2026-10-06 20:37 UTCOriginal source1 min readUpdated: 2026-10-06 20:47 UTC

EmbeddingGemma 2 and Why Apache 2.0 Matters for Embedding Models

Summary

In a comment on the Hacker News thread about EmbeddingGemma 2, Simon Willison welcomes the model's Apache 2.0 license and argues that embedding models in particular should not be closed, proprietary and hosted-only: once a vendor retires a model, users must pay to re-embed the millions of vectors they have already stored. He does not want to self-host, but wants the open weights as a fallback if a provider ever stops serving the model.

SourceSimon Willison's Weblog

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Comment: EmbeddingGemma 2

Simon Willison’s Weblog

6th October 2026

Comment My comment on EmbeddingGemma 2 — Hacker News

I really appreciate that EmbeddingGemma 2 is under the Apache 2.0 license.

For embedding models in particular, I don't think it makes sense to use a closed, proprietary, hosted-only model.

Most applications of embedding models involve calculating thousands or even millions of embedding vectors and storing them for later comparison.

If your model is proprietary, the vendor is likely someday going to decide to stop offering that model. They'll have a better model to replace it, but you still need to pay to re-calculate those millions of stored existing vectors.

(In April 2024 OpenAI offered to "cover the financial cost of users re-embedding content with these new models" - https://openai.com/index/gpt-4-api-general-availability/ - but I don't think that's something we can rely on from every provider.)

Notably, I don't want to host the model myself. I'd much rather pay a provider for a hosted model while knowing that if they ever stop hosting it I can run the open weights version myself - or find another vendor who can do that for me.

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Key points

Simon Willison praises EmbeddingGemma 2 for shipping under the Apache 2.0 license, arguing embedding models especially should not be closed, hosted-only and proprietary.
Embedding workflows involve computing and storing thousands or millions of vectors, so a vendor retiring a model forces users to pay to recompute all existing embeddings.
He notes OpenAI offered in April 2024 to cover the financial cost of users re-embedding content with new models, but says that cannot be relied on from every provider.
He prefers paying a provider to host the model rather than hosting it himself, while knowing he could run the open weights or move to another vendor if hosting stops.

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