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Signs It May Be Time to Reassess Your IT Infrastructure Strategy

Mark Christie
StorMagic

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions.

This shift is already showing up as contracts come up for renewal, costs change and operating requirements tighten. In many cases, the question is no longer whether the original decision made sense, but whether it still makes sense today.

There are clear signs when a strategy no longer fits. Recognizing them early helps avoid being locked into a model that is difficult to unwind.

No. 1: The Terms Keep Changing

Predictability matters. If licensing models shift unexpectedly, bundles change or long-term costs become difficult to forecast, that points to misalignment. Many organizations have experienced this as consolidation has reshaped the virtualization market.

The impact is most visible in VMware environments following Broadcom's acquisition. Customers have seen pricing changes, revised packaging and adjustments to channel structures. For organizations that built their infrastructure around long-term assumptions, these changes introduce uncertainty at renewal time.

Higher costs alone are not the issue. The concern is when costs rise while flexibility decreases. When negotiation options narrow and contract terms tighten, leaders need to reassess the foundation those decisions were built on.

No. 2: Too Much Depends on One Environment

Large-scale cloud disruptions over the past year have demonstrated how quickly centralized systems can become single points of failure. Cloud platforms remain critical to modern IT strategies, but overreliance on any single environment concentrates risk.

In healthcare, retail, manufacturing and utilities, downtime directly affects patient care, transactions and production. When connectivity fails or a provider experiences an outage, core systems must continue operating locally.

This is why many organizations are distributing workloads across environments. Keeping critical applications closer to where they are used provides more control and reduces reliance on constant connectivity. It also allows teams to isolate failures rather than having them cascade across the entire environment.

No. 3: Complexity Is Increasing

Infrastructure should become easier to manage over time. If maintaining stability requires layering additional tools, stitching together multiple platforms or relying on increasingly specialized expertise, complexity may be compounding.

This often happens gradually. A new tool is added to solve a specific problem. Another system is layered in to address a gap. Over time, the environment becomes harder to manage, not easier.

That complexity introduces risk. It increases the chance of configuration errors and makes long-term planning harder. It also drives up operational costs, as teams spend more time maintaining the system instead of improving it.

If maintaining the environment requires constant effort just to keep it running, the architecture needs to be reconsidered.

No. 4: Innovation Feels Like a Rebuild

AI initiatives, real-time analytics and distributed workloads are changing infrastructure demands. Training may happen in the cloud, but inference and decision-making increasingly happen closer to where data is created.

If launching new workloads requires significant architectural changes, new hardware investments or renegotiated contracts, the foundation may not be built for modern requirements.

Infrastructure should enable experimentation and growth. If every new initiative feels like a major migration, that is worth examining.

No. 5: Familiarity Is Driving the Decision

Major infrastructure changes carry risk. Retraining teams, migrating workloads and evaluating alternatives require time and investment. Staying solely because a platform feels familiar can also introduce risk. Vendor strategies shift. Market conditions change, and business priorities evolve.

The most important question may be the simplest one: If you were designing your infrastructure strategy today, would you build it the same way?

If the answer isn't clear, it may be time to reconsider the current approach.

When It's Time to Reassess

Reassessment does not always mean replacing everything. In many cases, it means introducing additional options, shifting certain workloads or reducing dependence on a single platform.

Some organizations are moving toward smaller, more flexible deployments that run on standard hardware. Others are keeping core systems in place while shifting new workloads to environments that are easier to scale and manage.

The goal isn't just to adopt a new model for its own sake. The key is to ensure the infrastructure supports how the business operates today and can adapt as requirements change.

When systems begin to limit flexibility, increase cost unpredictably or require constant intervention, the strategy needs to be revisited.

Mark Christie is Sr. Director of Technical Services at StorMagic

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Signs It May Be Time to Reassess Your IT Infrastructure Strategy

Mark Christie
StorMagic

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions.

This shift is already showing up as contracts come up for renewal, costs change and operating requirements tighten. In many cases, the question is no longer whether the original decision made sense, but whether it still makes sense today.

There are clear signs when a strategy no longer fits. Recognizing them early helps avoid being locked into a model that is difficult to unwind.

No. 1: The Terms Keep Changing

Predictability matters. If licensing models shift unexpectedly, bundles change or long-term costs become difficult to forecast, that points to misalignment. Many organizations have experienced this as consolidation has reshaped the virtualization market.

The impact is most visible in VMware environments following Broadcom's acquisition. Customers have seen pricing changes, revised packaging and adjustments to channel structures. For organizations that built their infrastructure around long-term assumptions, these changes introduce uncertainty at renewal time.

Higher costs alone are not the issue. The concern is when costs rise while flexibility decreases. When negotiation options narrow and contract terms tighten, leaders need to reassess the foundation those decisions were built on.

No. 2: Too Much Depends on One Environment

Large-scale cloud disruptions over the past year have demonstrated how quickly centralized systems can become single points of failure. Cloud platforms remain critical to modern IT strategies, but overreliance on any single environment concentrates risk.

In healthcare, retail, manufacturing and utilities, downtime directly affects patient care, transactions and production. When connectivity fails or a provider experiences an outage, core systems must continue operating locally.

This is why many organizations are distributing workloads across environments. Keeping critical applications closer to where they are used provides more control and reduces reliance on constant connectivity. It also allows teams to isolate failures rather than having them cascade across the entire environment.

No. 3: Complexity Is Increasing

Infrastructure should become easier to manage over time. If maintaining stability requires layering additional tools, stitching together multiple platforms or relying on increasingly specialized expertise, complexity may be compounding.

This often happens gradually. A new tool is added to solve a specific problem. Another system is layered in to address a gap. Over time, the environment becomes harder to manage, not easier.

That complexity introduces risk. It increases the chance of configuration errors and makes long-term planning harder. It also drives up operational costs, as teams spend more time maintaining the system instead of improving it.

If maintaining the environment requires constant effort just to keep it running, the architecture needs to be reconsidered.

No. 4: Innovation Feels Like a Rebuild

AI initiatives, real-time analytics and distributed workloads are changing infrastructure demands. Training may happen in the cloud, but inference and decision-making increasingly happen closer to where data is created.

If launching new workloads requires significant architectural changes, new hardware investments or renegotiated contracts, the foundation may not be built for modern requirements.

Infrastructure should enable experimentation and growth. If every new initiative feels like a major migration, that is worth examining.

No. 5: Familiarity Is Driving the Decision

Major infrastructure changes carry risk. Retraining teams, migrating workloads and evaluating alternatives require time and investment. Staying solely because a platform feels familiar can also introduce risk. Vendor strategies shift. Market conditions change, and business priorities evolve.

The most important question may be the simplest one: If you were designing your infrastructure strategy today, would you build it the same way?

If the answer isn't clear, it may be time to reconsider the current approach.

When It's Time to Reassess

Reassessment does not always mean replacing everything. In many cases, it means introducing additional options, shifting certain workloads or reducing dependence on a single platform.

Some organizations are moving toward smaller, more flexible deployments that run on standard hardware. Others are keeping core systems in place while shifting new workloads to environments that are easier to scale and manage.

The goal isn't just to adopt a new model for its own sake. The key is to ensure the infrastructure supports how the business operates today and can adapt as requirements change.

When systems begin to limit flexibility, increase cost unpredictably or require constant intervention, the strategy needs to be revisited.

Mark Christie is Sr. Director of Technical Services at StorMagic

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...