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Hyperconverged Infrastructure Part 2 - What's Included, What's in It for Me and How to Get Started

Alan Conboy
Scale Computing

Hyperconvergence is a term that is gaining rapid interest across the manufacturing industry due to the undeniable benefits it has delivered to IT professionals seeking to modernize their data center, or as is a popular buzzword today ― "transform." Today, in particular, the manufacturing industry is looking to hyperconvergence for the potential benefits it can provide to its emerging and growing use of IoT and its growing need for edge computing systems.

Start with Hyperconverged Infrastructure Part 1 - A Modern Infrastructure for Modern Manufacturing

Hyperconverged – What's Included?

Hyperconverged infrastructure is sometimes referred to as a "data center in a box" because, after the initial cabling and minimal networking configuration, it has all of the features and functionality of the traditional 3-2-1 virtualization architecture (except that single point of failure).

Rapid Deployment

Hyperconverged infrastructure systems can be deployed more rapidly than other virtualization solutions because of the appliance-based architecture. Racking and networking are often the most time-consuming factors in implementation. Deployment times vary by vendor, especially if there is a third party hypervisor to install and VSAs to configure but with a native hypervisor pre-loaded, an entire cluster of appliances can be up and running in under an hour.

Management

Hyperconverged infrastructure solutions can generally be managed from a single management interface, eliminating the multiple management consoles and interfaces found in 3-2-1 architectures. This is not necessarily the case for hyperconverged infrastructure solutions using third-party hypervisors which typically end up using two interfaces. For hyperconverged infrastructures with a native hypervisor included, this single interface approach significantly reduces management time and effort and simplifies management tasks for the administrator.

Backup and Disaster Recovery (DR)

Backup and disaster recovery are included at no extra cost in some hyperconverged infrastructure solutions to help eliminate yet another vendor from your IT environment. And truly, backup, failover, failback, and recovery should be a part of every IT environment. In that line of thought, it makes perfect sense to include these features natively in hyperconverged infrastructure solutions. Unlike third-party solutions, native solutions are typically embedded in the storage layer and allow innate awareness of block changes for cleaner backup, replication, and recovery options.

Clustering

Although a hyperconverged infrastructure can sometimes be deployed as a single appliance for selected use cases, it is usually deployed as a cluster of appliances for high availability (HA). This way, not only can an appliance absorb the loss of a disk drive, but the cluster can absorb the loss of an entire appliance. Clustering also allows the hyperconverged infrastructure system to scale seamlessly by adding more appliances to the cluster. Some hyperconverged infrastructure solutions require clustering appliances of the same model and configuration while others allow clustering of dissimilar appliances.

Software and Hardware Updates

Doing regular system software and firmware updates can be a dreaded task, but hyperconverged infrastructures tend to make this process easy. By owning the entire virtualization, server, and storage stack, and operating in a highly available cluster, updates can be performed automatically across the entire cluster. All software layers (hardware firmware, hypervisor, storage, and management) can be upgraded in unison as a single, fully tested system to eliminate component compatibility concerns. VMs can be automatically moved from appliance to appliance in the cluster as updates are made to keep all systems operational. hyperconverged infrastructure can eliminate downtime and headaches when performing updates.

Lower Cost of Ownership

A hyperconverged infrastructure may not always be the lowest cost solution in terms of the initial Capex investment ― although it often is because the ease of scalability allows organizations to purchase only the needed appliances and does not require excessive over-provisioning in the initial investment. Buying only what you need, when you need it, can lead to significant savings. In addition to Capex savings, a hyperconverged infrastructure provides considerable Opex savings over time by greatly reducing the costs of management and maintenance. Simplifying an IT environment with a hyperconverged infrastructure can save over 50 percent in the total cost of ownership over 3-2-1 solutions.

So, What's In It for Manufacturing Organizations?

Hyperconverged Infrastructure is designed as a replacement for 3-2-1 architecture to eliminate excess cost and complexity. Therefore, it can benefit any size organization that requires a robust virtualization environment. However, the extreme simplicity of a hyperconverged infrastructure makes it most beneficial in use cases where IT staff is limited. Small and medium business (SMB) and distributed enterprises with many remote offices or branch offices (ROBO) typically have staffing issues that make a hyperconverged infrastructure an ideal choice.

