Five critical strategies for AI-ready backup infrastructure

Aaron Blutstein 3 June 2026  

Many enterprises embarked on their cloud journeys five, ten years ago hoping their data solution would fit for the long-term, scale with them and have their IT provisioning simplified. But now that data has morphed into artificial intelligence (AI), data is redefined and that means companies have to rethink how they operate and in particular how they backup their data. As Gartner predicts, half of all cloud computing resources will be allocated to AI workloads by 2029, the focus is now making backups “AI-ready”. Sebastian Straub, Principal Solution Architect at N2W looks at five critical strategies for AI-ready backup infrastructure.

AI isn’t just “more data.” It’s a new class of data — massive, dynamic, and expensive. Training models require powerful (and costly) GPU instances, billed down to the second. These models ingest terabytes or even petabytes of training data, while real-time inference and analytics generate new volumes by the hour. Add transfer fees and idle compute time, and your cloud bill can spiral before you know it. Just look at Airbnb: they had to reassess their entire AWS strategy after AI workloads drove their cloud spending into the tens of millions annually.

Gartner predicts that by 2029, half of all cloud computing resources will be consumed by AI workloads. That stat alone should be a wake-up call. If your backups aren’t evolving with your data, your AI strategy could be at serious risk.

Here’s the part many overlook: AI infrastructure isn’t just about speed and compute power — it’s about resilience. The big piece of the puzzle that enterprises tend to overlook when it comes to making their data AI-ready: their backups. It’s about having scalable, secure, and cost-efficient backup systems in place to protect your most valuable digital assets. In a world where AI data is becoming a strategic weapon — think economic competition, cyber sabotage, and sovereign data regulations — your backup strategy is no longer just IT’s problem. It’s a board-level issue and it requires maximum secured backup protection with minimal RTO.

Enterprises are coming to terms that their backup systems can’t keep pace with the demands of AI data. And what happens if your backups aren’t AI-ready? Then your entire AI strategy is on shaky ground.

At N2W, I’ve spent nearly a decade helping over 1,000 organizations safeguard petabytes of cloud-based data. Lately, we’re seeing a surge in customers coming to us with a common concern: their old backup solutions can’t keep pace with the demands of AI. And they’re right.

So, what does it take to make your backup infrastructure AI-ready? Here are five critical strategies every enterprise should consider to future-proof their data protection and disaster recovery.

1. Use snapshot-based backups for high-volume AI data

AI systems churn through enormous datasets — we’re talking terabytes or even petabytes of training data, vector embeddings, metadata, and more. These datasets are in constant motion as models evolve and retrain.

Traditional backups (think file-by-file or agent-based methods) just can’t keep up — they’re slow, fragile, and don’t scale. What you need are snapshot-based, incremental backups that plug directly into your cloud-native environment (AWS, Azure, GCP).

Snapshot backups capture data quickly and consistently, with minimal performance impact. But here’s the catch: not all snapshot solutions are created equal. Some tools store full backups instead of true incrementals, leading to sky-high storage costs over time. Make sure your solution — like N2W — supports true incremental snapshots and long-term retention without blowing your cloud budget.

2. Optimise for Cloud-native scale and flexibility

AI workloads are fluid — they spin up and down rapidly, scale horizontally, and toggle between training and inference modes.

Your backup architecture needs to live in the same universe. Backups must integrate seamlessly with your cloud infrastructure, support automation at scale, and allow for rapid restores into live production or test environments.

Many backup SaaS platforms often become a bottleneck — expensive, rigid, and detached from modern DevOps workflows. Instead, go cloud-native:

• Use tools that integrate directly with cloud APIs
• Support auto-scaling backup operations
• Allow in-place or cross-region restores
• Work with IaC and CI/CD pipelines

Treat your backup infrastructure just like your AI infrastructure: dynamic, scalable, and programmable. If it’s not baked into your DevOps mindset, it’s slowing you down.

3. Enable cross-region and cross-account replication

AI data is often global — but compliance and security considerations demand strict boundaries. If your model is training on healthcare data in Europe and customer support transcripts in the U.S., you’ll need to keep data in-region, while still ensuring resilience against outages, ransomware, or accidental deletions.

To meet these requirements, backup strategies must incorporate cross-region and cross-account replication. This ensures that if one region or cloud account is compromised, your AI data is still recoverable.

It also enables a more robust ransomware resilience strategy: by keeping backup copies isolated in different accounts, the risk of malicious access or automated deletion (from compromised IAM credentials or ransomware bots) is significantly reduced.

From a compliance perspective, this separation can also help you prove that data is stored according to regional laws — a growing concern with regulations like GDPR, HIPAA, and the new EU AI Act.

4. Plan for long-term storage costs and lifecycle management

AI workloads don’t just generate massive datasets — they generate long-lived datasets. From model checkpoints and vector embeddings to simulation results and audit logs, much of it needs to be retained long-term.

The key: automated lifecycle management.

Set up policies to:

• Tier old backups to cold storage (like S3 Glacier or Azure Archive)
• Tag metadata to find what you need later
• Avoid unnecessary rehydration — only restore what’s actually needed

But beware: many tools don’t distinguish between incremental and full backups. If your system rehydrates entire datasets every time, your cloud bill will spike fast. Be surgical. Index well. Choose tools that play the long game with your data — and your budget.

5. Don’t be afraid to rethink your cloud provider

Here’s a growing trend: enterprises are starting to look beyond the hyperscalers.

The big three (AWS, Azure, GCP) are powerful, but they come with complexity, lock-in, and rising costs — especially for large-scale AI data management and backup. More organisations are exploring:

• Private cloud environments
•  Sovereign cloud solutions (to meet geopolitical and regulatory demands)
• Specialised backup providers with more transparent pricing and tighter integration

It’s not about ditching the cloud — it’s about choosing the right cloud for the job. For AI workloads, that might mean a hybrid strategy with public cloud compute and third party backup retention and data lifecycle management. It’s always worth evaluating.

Building an AI-resilient future

For years, backup was treated as a basic IT hygiene task — something to check off the list. But in the era of AI, backups are now highly strategic infrastructure.

What happens when that well-governed, well-labelled training dataset is accidentally deleted? Or a model’s intermediate checkpoint is overwritten during fine-tuning? Or a regional outage makes a real-time AI service unavailable?

If your backup strategy isn’t built to handle these scenarios – fast, intelligently, and in compliance with governance frameworks – then your AI infrastructure is incomplete. AI will transform businesses, but only if we lay the groundwork now to protect the data that powers it.