NNext.net – A Firebase-like managed vector storage for ML applications
Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…
NNext.net – A Firebase-like managed vector storage for ML applications launched on March 3, 2021 with 37 votes, #1,326 of 3,696 launches that month and more than 75% of that year's launches.
In plain words
NNext.net is a managed vector storage service designed for machine learning applications, offering a database where feature vectors and embeddings are first-class citizens. It eliminates the need for workarounds like storing vectors in SQL/NoSQL databases or Redis, providing native support for vector-based query operations such as nearest neighbor search. The platform serves ML engineers building applications including similarity search, recommendation systems, and duplicate detection.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors…
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