qdrant/vector-db-benchmark
Framework for benchmarking vector search engines
This framework helps you compare the speed and efficiency of different vector search engines. It takes a vector search engine, a dataset (like embeddings for text or images), and a defined test scenario as input. It then measures how well and how quickly the engine performs, providing results to help you choose the best one for your specific needs. This is ideal for machine learning engineers, MLOps specialists, or anyone building or deploying applications that rely on fast and accurate vector search.
353 stars.
Use this if you need to choose the most performant vector database for your application and want to objectively compare different options under your specific workload and hardware constraints.
Not ideal if you are looking for a general-purpose database or if you only need to evaluate a single vector search engine without comparing it against others.
Stars
353
Forks
139
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 12, 2026
Commits (30d)
0
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