Storage

Supabase Storage is a robust, scalable solution for managing files of any size with fine-grained access controls and optimized delivery. Whether you're storing user-generated content, analytics data, or vector embeddings, Supabase Storage provides specialized bucket types to meet your specific needs.

Key features

• Multi Protocol - S3 compatible Storage, RESTful API, TUS resumable uploads
• Global CDN - Serve your assets with lightning-fast performance from over 285 cities worldwide
• Image Optimization - Resize, compress, and transform media files on the fly with built-in image processing
• Fine-grained Access Control - Manage file permissions with row-level security and custom policies
• Multiple Bucket Types - Specialized storage solutions for different use cases

Storage bucket types

Supabase Storage offers different bucket types optimized for specific use cases:

Files buckets

Store and serve traditional files including images, videos, documents, and general-purpose content. Ideal for user-generated content, media libraries, and asset management.

Use cases: Images, videos, documents, PDFs, archives

Features:

• Global CDN delivery
• Image optimization and transformation
• Row-level security integration
• Direct URL access for files

Learn more about Files Buckets

Analytics buckets

Purpose-built for storing and analyzing data in open table formats like Apache Iceberg. Perfect for time-series data, logs, and large-scale analytical workloads.

Use cases: Data lakes, analytics pipelines, ETL operations, historical data analysis

Features:

• Apache Iceberg table format support
• SQL-accessible via Postgres foreign tables
• Partitioned data organization
• Efficient data querying and transformation

Learn more about Analytics Buckets

Vector buckets

Specialized storage for vector embeddings and similarity search operations. Designed for AI and ML applications requiring semantic search capabilities.

Use cases: AI-powered search, semantic similarity matching, embedding storage, RAG systems

Features:

• Optimized vector indexing (HNSW, Flat)
• Multiple distance metrics (cosine, euclidean, L2)
• Metadata filtering for vectors
• Similarity search queries

Learn more about Vector Buckets

Examples

Check out all of the Storage templates and examples in our GitHub repository.

<div className="grid md:grid-cols-12 gap-4 not-prose"> {storageExamples.map((x) => ( <div className="col-span-12" key={x.href}> <Link href={x.href} passHref> <GlassPanel icon={'/docs/img/icons/github-icon'} hasLightIcon={true} title={x.name}> {x.description} </GlassPanel> </Link> </div> ))} </div>

Resources

Find the source code and documentation in the Supabase GitHub repository.

<div className="grid md:grid-cols-12 gap-4 not-prose"> {[ { name: 'Supabase Storage API', description: 'View the source code.', href: 'https://github.com/supabase/storage-api', }, { name: 'OpenAPI Spec', description: 'See the Swagger Documentation for Supabase Storage.', href: 'https://supabase.github.io/storage/', }, ].map((x) => ( <div className="col-span-6" key={x.href}> <Link href={x.href} passHref> <GlassPanel title={x.name}>{x.description}</GlassPanel> </Link> </div> ))} </div>