Most disaster recovery plans were written for a world of databases and virtual machines. Replicate the data, keep a warm copy somewhere else, document the runbook, and when the primary site fails, you fail over to the replica. That logic still holds for the systems it was designed around. It quietly breaks for AI. An AI estate holds far more than data at rest: live model state, long-running training jobs, inference services with latency commitments, and a dependence on scarce GPU capacity that a replica site may not have. A backup of the data is not the same as continuity of the AI service. At L&T Vyoma, our position is that disaster recovery for AI has to be designed for what AI actually is, not retrofitted from a plan built for the last generation of workloads.
What Classic DR Doesn't Cover for AI
Three gaps open up when you point a conventional DR plan at an AI workload. The first is model and checkpoint state. A training run that has consumed weeks of GPU time holds enormous value in its intermediate checkpoints, and a DR plan that protects the dataset but not the checkpoints means an outage restarts the run, not resumes it. The second is inference availability. A model serving live predictions carries a latency and uptime commitment, and failing it over is not a file copy; it is standing the model back up, loading weights, warming caches and re-establishing throughput, fast enough to hold the service level. The third, and the one most plans ignore, is GPU failover capacity. You cannot fail over to accelerators you do not have. A recovery site with storage and CPU but no available GPUs can restore the data and still leave the AI service down. Recovery for AI is a compute problem as much as a data one.
Setting RTO and RPO for a Model, Not a Database
Classic DR is measured with two numbers: recovery-time objective, how fast you must be back, and recovery-point objective, how much data you can afford to lose. AI changes what both mean. The recovery-point question shifts from how recent the data copy is to how recent the model checkpoint is, because a lost day of training is a lost day of GPU time and cost, far more expensive than a lost day of records. The recovery-time question gains a step classic DR does not have: a model is not back when the data is restored, it is back when it is loaded, warmed and serving within its latency target, which can take materially longer than standing up a database. Setting honest objectives for an AI workload means measuring recovery against the model serving, not the storage mounting, and sizing the recovery site's compute to hit that target. Most plans quietly skip the distinction, and find the gap mid-incident.
The Cost of Getting It Wrong Is Rising
Downtime was never cheap, and AI is making the dependency deeper. New Relic's 2025 Observability Forecast put the median annual cost of high-impact IT outages at USD 76 million, and the cost of an hour of that downtime at a median of USD 2 million. As AI moves into revenue-facing and decision-making roles, an AI service that is down is no longer a degraded feature; it is a stalled process. The same forecast noted that AI monitoring adoption rose from 42% to 54% in a year, and that agentic and large-language-model applications create failure and visibility modes traditional monitoring was never built to catch. The exposure is growing faster than most DR plans are being updated to match it.
The India Angle: Residency, and a Real DR Pair
For an Indian enterprise, AI disaster recovery is also a sovereignty question. Under the DPDP Act, the data an AI model trains and infers on is governed by residency and cross-border rules, so the recovery copy and the failover compute both have to stay in the country to be usable under pressure. That raises a practical requirement most cloud disaster recovery solutions gloss over: a genuine second site, far enough from the first to survive a regional event, but still on Indian soil. Two data centers in the same city are a convenience, not a DR pair. Real geographic separation within the jurisdiction is what turns a recovery plan into business continuity you can actually rely on.
|
Dimension |
Classic DR (databases, VMs) |
DR for AI Workloads |
|
What is protected |
Data at rest |
Data, model state and checkpoints |
|
Failover |
Copy and restart |
Reload weights, warm caches, hold SLA |
|
Compute at the DR site |
CPU and storage |
Available GPU capacity to fail over to |
|
Recovery measure |
Data restored |
Model serving within its SLA |
|
Location |
Often cross-border |
In-country, genuinely separated |
Cloud Disaster Recovery Solutions, Designed for AI
This is the combination L&T Vyoma is built to provide: cloud disaster recovery solutions designed for AI workloads, with in-country replicas, reserved GPU capacity to fail over to, and a genuine DR pair across our Mumbai and Chennai data centers — real geographic separation, one operator, one jurisdiction. As an AI-ready data center provider in India, we run the high performance AI data center capacity and the colocation data center services in India that let the recovery site actually carry the load, rather than only hold the data. Our managed services team builds the model-aware runbook — checkpoint protection, the load-and-warm sequence, dependency ordering and the GPU reservation that backs it — and rehearses the failover on a schedule, so recovery is a tested motion against an agreed target rather than an assumption. The result is business continuity you can evidence: an AI service that comes back within its SLA, on Indian soil, with the failover proven before you needed it. Talk to our team about cloud disaster recovery solutions for your AI estate, or explore our managed cloud services and business continuity offerings in India.
Sources: cost of high-impact IT outages and AI-monitoring adoption — New Relic, 2025 Observability Forecast (median USD 76 million per year; median USD 2 million per hour of downtime; AI monitoring adoption up from 42% to 54%); DPDP Act, 2023 (data residency and cross-border rules).