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Peloton had a problem: Manage peak traffic events -- greater than 450,000 requests per second -- while controlling its AWS infrastructure costs. In 2024, Peloton's engineering team made a bold decision to reduce costs and improve performance. It eliminated the performance environment entirely and chose to test in production instead.

As enterprises accelerate AI deployment, governance frameworks have not kept pace with the systems they are meant to control. Following his session at the BrightTALK Cloud Convergence Summit, thought leader Varun Raj dives deeper into why existing AI governance models are fundamentally misaligned with modern systems and proposes a control-plane approach to restore accountability, reliability, and system-level oversight in production environments.

SAN DIEGO -- AI has become both a challenge and an opportunity for FinOps teams, and AWS used this year's FinOps X conference to showcase a series of product updates that align closely with that reality. AWS introduced the AWS FinOps Agent in public preview, an AI-powered tool designed to automate cloud cost analysis, anomaly investigation and reporting, while surfacing optimization recommendations.

It's no secret that many enterprises still rely on legacy systems to run mission-critical operations. However, outdated platforms often leads to higher operational costs and slower innovation cycles. According to the "2025 State of Legacy Software Modernization Report," by Researchscape International on behalf of Saritasa, 62% of U.S. organizations still rely on legacy systems.

In theory, innovations in cloud cost management tools provide enterprise leaders with the knowledge needed to drive efficient cloud usage. Increased visibility into cloud spend details should help everyone get on the same page, realize savings and align business growth with the right cloud infrastructure. But the real world is full of messy details, cultural pushback and other sources of friction for grand visions of cloud and AI spending nirvana.

Rapid advances in technology have made digital transformation essential for business survival. When undergoing a cloud migration and modernization, a key factor to success is choosing the best cloud architecture for your business. Harvard Business Review reported that 89% of large companies have a digital and artificial intelligence transformation underway and there are many reasons why that number is so high.

Much of the conversation about AI and security focuses on security risks specific to AI models, such as prompt injection or training data poisoning. However, AI workloads are only as secure as the cloud environments where they reside -- hence why it’s critical to make cloud security a key element of broader AI security strategies.

At first glance, cloud networking may not seem to exert a major impact on AI workloads. After all, cloud networks are typically quite fast and reliable. IT leaders might plausibly assume that AI performance and cost optimization have much more to do with factors like model design and training processes than with cloud network configuration.

The strategy laid out by IBM during its Think conference isn't about competing head-to-head with hyperscalers on every front. It's about owning the enterprise segments where sovereignty, governance and hybrid infrastructure are non-negotiable -- and proving it can deliver at scale in the AI era.

For more than a decade, enterprise cloud strategy has followed a clear direction: consolidate. Applications moved from fragmented on-premises environments into centralized public cloud platforms. The goal was operational simplicity, scalability and cost efficiency. Hybrid architectures were tolerated but rarely embraced. They were seen as transitional states, necessary during migration, but ultimately something to move beyond. That assumption is beginning to break down.