Local AI on S3
You're running local inference. Your data lives on disk, in folders, maybe in a database. At some point, you need durable, searchable, shared storage. This page maps the path from local files to an S3-based data layer — the technologies, formats, and tradeoffs that matter.
Your models generate artifacts that outgrow local disk
Your retrieval pipeline needs persistent, searchable storage
Scattered files, embeddings, and metadata = pipeline chaos
S3-compatible storage is the common protocol — self-hosted or cloud
I need durable storage for local AI
Self-hosted and cloud S3-compatible object stores — the foundation layer everything else builds on.
I need retrieval over many files
Vector databases, hybrid indexes, and ML-native formats for search and RAG on S3-stored data.
I need structured analytics over S3
Embedded and distributed query engines, table formats, and lakehouse patterns for analytical workloads.
I need metadata and indexing
Catalogs, governance, and metadata services that control what lives in object storage.
I need to compare tools and formats
Side-by-side evaluations of table formats, vector databases, and streaming engines.
I need to avoid vendor lock-in
Zero-egress providers, open formats, and strategies to keep your data portable.