, we invite you to read the [detailed report](/benchmarks/).
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If you're interested in testing the benchmark yourself or want to contribute to its development, head over to our [benchmark repository](https://github.com/qdrant/vector-db-benchmark). We appreciate your support and involvement in improving the performance of vector databases.
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