For a small to mid-sized manufacturing organization, the entire IT staff may be as small as only one full-time or even part-time IT administrator. The complexity of a 3-2-1 architecture can be extremely challenging. It can require levels of training and certification that make managing administrators either under-trained or too expensive to afford. The simplicity of a hyperconverged infrastructure, in turn, can allow it to be managed easily by a junior administrator or allow a more senior administrator to simply spend less time managing the infrastructure and more time delivering better applications and services and improving the business.

In a distributed manufacturing organization, remote offices and branch offices rarely have dedicated IT staff. These remote locations often require frequent visits from IT staff which can result in high travel costs and lower productivity. The simplicity of a hyperconverged infrastructure includes multiple redundancies for high availability, failure handling, and self-healing. A failed drive at a remote site does not cause an outage and does not require immediate replacement, cutting down on IT staff visits. Greater uptime and accessible remote monitoring and management lead to lower travel costs of IT staff to these locations and significantly lower operating costs ― not to mention the increase in productivity

How Do I Start?

Hyperconverged infrastructure is a revolutionary way for manufacturing organizations to think about IT infrastructure that reduces IT investments in terms of both money and manpower. Although it may be difficult to determine whether a solution is truly hyperconverged, or some other pretender, it is worth investigating hyperconverged infrastructure solutions to learn if/how your organization can gain the maximum benefit of modern IT infrastructure.

Here's where to start ― when speaking with a potential vendor ask:

■ Does the solution provide a native hypervisor or does it require an additional purchase of hypervisor licensing and support?

■ Does the solution offer hypervisor-embedded storage or does it use virtual storage appliances (VSAs)?

■ Can the solution combine and scale with dissimilar appliance models and configurations?

■ Does the solution offer native backup and DR capabilities?

■ Does the solution integrate with cloud computing? How?

As the manufacturing industry and its associated compute requirements continue to evolve, hyperconverged infrastructure is the next logical step in on-premises and cloud-integrated virtualization infrastructure. Standing still with more traditional virtualization solutions like the 3-2-1 architecture may end up costing organizations far more in capital, manpower, and training than switching over to the simplicity and savings of a hyperconverged infrastructure solution.

Alan Conboy is the Office of the CTO at Scale Computing

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Hyperconverged Infrastructure Part 2 - What's Included, What's in It for Me and How to Get Started

Alan Conboy
Scale Computing

Hyperconvergence is a term that is gaining rapid interest across the manufacturing industry due to the undeniable benefits it has delivered to IT professionals seeking to modernize their data center, or as is a popular buzzword today ― "transform." Today, in particular, the manufacturing industry is looking to hyperconvergence for the potential benefits it can provide to its emerging and growing use of IoT and its growing need for edge computing systems.

Start with Hyperconverged Infrastructure Part 1 - A Modern Infrastructure for Modern Manufacturing

Hyperconverged – What's Included?

Hyperconverged infrastructure is sometimes referred to as a "data center in a box" because, after the initial cabling and minimal networking configuration, it has all of the features and functionality of the traditional 3-2-1 virtualization architecture (except that single point of failure).

Rapid Deployment

Hyperconverged infrastructure systems can be deployed more rapidly than other virtualization solutions because of the appliance-based architecture. Racking and networking are often the most time-consuming factors in implementation. Deployment times vary by vendor, especially if there is a third party hypervisor to install and VSAs to configure but with a native hypervisor pre-loaded, an entire cluster of appliances can be up and running in under an hour.

Management

Hyperconverged infrastructure solutions can generally be managed from a single management interface, eliminating the multiple management consoles and interfaces found in 3-2-1 architectures. This is not necessarily the case for hyperconverged infrastructure solutions using third-party hypervisors which typically end up using two interfaces. For hyperconverged infrastructures with a native hypervisor included, this single interface approach significantly reduces management time and effort and simplifies management tasks for the administrator.

Backup and Disaster Recovery (DR)

Backup and disaster recovery are included at no extra cost in some hyperconverged infrastructure solutions to help eliminate yet another vendor from your IT environment. And truly, backup, failover, failback, and recovery should be a part of every IT environment. In that line of thought, it makes perfect sense to include these features natively in hyperconverged infrastructure solutions. Unlike third-party solutions, native solutions are typically embedded in the storage layer and allow innate awareness of block changes for cleaner backup, replication, and recovery options.

