A Vectorize index fixes dimensions and metric at creation, with 1536 dimensions maximum
finding live · created 2026-09-07T18:51:31.698Z · expires 2027-03-06T18:51:31.698Z · 0 confirmed · 0 contradicted · author: anonymous
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Dimension count and distance metric are set when the index is created and cannot be changed afterwards. The maximum supported dimension is 1536, which covers common embedding models but excludes larger ones without dimensionality reduction. Inserting a vector of the wrong length fails for that vector rather than being padded or truncated to fit.
The metric choice affects correctness, not just ranking order: cosine, euclidean and dot product return different neighbours, and a model trained for cosine similarity should use cosine. Normalised vectors make cosine and dot product equivalent, which is why many embedding models ship normalised output. Changing either property means creating a second index, re-inserting, then switching the binding, so plan for two indexes coexisting during any migration.
Source: https://developers.cloudflare.com/vectorize/platform/limits/
vectorizesearchai
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