Merge pull request #528 from qdrant/benchmarks-blog

Benchmarks Updates blog

qdrant-landing/content/blog/qdrant-updated-benchmarks-2024.md

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+---
+title: "Qdrant Updated Benchmarks 2024"
+draft: false
+slug: qdrant-benchmarks-2024 # Change this slug to your page slug if needed
+short_description:  Qdrant Updated Benchmarks 2024 # Change this
+description: We've compared how Qdrant performs against the other vector search engines to give you a thorough performance analysis # Change this
+preview_image: /benchmarks/social-preview.png # Change this
+categories:
+  - News
+# social_preview_image: /blog/Article-Image.png # Optional image used for link previews
+# title_preview_image: /blog/Article-Image.png # Optional image used for blog post title
+# small_preview_image: /blog/Article-Image.png # Optional image used for small preview in the list of blog posts
+
+date: 2024-01-15T09:29:33-03:00
+author: Sabrina Aquino # Change this
+featured: false # if true, this post will be featured on the blog page
+tags: # Change this, related by tags posts will be shown on the blog page
+  - qdrant
+  - benchmarks
+  - performance
+---
+
+It's time for an update to Qdrant's benchmarks! 
+
+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. 
+
+### What's Changed?
+
+#### All engines have improved
+
+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. 
+
+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](https://qdrant.tech/benchmarks/single-node-speed-benchmark-2022/).
+
+#### Introducing a New Dataset
+
+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.
+
+![rps vs precision benchmark - up and to the right is better](/blog/qdrant-updated-benchmarks-2024/rps-bench.png)
+#### Separation of Latency vs RPS Cases
+
+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. 
+
+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.
+
+![mean-time vs precision benchmark - down and to the right is better](/blog/qdrant-updated-benchmarks-2024/latency-bench.png)
+### What Hasn't Changed?
+
+#### Our Principles of Benchmarking
+
+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!
+
+
+Our benchmarks are strictly limited to open-source solutions, ensuring hardware parity and avoiding biases from external cloud components. 
+
+
+We deliberately don't include libraries or algorithm implementations in our comparisons because our focus is squarely on vector databases. 
+
+Why?
+
+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.
+
+
+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.
+
+### Detailed Report and Access
+
+For an in-depth look at our latest benchmark results, we invite you to read the [detailed report](qdrant.tech/benchmarks).
+
+
+If you're interested in testing the benchmark yourself or want to contribute to its development, head over to our [benchmark repository](github.com/qdrant/vector-db-benchmark). We appreciate your support and involvement in improving the performance of vector databases.
+
+