Clustering

Although a hyperconverged infrastructure can sometimes be deployed as a single appliance for selected use cases, it is usually deployed as a cluster of appliances for high availability (HA). This way, not only can an appliance absorb the loss of a disk drive, but the cluster can absorb the loss of an entire appliance. Clustering also allows the hyperconverged infrastructure system to scale seamlessly by adding more appliances to the cluster. Some hyperconverged infrastructure solutions require clustering appliances of the same model and configuration while others allow clustering of dissimilar appliances.

Software and Hardware Updates

Doing regular system software and firmware updates can be a dreaded task, but hyperconverged infrastructures tend to make this process easy. By owning the entire virtualization, server, and storage stack, and operating in a highly available cluster, updates can be performed automatically across the entire cluster. All software layers (hardware firmware, hypervisor, storage, and management) can be upgraded in unison as a single, fully tested system to eliminate component compatibility concerns. VMs can be automatically moved from appliance to appliance in the cluster as updates are made to keep all systems operational. hyperconverged infrastructure can eliminate downtime and headaches when performing updates.

Lower Cost of Ownership

A hyperconverged infrastructure may not always be the lowest cost solution in terms of the initial Capex investment ― although it often is because the ease of scalability allows organizations to purchase only the needed appliances and does not require excessive over-provisioning in the initial investment. Buying only what you need, when you need it, can lead to significant savings. In addition to Capex savings, a hyperconverged infrastructure provides considerable Opex savings over time by greatly reducing the costs of management and maintenance. Simplifying an IT environment with a hyperconverged infrastructure can save over 50 percent in the total cost of ownership over 3-2-1 solutions.

So, What's In It for Manufacturing Organizations?

Hyperconverged Infrastructure is designed as a replacement for 3-2-1 architecture to eliminate excess cost and complexity. Therefore, it can benefit any size organization that requires a robust virtualization environment. However, the extreme simplicity of a hyperconverged infrastructure makes it most beneficial in use cases where IT staff is limited. Small and medium business (SMB) and distributed enterprises with many remote offices or branch offices (ROBO) typically have staffing issues that make a hyperconverged infrastructure an ideal choice.

For a small to mid-sized manufacturing organization, the entire IT staff may be as small as only one full-time or even part-time IT administrator. The complexity of a 3-2-1 architecture can be extremely challenging. It can require levels of training and certification that make managing administrators either under-trained or too expensive to afford. The simplicity of a hyperconverged infrastructure, in turn, can allow it to be managed easily by a junior administrator or allow a more senior administrator to simply spend less time managing the infrastructure and more time delivering better applications and services and improving the business.

In a distributed manufacturing organization, remote offices and branch offices rarely have dedicated IT staff. These remote locations often require frequent visits from IT staff which can result in high travel costs and lower productivity. The simplicity of a hyperconverged infrastructure includes multiple redundancies for high availability, failure handling, and self-healing. A failed drive at a remote site does not cause an outage and does not require immediate replacement, cutting down on IT staff visits. Greater uptime and accessible remote monitoring and management lead to lower travel costs of IT staff to these locations and significantly lower operating costs ― not to mention the increase in productivity

How Do I Start?

Hyperconverged infrastructure is a revolutionary way for manufacturing organizations to think about IT infrastructure that reduces IT investments in terms of both money and manpower. Although it may be difficult to determine whether a solution is truly hyperconverged, or some other pretender, it is worth investigating hyperconverged infrastructure solutions to learn if/how your organization can gain the maximum benefit of modern IT infrastructure.

Here's where to start ― when speaking with a potential vendor ask:

■ Does the solution provide a native hypervisor or does it require an additional purchase of hypervisor licensing and support?

■ Does the solution offer hypervisor-embedded storage or does it use virtual storage appliances (VSAs)?

■ Can the solution combine and scale with dissimilar appliance models and configurations?

■ Does the solution offer native backup and DR capabilities?

■ Does the solution integrate with cloud computing? How?

As the manufacturing industry and its associated compute requirements continue to evolve, hyperconverged infrastructure is the next logical step in on-premises and cloud-integrated virtualization infrastructure. Standing still with more traditional virtualization solutions like the 3-2-1 architecture may end up costing organizations far more in capital, manpower, and training than switching over to the simplicity and savings of a hyperconverged infrastructure solution.

Alan Conboy is the Office of the CTO at Scale Computing

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...