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Gartner Reveals 7 Myths for Hyperconverged Integrated Systems

As with many new technology trends, certain assumptions and hype emerge that can influence buyer behavior and lead to poor decisions. Gartner, Inc. has identified seven of the most common flawed assumptions in the hyperconverged integrated systems (HCIS) market.

"HCIS, which encompasses software-centric architectures that integrate compute, storage and networking on commodity hardware, promises a cost-effective infrastructure solution that is simple to deploy, manage and scale," said George Weiss, VP and Distinguished Analyst at Gartner. "However, new and emerging technologies are often surrounded by hype as vendors try to accelerate sales. Infrastructure and operations (I&O) leaders and decision makers should examine the following points carefully to avoid later disappointments or traps."

Myth 1: All Implementations Comprise Standard and Open Architectures

I&O leaders need to ask: what is a standard and open architecture? In the software-defined world of HCIS, the levels of standardization and openness depend increasingly on the codebase. It's important to be clear on who controls the code and who is responsible for its development, maintenance and performance. There are no software-defined standards, so one vendor's management controls may not manage another vendor's devices or software-defined network.

Myth 2: All Implementations Are Destined to Fail Mission-Critical Scalability and Resiliency Tests

HCIS implementations will vary widely in robustness, scalability and security. The vital context that needs to be considered is the intended use case. HCIS is designed and best-suited to high-availability and virtualized workloads. Yet even here there is wide variance; some HCIS clusters scale only to eight nodes, whereas others claim to scale to hundreds or even thousands.

"Caution is advised as this kind of scalability often does not fit the Gartner definition of a seamlessly managed HCIS appliance," Weiss said.

Myth 3: HCIS Costs Represent the Least-Expensive Deployment Model

HCIS infrastructure can be scaled up easily in small incremental adjustments by adding additional nodes as needed. However, over an extended period of time where the use-case demand rises and regularly requires additional nodes, the investment in HCIS could easily exceed an upfront investment.

Myth 4: The Most Important Use Case Is Virtual Desktop Infrastructure (VDI)

VDI has become the "celebrity" use case for HCIS. However, many general-purpose workloads are now a match for HCIS due to improved performance, scaling, data protection and ease of deployment, as well as an expanding hybrid cloud ecosystem. I&O leaders should expect further expansion to occur in the next three years to handle greater levels of agility, DevOps, containers, bimodal applications and consumer-based services.

Myth 5: HCIS Spells the Demise of Traditional Storage Arrays

HCIS has huge potential to replace small-to-midsize, general-purpose disk arrays in highly virtualized environments. In the case of large mission-critical applications that require predictable behavior and proven reliability, HCIS may be less effective. Once all capacity utilization and cost factors are considered, modern hybrid or solid-state array deployments are likely to be more economical over the long haul.

Myth 6: HCIS Eliminates Data Center Interoperability and Silos

HCIS lacks tight integration with existing traditional infrastructures, which forces I&O leaders to position them in silo deployments. The silo approach accommodates the existing administration and technical support of legacy operations. Nevertheless, HCIS also demands new models of team collaboration and specialty integrations that are different to existing legacy solutions. HCIS deployment models resonate most with IT leaders who want to switch from hardware stack management models to simple-to-deploy virtualized platform delivery.

Myth 7: Traditional Vendor Selection Preference Will Remain the Same

Gartner's focus group participants have shown that loyalty to traditional vendors would be tested by several criteria:

- Is the vendor increasingly fluent in the new wave of HCIS?

- Is it seriously willing to disrupt its conventional solutions?

- Does it have the vision to drive innovation?

- Can it keep ahead of emerging, agile competitors and increase its savings?

While there are risks in engaging with vendors that lack a solid track record, the commodity pricing of parts and infrastructure will alleviate some of that risk. It will become increasingly easy to navigate a worst-case vendor failure.

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Gartner Reveals 7 Myths for Hyperconverged Integrated Systems

As with many new technology trends, certain assumptions and hype emerge that can influence buyer behavior and lead to poor decisions. Gartner, Inc. has identified seven of the most common flawed assumptions in the hyperconverged integrated systems (HCIS) market.

"HCIS, which encompasses software-centric architectures that integrate compute, storage and networking on commodity hardware, promises a cost-effective infrastructure solution that is simple to deploy, manage and scale," said George Weiss, VP and Distinguished Analyst at Gartner. "However, new and emerging technologies are often surrounded by hype as vendors try to accelerate sales. Infrastructure and operations (I&O) leaders and decision makers should examine the following points carefully to avoid later disappointments or traps."

Myth 1: All Implementations Comprise Standard and Open Architectures

I&O leaders need to ask: what is a standard and open architecture? In the software-defined world of HCIS, the levels of standardization and openness depend increasingly on the codebase. It's important to be clear on who controls the code and who is responsible for its development, maintenance and performance. There are no software-defined standards, so one vendor's management controls may not manage another vendor's devices or software-defined network.

Myth 2: All Implementations Are Destined to Fail Mission-Critical Scalability and Resiliency Tests

HCIS implementations will vary widely in robustness, scalability and security. The vital context that needs to be considered is the intended use case. HCIS is designed and best-suited to high-availability and virtualized workloads. Yet even here there is wide variance; some HCIS clusters scale only to eight nodes, whereas others claim to scale to hundreds or even thousands.

"Caution is advised as this kind of scalability often does not fit the Gartner definition of a seamlessly managed HCIS appliance," Weiss said.

Myth 3: HCIS Costs Represent the Least-Expensive Deployment Model

HCIS infrastructure can be scaled up easily in small incremental adjustments by adding additional nodes as needed. However, over an extended period of time where the use-case demand rises and regularly requires additional nodes, the investment in HCIS could easily exceed an upfront investment.

Myth 4: The Most Important Use Case Is Virtual Desktop Infrastructure (VDI)

VDI has become the "celebrity" use case for HCIS. However, many general-purpose workloads are now a match for HCIS due to improved performance, scaling, data protection and ease of deployment, as well as an expanding hybrid cloud ecosystem. I&O leaders should expect further expansion to occur in the next three years to handle greater levels of agility, DevOps, containers, bimodal applications and consumer-based services.

Myth 5: HCIS Spells the Demise of Traditional Storage Arrays

HCIS has huge potential to replace small-to-midsize, general-purpose disk arrays in highly virtualized environments. In the case of large mission-critical applications that require predictable behavior and proven reliability, HCIS may be less effective. Once all capacity utilization and cost factors are considered, modern hybrid or solid-state array deployments are likely to be more economical over the long haul.

Myth 6: HCIS Eliminates Data Center Interoperability and Silos

HCIS lacks tight integration with existing traditional infrastructures, which forces I&O leaders to position them in silo deployments. The silo approach accommodates the existing administration and technical support of legacy operations. Nevertheless, HCIS also demands new models of team collaboration and specialty integrations that are different to existing legacy solutions. HCIS deployment models resonate most with IT leaders who want to switch from hardware stack management models to simple-to-deploy virtualized platform delivery.

Myth 7: Traditional Vendor Selection Preference Will Remain the Same

Gartner's focus group participants have shown that loyalty to traditional vendors would be tested by several criteria:

- Is the vendor increasingly fluent in the new wave of HCIS?

- Is it seriously willing to disrupt its conventional solutions?

- Does it have the vision to drive innovation?

- Can it keep ahead of emerging, agile competitors and increase its savings?

While there are risks in engaging with vendors that lack a solid track record, the commodity pricing of parts and infrastructure will alleviate some of that risk. It will become increasingly easy to navigate a worst-case vendor failure.

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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 ...