Observability: The Key to Managing Multi-Cloud, Cost Optimization, and AI WorkloadsObservability: The Key to Managing Multi-Cloud, Cost Optimization, and AI Workloads

In today’s complex IT environments, observability has emerged as an indispensable practice. Providing comprehensive insights into an organization’s IT infrastructure and applications, observability empowers businesses to tackle the challenges of multi-cloud environments, manage costs, and optimize AI workloads. However, observability alone cannot drive outcomes—it must be paired with application delivery mechanisms, like traffic steering, to act on those insights effectively.

Observability in Multi-Cloud Environments

Operating across multiple cloud platforms brings undeniable benefits, but it also introduces many challenges. For over a decade consistent application performance has been called out as a top challenge for organizations operating a multi-cloud estate.

By leveraging open-source frameworks like OpenTelemetry, businesses can collect and analyze data across various cloud platforms. This interoperability enables seamless integration and reduces operational silos. No surprise that early results for our annual research shows a healthy adoption for OpenTelemetry, with most either already adopting the standard or planning to in the next year.

Moreover, observability aids in workload placement. For instance, AI-powered analytics can dynamically allocate workloads to the most suitable cloud regions, optimizing both performance and resource utilization. This capability is particularly crucial for latency-sensitive applications and globally distributed workloads.

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Controlling Costs Through Observability

Cloud spending often spirals out of control without proper visibility. Observability offers a solution by uncovering inefficiencies in resource usage and enabling proactive cost management. Given that we’re now seeing cost as the top reason for repatriation of workloads from public cloud to on-premises, cost reduction is a good thing if public cloud is the preferred environment.

Through granular insights into CPU, memory, and GPU usage, observability tools help detect over-provisioned resources and underutilized assets. These insights inform strategic—and even dynamic—workload placement.

In addition to optimizing existing resources, observability fosters better forecasting. With predictive analytics, organizations can anticipate resource demands during peak periods, avoiding overages and maintaining budget discipline. This capability is particularly valuable for industries with cyclical workload demands, such as retail during holiday seasons or financial services during market fluctuations.

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Enhancing AI Workloads with Observability

The growing adoption of AI workloads introduces unique demands on IT infrastructure. Observability plays a critical role in managing these workloads by ensuring resource availability, detecting bottlenecks, and maintaining high performance.

AI workloads are notoriously resource-intensive, often requiring significant computational power from GPUs and CPUs. Observability provides visibility into resource consumption, enabling IT teams to allocate resources dynamically based on workload requirements.

Moreover, observability enhances the reliability of AI systems. By identifying performance bottlenecks and anomalies in real time, organizations can take corrective action before these issues impact end-users. This proactive approach ensures smoother operations and builds trust in AI-driven processes.

The Role of Application Delivery

While observability provides the insights, application delivery mechanisms—such as traffic steering—enable organizations to act on them. Traffic steering dynamically routes application traffic based on real-time data, ensuring optimal performance, cost efficiency, and user experience.

Consider a scenario where observability tools detect increased latency in a specific cloud region. Without traffic steering, IT teams would struggle to address the issue promptly. However, with an application delivery solution in place, traffic can be rerouted to a more responsive region automatically, maintaining performance standards and minimizing user disruption.

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Additionally, application delivery supports governance by enforcing consistent policies across multiple clouds. By integrating observability data with delivery platforms, organizations can automate compliance and security measures, reducing manual effort and risk.

It should not be a surprise to learn that Incomplete Observability, Unoptimized Traffic Steering, and Incompatible Delivery Policies are all on our Application Delivery Top 10 challenges.

Looking Ahead: Observability and Traffic Steering in 2025

As businesses embrace hybrid, multi-cloud, and AI-driven workloads, the combination of observability and application delivery will become even more critical. Organizations will need to invest in AI-powered observability tools to handle the increasing complexity of their environments. Simultaneously, application delivery solutions will evolve to provide more granular control and predictive capabilities.

Future observability platforms may integrate directly with application delivery systems to create closed-loop automation. This integration would allow insights from observability tools to trigger real-time actions, such as scaling resources or adjusting traffic routes, without human intervention. Such advancements will enable organizations to achieve unprecedented levels of efficiency, resilience, and agility.

Conclusion

Observability has cemented its place as a cornerstone of modern IT operations, offering critical insights that drive decision-making in multi-cloud, cost management, and AI workloads. However, its true potential is unlocked when paired with application delivery solutions. Together, these technologies empower organizations to not only see their environments but also act on that knowledge effectively. As we move through 2025, businesses that embrace this synergy will be well-positioned to navigate the complexities of the AI era.