Introducing Storage Buckets on the Hugging Face Hub

Read the full articleIntroducing Storage Buckets on the Hugging Face Hub on Hugging Face

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What Happened

Our Take

Hugging Face is trying to own the MLOps data layer. This is vendor lock-in disguised as convenience. We need centralized control over our large, distributed datasets, not another platform to manage. Don't mistake storage buckets for true architectural freedom. Focus on how you ingest and process the data, not where it lives.

What To Do

Audit your current data pipelines to identify where data egress costs are disproportionately high.

Builder's Brief

Who

ML engineers managing large dataset and artifact storage on Hugging Face

What changes

teams can consolidate model weights and training data in one hub rather than mirroring to S3, reducing egress complexity

When

now

Watch for

pricing per-GB versus S3 standard at production dataset scale

What Skeptics Say

Storage buckets are commodity S3-compatible infrastructure; Hugging Face risks diluting its model-hub differentiation by competing with AWS and GCS on generic storage, a race it cannot win on price or reliability at scale.

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