Skip to main content

Hybrid Cloud Management Platforms Help Control Data and Costs

More enterprises are implementing hybrid cloud management platforms as they diversify their IT environments to overcome the limits of relying solely on public clouds, according to a new research report published by Information Services Group (ISG).

The 2024 ISG Provider Lens™ global Private/Hybrid Cloud — Data Center Solutions report finds that organizations want the flexibility, scalability and agility of cloud computing while addressing their unique operational, regulatory and security challenges. In many cases, intelligently planned hybrid cloud platforms help them control expenses, data residency and compliance.

"Companies that are worried about the economy want to get more out of their IT investments," said Anay Nawathe, ISG cloud delivery lead. "With strong management, private and hybrid cloud infrastructures can maximize operational efficiency and financial resilience."

Along with these benefits, hybrid clouds bring more complexity, especially with the need for resource coordination across platforms and smooth data flow between on-premises and cloud infrastructure, ISG says. This requires specialized tools and skills, so enterprises are implementing hybrid cloud management platforms that let them get the most out of each cloud environment and minimize performance bottlenecks.

Organizations are also under pressure to make IT infrastructure more resilient, increasing the demand for backup and disaster recovery platforms, the report says. These create copies of critical data and systems so operations can quickly resume after a cyberattack or natural disaster. Scalable, secure and cost-effective resiliency solutions are becoming as crucial as primary on-premises and public cloud infrastructure.

AI and ML play growing roles in both cloud management and resilience platforms, ISG says. Companies are embracing AI and ML cloud management tools that use data from various sources to predict downtime and initiate self-healing tools, enhancing reliability. Such technologies are also being used to automate backup and recovery platforms, some of which use algorithms to identify and respond to anomalies or threats in real time.

"Faster response and recovery to a disruption minimizes any loss of revenue and productivity, while at the same time improving customer satisfaction," said Jan Erik Aase, partner and global leader, ISG Provider Lens Research. "Vendors are helping enterprises achieve these gains through AI and automation."

Companies are also tightening control over data in both cloud management and resilience platforms using privacy-enhancing features, the report says. These include access controls and encryption key management that allow them to define and enforce granular access policies.

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Hybrid Cloud Management Platforms Help Control Data and Costs

More enterprises are implementing hybrid cloud management platforms as they diversify their IT environments to overcome the limits of relying solely on public clouds, according to a new research report published by Information Services Group (ISG).

The 2024 ISG Provider Lens™ global Private/Hybrid Cloud — Data Center Solutions report finds that organizations want the flexibility, scalability and agility of cloud computing while addressing their unique operational, regulatory and security challenges. In many cases, intelligently planned hybrid cloud platforms help them control expenses, data residency and compliance.

"Companies that are worried about the economy want to get more out of their IT investments," said Anay Nawathe, ISG cloud delivery lead. "With strong management, private and hybrid cloud infrastructures can maximize operational efficiency and financial resilience."

Along with these benefits, hybrid clouds bring more complexity, especially with the need for resource coordination across platforms and smooth data flow between on-premises and cloud infrastructure, ISG says. This requires specialized tools and skills, so enterprises are implementing hybrid cloud management platforms that let them get the most out of each cloud environment and minimize performance bottlenecks.

Organizations are also under pressure to make IT infrastructure more resilient, increasing the demand for backup and disaster recovery platforms, the report says. These create copies of critical data and systems so operations can quickly resume after a cyberattack or natural disaster. Scalable, secure and cost-effective resiliency solutions are becoming as crucial as primary on-premises and public cloud infrastructure.

AI and ML play growing roles in both cloud management and resilience platforms, ISG says. Companies are embracing AI and ML cloud management tools that use data from various sources to predict downtime and initiate self-healing tools, enhancing reliability. Such technologies are also being used to automate backup and recovery platforms, some of which use algorithms to identify and respond to anomalies or threats in real time.

"Faster response and recovery to a disruption minimizes any loss of revenue and productivity, while at the same time improving customer satisfaction," said Jan Erik Aase, partner and global leader, ISG Provider Lens Research. "Vendors are helping enterprises achieve these gains through AI and automation."

Companies are also tightening control over data in both cloud management and resilience platforms using privacy-enhancing features, the report says. These include access controls and encryption key management that allow them to define and enforce granular access policies.

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...