mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-29 16:08:32 +02:00
Kumar Shivenduandgenerall
80f2b6f978
* feat: Update benchmarks * fix: Improve blog * feat: Add result files with 1 and 100 parallel clients * fix: Add elasticsearch benchmark numbers from dbpedia 1M openai embeddings * feat: Make mean time (latency) the default metric for the 2nd plot * feat: Improve conclusions based on es results * fix: Make charts work on changing dataset * fix: Typo in table * feat: Update results with quantization * fix: Show a single graph to keep things simple * fix: Improve words * review fixes * add link to open-source * feat: Improve units to make results more readable * fix: Small grammatical mistake * fix: Make search threads value constant based on plot metric * feat: Make dataset num vectors more readable * fix: Spacing in table * feat: Add results from the latest Redis benchmarks * feat: Update benchmarks page * feat: Update date and some of the points * feat: Update benchmarks and content * chores: Improve observations * feat: Improve observations and put it after the graph --------- Co-authored-by: generall <andrey@vasnetsov.com>
33 lines
1.7 KiB
Markdown
33 lines
1.7 KiB
Markdown
---
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draft: false
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id: 2
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title: How vector search should be benchmarked?
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weight: 1
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---
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# Benchmarking Vector Databases
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At Qdrant, performance is the top-most priority. We always make sure that we use system resources efficiently so you get the **fastest and most accurate results at the cheapest cloud costs**. So all of our decisions from [choosing Rust](/articles/why-rust), [io optimisations](/articles/io_uring), [serverless support](/articles/serverless), [binary quantization](/articles/binary-quantization), to our [fastembed library](/articles/fastembed) are all based on our principle. In this article, we will compare how Qdrant performs against the other vector search engines.
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Here are the principles we followed while designing these benchmarks:
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- We do comparative benchmarks, which means we focus on **relative numbers** rather than absolute numbers.
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- We use affordable hardware, so that you can reproduce the results easily.
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- We run benchmarks on the same exact machines to avoid any possible hardware bias.
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- All the benchmarks are [open-sourced](https://github.com/qdrant/vector-db-benchmark), so you can contribute and improve them.
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<details>
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<summary> Scenarios we tested </summary>
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1. Upload & Search benchmark on single node [Benchmark](/benchmarks/single-node-speed-benchmark/)
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2. Filtered search benchmark - [Benchmark](/benchmarks/#filtered-search-benchmark)
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3. Memory consumption benchmark - Coming soon
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4. Cluster mode benchmark - Coming soon
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</details>
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</br>
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Some of our experiment design decisions are described in the [F.A.Q Section](/benchmarks/#benchmarks-faq).
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Reach out to us on our [Discord channel](https://qdrant.to/discord) if you want to discuss anything related Qdrant or these benchmarks.
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