Businesses typically spend between 6% and 12% of their revenue specifically on cloud infrastructure, with high-growth SaaS companies often landing on the higher end of this spectrum (IBM). Across all corners of scaling infrastructure, companies spend roughly 5% to 7% of their total revenue on IT (Techvera), so there are big budgets that go into. 

Despite that, Gartner found 76% of digital transformation efforts fail to scale effectively due to poor infrastructure design and performance limitations.

So it’s not about big budgets, it’s about cost-effective strategies that work. Read on to find out more.

Use Demand Forecasting Before Buying Capacity

Companies should stop scaling infrastructure reactively. That’s such a big part of the issue.

Instead of buying more servers, licenses, cloud capacity, or networking bandwidth when demand rises, they should forecast which parts of the business actually need more capacity. Infrastructure demand is now distorted by AI, data-heavy applications, remote work, and real-time analytics.

 And with Gartner predicting global IT spending to pass $6 trillion in 2026, there has to be a logical approach to spending.

Approach infrastructure capacity like inventory. Buying too late creates outages and emergency procurement costs. Buying too early creates idle hardware, unused licenses, and unnecessary energy spend.

How to do demand forecasting

We recommend building a 12 to 18-month capacity forecast covering compute, storage, network traffic, software licenses, AI workloads, and data growth. Create separate normal business growth trends from “spiky” growth trends, we’ll call them, caused by one-off AI projects, marketing campaigns, seasonal demand, acquisitions, or product launches.

You should also create thresholds, such as “scale when average utilization exceeds 70% for 30 days.”

For mapping and usage visibility, you’ve got Datadog Cloud Management, Flexera One, ServiceNow IT Asset Management, or Device42 as great options.

Choose a Better Infrastructure Location

Location strategy is just as important as anything we’ve mentioned so far.  Data centers, which are where your physical servers, data storage drives (HDDs/SSDs), networking equipment (routers/switches), and security systems are stored, have such an impact on the quality of your infrastructure. Think cooling costs, connectivity, physical capacity, local regulation, etc.

The International Energy Agency reported that data center investment pushed the capital expenditure of five large technology companies to more than $400 billion in 2025, with spending expected to rise by a further 75% in 2026. 

They can be so expensive, and the CBRE also notes that limited power availability is now a major inhibitor of data center growth in core hub markets, with demand outpacing new supply.

So companies choosing the most familiar region, such as London, Frankfurt, Northern Virginia, or Dublin, may face higher costs, limited capacity, or worse procurement terms. A more practical strategy is to:

• Place latency-sensitive workloads closer to users.
• Place batch processing, backups, analytics, and archives in lower-cost regions where latency matters less.
• Consider secondary data center markets where capacity and energy pricing may be more attractive.
• Use content delivery networks to avoid overbuilding central infrastructure.

This also ties into modernizing your connectivity and uptime, and having more predictable infrastructure costs.

For smaller businesses, location strategy doesn’t always mean leasing space in a data center or building complex enterprise infrastructure. 

It can simply mean choosing a hosting provider with suitable server options, predictable pricing, and infrastructure that matches where your users are. Providers such as IONOS are a good option, and you can get an IONOS hosting discount that reduces upfront costs.

Rationalize SaaS and Internal Tools Before Scaling Core Infrastructure

A lot of articles you read online focus on cloud and server overload, but corporate infrastructure costs also rise due to software sprawl.

So companies run too many overlapping tools: multiple project management platforms, duplicated CRMs, unused collaboration tools, old monitoring platforms, and department-bought AI tools.

One 2026 analysis by Zylo reported that average SaaS spend per employee reached $4,830 in 2025, a 21.9% year-on-year increase.

It’s so much money, and any company that scales infrastructure without noticing duplicated tools is essentially paying twice. Once for the unnecessary software, and again for the infrastructure, integrations, security, storage, and support around it.

A practical strategy is to do an audit and find tools with less than 30% active usage and remove tools performing the same function. We also recommend putting all new SaaS and AI tools through IT and procurement review before you buy into them.

Productiv is a great tool to help you do all this if you want to focus on AI related tools, with its core feature being SaaS management and license optimization. Or you’ve got Zylo, which is a bit more general.

Scaling your corporate infrastructure doesn’t mean adding more tools and costs and feeling overwhelmed. You need to take a structured approach that focuses on overall infrastructure optimization and cost-saving strategies.