Multi-Cloud Disaster Recovery and Active-Active State Synchronization for Stateful AI Services

Stateful artificial intelligence inference services, such as real-time payment fraud detection, dynamic credit underwriting, and conversational session agents, depend on sub-second access to mutable feature vectors, sliding-window velocity counters, and key-value attention caches. When deployed across a single cloud provider or single region, correlated control-plane outages, border gateway protocol route leaks, or availability zone power failures can halt inference pipelines and violate strict financial service-level objectives. Traditional active-passive disaster recovery architectures fail to meet zero-data-loss recovery point objectives (RPO) and sub-second recovery time objectives (RTO) because asynchronous storage snapshots leave in-flight streaming feature aggregations unpersisted and require minutes of cold-cache warming after failover. This paper presents a hybrid multi-cloud active-active state synchronization architecture that partitions stateful AI workloads into two consistency tiers: strongly serializable transaction ledgers backed by Multi-Raft quorum consensus with hardware-assisted clock uncertainty bounds, and low-latency velocity feature counters replicated via delta-state Conflict-Free Replicated Data Types (CRDTs) over encrypted cross-cloud interconnects. We formulate a mathematical staleness-accuracy trade-off model that bounds fraud classification Area Under the Precision-Recall Curve (AUPRC) degradation as a function of replication lag and clock drift uncertainty. Evaluating the architecture through a production-grade case study modeled on Stripe multi-region payment fraud scoring alongside Google Cloud Spanner TrueTime and CockroachDB multi-region consensus deployments across AWS, Google Cloud, and Azure, empirical benchmarks demonstrate a 99.9994% availability rate, a p99 inference latency of 18.4 ms under active-active load, an RTO of 1.2 seconds during complete regional blackouts, and zero committed feature ledger loss. The findings provide cloud architects with a quantitative framework for balancing PACELC latency-consistency trade-offs against egress network costs in stateful multi-cloud AI deployments. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Distributed Consensus, High Availability, and Multi-Cloud Systems. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.