The AI Workload On Your Cluster Has No Backup Plan - And That's A Platform Problem
Platform Engineering, Friday, April 24th, 2026
AI workloads lack adequate backup and recovery strategies, creating a critical platform engineering gap.
Platform teams are deploying AI workloads without proper backup and recovery plans, treating them like traditional applications despite their unique characteristics. Vector databases, training datasets, and model artifacts behave differently than standard data stores and require outcome-level validation rather than just infrastructure recovery.
Recovering AI systems isn't simply about restoring data - it's about ensuring the system behaves identically after recovery, which most platforms cannot guarantee.
Platform engineering teams must take ownership of AI workload resilience as a core requirement, not an afterthought, to prevent failures from corrupted data or model poisoning attacks.