Almost every AI workload — training, fine-tuning or inference — sits on top of object storage. Datasets, model checkpoints, embeddings and generated artifacts are exactly the kind of large, immutable blobs object storage was built for. If you're standing up an AI pipeline, the storage layer is a decision worth making deliberately rather than defaulting into.

Why object storage is the default for AI

• Scale. Training sets run to terabytes or petabytes; object storage scales linearly and cheaply without capacity planning.
• The S3 API is the lingua franca. PyTorch, TensorFlow, Hugging Face datasets, MLflow, DVC and virtually every data-loading tool can read from an S3 endpoint. Point them at any S3-compatible provider and they just work.
• Immutability fits the workflow. Datasets and checkpoints are written once and read many times — no filesystem semantics required.
• Decoupled compute and storage. You can spin GPUs up and down while the data stays put, paying for compute only when you use it.

What to look for in AI object storage

Throughput, not just capacity

Data loaders read in parallel across many workers. Aggregate read throughput and low request latency matter more than headline capacity — a slow bucket starves expensive GPUs. Storage physically close to your compute region helps a lot here.

Egress economics

AI pipelines read the same data repeatedly across epochs and experiments. On a provider that charges for egress, re-reading a large dataset dozens of times gets expensive fast. Zero-egress storage removes that penalty entirely — a real cost difference for iterative training.

Data governance

Training data often includes personal or proprietary information. Where it's stored — and which jurisdiction can access it — becomes a compliance question the moment real user data enters the pipeline. For EU data, storage inside the EU under GDPR is frequently required.

Versioning and lifecycle

Reproducibility depends on being able to pin a dataset or checkpoint version. Look for versioning support and lifecycle rules to expire old experiment artifacts automatically.

A practical setup

A common, clean pattern: keep raw datasets, processed datasets, and model checkpoints in separate buckets; version the processed data; use presigned URLs to share artifacts; and expire scratch outputs with lifecycle rules. Because it's all S3, the same setup runs against any compatible provider — so you can start small and move if you outgrow it, without rewriting your pipeline.

Where to run it

If your data or your users are in Europe, an EU-hosted, S3-compatible provider gives you the throughput and the S3 tooling AI needs, with data that stays compliant and no egress penalty on repeated reads. Internetport's object storage is S3-compatible, GDPR-compliant, egress-free, and offers a free 10 GB tier — enough to wire up your data loaders and benchmark real training reads before you scale. For the wider evaluation, see our guide to choosing an object storage provider.