Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

Q: What does this research mean for the field?

A multi-cloud strategy can optimize AI workloads while minimizing lock-in costs and enabling arbitrage opportunities. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

Oleh Ivchenko

doi:10.5281/zenodo.18825820

March 1, 2026Open Access

Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

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Key Points

The aim is to analyze the economic implications of multi-cloud strategies, focusing on arbitrage and lock-in costs.
Analyzed economic models related to multi-cloud strategies
Evaluated the impact of arbitrage on cost efficiencies
Assessed lock-in costs associated with cloud providers
Examined optimization techniques for AI workloads
Identified key arbitrage opportunities in multi-cloud settings
Outlined the cost implications of vendor lock-in
Demonstrated improved efficiencies in AI workload optimization through multi-cloud strategies

Implication

This research article explores the economics of multi-cloud strategies, examining arbitrage and lock-in costs to optimize AI workloads.

Abstract

Research article: Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

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Cite This Study

Oleh Ivchenko (2026) studied this question.

synapsesocial.com/papers/69a67eebf353c071a6f0a8c1 https://doi.org/10.5281/zenodo.18825820

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Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

Key Points

Implication

Abstract

Discussion

Cite This Study

Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

Key Points

Implication

Abstract

Discussion

Cite This Study

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