Choosing a Storage Provider for Your DataDAO

Vana is designed to give you full custody control over your data — what gets shared, how it’s accessed, and where it’s stored. While on-chain attestations ensure verifiability, the raw data itself is stored off-chain: encrypted, and referenced by a public URL or content hash.

As a DataDAO, this means you're not just choosing storage for yourself — you’re also guiding others. On one side, contributors need a simple way to store encrypted files they control. On the other, your DAO is responsible for refined datasets that power rewards, queries, and jobs — and those need to stay reliably online.

This guide walks through the most common storage options and tradeoffs, so you can choose what works best for both roles.

🛡️ Security First: CIA Principles in Practice

When selecting a storage solution, it's essential to adhere to the CIA triad — Confidentiality, Integrity, and Availability:

Confidentiality: Ensure your data is encrypted before uploading to maintain privacy.
Integrity: Vana validates your data using on-chain fingerprints and Trusted Execution Environments (TEEs).
Availability: While Vana maintains on-chain pointers, you are responsible for ensuring the raw data remains accessible.

Note that as a DataDAO, you’ll deal with two types of encrypted data:

Raw files uploaded by users – these should stay under the user’s control (self-custody).
Refined files published by your DAO – these must remain reliably available, since they’re used for rewards and permissioned access.

For refined datasets, storage is fully managed by the DataDAO to ensure long-term availability. In some cases, DataDAOs may also host encrypted data contributed by users, depending on the UX they aim to provide. Because all data is encrypted at the source, hosting strategy becomes a matter of operational design — not access or control.

🔍 Storage Options Overview

Vana is a storage-agnostic and supports any provider that offers publicly accessible URLs or content hashes. Below is a breakdown of recommended options, their pros and cons, and common use cases.

Option 1: IPFS (InterPlanetary File System)

IPFS is a decentralized and content-addressed storage system. Files are split into blocks, individually hashed, and distributed across nodes. Retrieval pulls from any peer with the content.

Advantages:

Decentralization: Resistant to censorship and centralized failures.
Redundancy: Files can be served from many sources simultaneously.
Immutability: Files are versioned by hash; updates generate new hashes.

Considerations:

Pinning: To ensure availability, files must be pinned using services like web3.storage or self-hosted nodes.
Performance: Can vary depending on the number of hosting peers.

Cost:

Self-hosted node: Infrastructure only.
Pinning services: ~$0.02–0.05 per GB/month (e.g., Pinata, web3.storage).

Best For: DataDAOs prioritizing decentralization or needing immutable, open-access datasets.

Option 2: Traditional Cloud Providers (Dropbox, Google Drive, OneDrive)

Popular consumer cloud providers with simple UX and public sharing options. Easy for contributors to use and upload encrypted files.

Advantages:

Ease of Use: Familiar to most users.
Integration: Works natively with OS and productivity apps.
Versioning: Basic rollback support via UI.

Considerations:

Centralization: Provider controls uptime and access.
No URL-based versioning: You must upload distinct versions manually.

Cost:

Dropbox: 2 GB free → $11.99/month for 2 TB.
Google Drive: 15 GB free → $1.99/month for 100 GB.
OneDrive: 5 GB free → $1.99/month for 100 GB.

Best For: Individual contributors storing their own encrypted data.

Option 3: Autonomys DSN (Distributed Storage Network)

Autonomys DSN is a decentralized storage network designed to ensure data permanence, integrity, and accessibility, particularly suited for AI and decentralized applications.

Advantages:

Decentralization: Eliminates single points of failure by distributing data across a vast network of nodes.
Data Integrity: Utilizes erasure coding and content addressing to maintain data accuracy and consistency.
Scalability: Designed to handle large-scale data storage needs, making it suitable for AI and big data applications.

Considerations:

Ecosystem Maturity: As an emerging technology, tooling and community support are still developing.
Integration Complexity: May require additional effort to integrate with existing systems compared to more established storage solutions.

Cost:

No fixed per-GB cost. Storage incentives are built into the protocol and often subsidized for early-stage usage.

Best For: Projects requiring decentralized, scalable, and secure storage solutions, particularly in AI and data-intensive applications.

Option 4: Enterprise Object Storage (AWS S3, Azure Blob Storage, Google Cloud Storage)

Cloud object storage with APIs, versioning, and lifecycle policies. Best suited for storing refined, DAO-maintained datasets with high uptime guarantees.

Advantages:

Scalability: Handles petabyte-scale workloads.
Versioning: Supports programmatic access to historical file versions.
Security & Compliance: Used across finance, health, and AI workloads.

Considerations:

Complexity: Requires setup, billing management, and infra knowledge.
Cost layers: Charges for storage, requests, and bandwidth.

Cost:

AWS S3: ~$0.023/GB/month (first 50TB).
Azure Blob: ~$0.018/GB/month (hot tier).
Google Cloud Storage: ~$0.02/GB/month (standard).

Best For: DataDAOs managing refined datasets with long-term availability needs.