This post was originally published on Info World
Tracking costs across clouds was another challenge. Each provider’s billing models were unique, making predicting and optimizing expenses difficult. I integrated APIs to pull real-time cost data into a unified dashboard, which allowed the AI system to include budget considerations in its decisions.
Cloud-specific variances sometimes caused misalignments, despite efforts to standardize deployments. For example, storage solutions handled certain operations differently across platforms, leading to occasional inconsistencies in how data was synchronized and retrieved. I resolved this by adopting hybrid storage models that abstracted platform-specific traits.
Autoscaling wasn’t consistent across environments, and some providers took longer than others to respond to bursts of demand. Tuning resource limits and improving orchestration logic helped reduce delays during unexpected scaling events.
Key takeaways
This experiment reinforced what I already knew: Agentic AI in multicloud is feasible with the right design and tools, and autonomous systems can successfully navigate the complexities of operating across multiple cloud providers. This architecture has excellent potential for more advanced use cases, including distributed AI pipelines, edge computing, and hybrid cloud integration.
However, challenges with interoperability, platform-specific nuances, and cost optimization remain. More work is needed to improve the viability of multicloud architectures. The big gotcha is that the cost
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