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Annoy Alternatives
Similar projects and alternatives to annoy
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txtai
đź’ˇ All-in-one AI framework for semantic search, LLM orchestration and language model workflows
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SaaSHub
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qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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Milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
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Weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
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bootcamp
Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc. (by milvus-io)
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featurebase
Discontinued A crazy fast analytical database, built on bitmaps. Perfect for ML applications. Learn more at: http://docs.featurebase.com/. Start a Docker instance: https://hub.docker.com/r/featurebasedb/featurebase
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hora
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better annoy alternative or higher similarity.
annoy discussion
annoy reviews and mentions
Posts with mentions or reviews of annoy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-05.
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How Spotify Decides What Plays Next
Increased opacity: As models become deeper, interpretability drops. Building your own “transparent” playlist generators using open‑source tools (e.g., Annoy for nearest‑neighbor searches) can serve as a personal sanity check.
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Should LLMs just treat text content as an image?
Yeah, that's why Erik Bernhardsson came up with the Approximate Nearest Neighbors Oh Yeah algorithm (or ANNOY for short)
> We use it at Spotify for music recommendations. After running matrix factorization algorithms, every user/item can be represented as a vector in f-dimensional space. This library helps us search for similar users/items. We have many millions of tracks in a high-dimensional space, so memory usage is a prime concern.
[0] https://erikbern.com/2013/04/12/annoy.html
[1] https://github.com/spotify/annoy?tab=readme-ov-file
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Do we think about vector dbs wrong?
The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy.
Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy) has been around since 2014. It was one of the first open source approximate nearest neighbor libraries. Recommendations have always been a good use case for vector similarity.
Recommendations are a natural extension of search and transformers models made building the vectors for natural language possible. To prove the worth of vector search over keyword search, the focus was always on showing how the top N matches include results not possible with keyword search.
In 2023, there has been a shift towards acknowledging keyword search also has value and that a combination of vector + keyword search (aka hybrid search) operates in the sweet spot. Once again this is validated through the same benchmarks which focus on the top 10.
On top of all this, there is also the reality that the vector database space is very crowded and some want to use their performance benchmarks for marketing.
Disclaimer: I am the author of txtai (https://github.com/neuml/txtai), an open source embeddings database
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Vector Databases 101
If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw.
- I'm an undergraduate data science intern and trying to run kmodes clustering. Did this elbow method to figure out how many clusters to use, but I don't really see an "elbow". Tips on number of clusters?
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Calculating document similarity in a special domain
I then use annoy to compare them. Annoy can use different measures for distance, like cosine, euclidean and more
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Can Parquet file format index string columns?
Yes you can do this for equality predicates if your row groups are sorted . This blog post (that I didn't write) might add more color. You can't do this for any kind of text searching. If you need to do this with file based storage I'd recommend using a vector based text search and utilize a ANN index library like Annoy.
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[D]: Best nearest neighbour search for high dimensions
If you need large scale (1000+ dimension, millions+ source points, >1000 queries per second) and accept imperfect results / approximate nearest neighbors, then other people have already mentioned some of the best libraries (FAISS, Annoy).
- Billion-Scale Approximate Nearest Neighbor Search [pdf]
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[R] Unlimiformer: Long-Range Transformers with Unlimited Length Input
Would be possible to further speed up the process with using something like ANNOY? https://github.com/spotify/annoy
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A note from our sponsor - SaaSHub
www.saashub.com | 16 Jun 2026
Stats
Basic annoy repo stats
42
14,252
5.6
8 months ago
spotify/annoy is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of annoy is C++.
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