Cut cloud costs without hurting performance

Executive summary

IT leaders continue to face contradictory infrastructure demands. They must regain control of spiraling cloud spend despite growing demand for compute, all while ensuring low latency and high performance. Many believe this is an impossible challenge, forcing them to choose between unsustainable cost inflation and unacceptable performance degradation.

The cost-performance dilemma: Why leaders struggle to optimize the cloud

While cloud is a critical component of nearly every organization’s IT strategy, cloud costs are spiraling. Nearly 40% of companies that saw cloud prices rise reported increases of more than 25% last year, with 85% now citing budget management as their biggest cloud-related challenge. New licensing models have driven up previously stable prices, while AI acceleration has made compute requirements and cost more unpredictable.

But cutting cloud spend is seen as a risk to performance, which is not an option given the competitive pressure most companies have. End-users now expect smooth, uninterrupted service, regardless of how much demand they place on servers. Across many industries, a single minute of downtime can cost companies thousands of dollars and cause lasting reputational harm. Businesses that experience frequent outages and brownouts require roughly 2x the staff to troubleshoot and take 2x longer to resolve the problems.

Many companies intentionally overprovision cloud to ensure excess capacity is available. While this can mitigate risk around outages, it leads to significant waste. The average CPU utilization across Kubernetes® clusters is just 10%. In extreme cases, companies can overprovision cloud compute by 400% or more.

Estimates vary on the exact level of cloud waste. BCG argues that up to 30% of cloud spend is wasted, while KPMG estimates that companies spend roughly 35% more on cloud resources than they need to meet performance requirements. But all research suggests unused cloud compute has a significant impact on overall costs.

Leaders are finding themselves in a feedback loop. Reducing costs risks disrupting service. Disrupted service increases costs. Addressing service disruptions requires extra maintenance spend, which adds to the pressure to cut costs.

Some companies believe AI investments will resolve the issue. If rapid adoption can drive high enough operational efficiency, the gains will recover the financial waste from cloud overspending. But that overlooks the more effective solution: finding ways to make cost reductions and performance improvements work together.

The reality is that cost and performance are both driven by infrastructure efficiency. Running the right workloads on the right infrastructure not only captures large cost savings but also delivers more reliable service.

Why traditional cloud infrastructure underdelivers on price and performance

The race to adopt cloud and accelerate digital transformation has left many businesses with infrastructure that is a poor fit for today’s competitive environment. Over one-third of mid-market companies feel they “rushed” into complex cloud migrations in recent years, which can lead to “cutting corners” and lower infrastructure quality.

Both cost and performance optimization are limited by three primary factors: 

Vendor lock-in

Many companies signed contracts with major cloud providers that leave them highly vulnerable to external disruptions. These providers allowed them to adopt and scale cloud fast, often at seemingly competitive rates. But as the industry has evolved, that commitment has become a liability.

Many companies have found hyperscaler costs creep up over the years, leading some to publicly leave the cloud. One company called its cloud bill “obscene”; despite heavily optimizing its usage, it had still racked up around $900,000 in AWS® costs.

Vendor lock-in also puts performance at risk. For many years, large-scale platforms were seen as inherently more stable, because extra capacity was almost always available. But two factors now challenge that narrative:

1. Concentration Risk: Recent outages across major cloud providers show that no amount of extra capacity will protect companies when the provider itself fails. Using a single vendor concentrates risk and allows a single outage to shut down entire companies’ IT systems.
2. System Inflexibility: Research shows a growing share of downtime stems from complexity rather than physical infrastructure. Vendor lock-in makes it harder for companies to adapt their systems to reduce the risk of latency and downtime. 

Costs are harder to manage, performance is harder to protect, and leaders are left with less control over their cloud system.

Limited visibility

Traditional cloud infrastructure rarely gives leaders real visibility into spend or usage. Costs accumulate across hundreds of services and dozens of teams, with no single view of what's running or why, leaving the people accountable for the budget reliant on after-the-fact billing rather than real-time insight. 

This problem has been exacerbated over the last three years due to rapid innovation. The adoption of new solutions, such as generative AI, has outpaced most teams’ capacity to map cloud usage. More than half of companies have had AI agents exceed their intended permissions. That is not just a security risk, but a scoping challenge for leaders trying to monitor and manage cloud compute. 

The majority of surveyed executives (70%) say teams across the business are deploying technology faster than IT can track. And most technology leaders say they’re accountable for systems they don’t fully control.

Importantly, this lack of visibility also impacts service delivery. When compute is used unpredictably and in unexpected environments, it can easily be depleted and cause latency or even downtime. Once again, we see that the underlying cause of overspending also compromises performance.

Poorly optimized workloads

Most existing cloud infrastructure lacks the flexibility required to optimize workloads. Limited visibility makes real-time allocation difficult. Workloads run on the same infrastructure, regardless of their latency requirements. And overprovisioning becomes the default way of maintaining performance.

Many solutions have emerged to optimize workload allocation, but companies often struggle to adopt them. Retrofitting AI-driven FinOps platforms or exerting control over real-time provisioning decisions is made difficult by legacy systems designed to deliver scale, not control or precision.

The solution to these problems is not to abandon the cloud. It is to rebuild infrastructure that addresses both cost and performance requirements. And that involves using three essential levers.

Taking back control: Three levers to improve cloud costs and performance

Match workloads to infrastructure

Many companies signed contracts with major cloud providers that leave them highly vulnerable to external disruptions. These providers allowed them to adopt and scale cloud fast, often at seemingly competitive rates. But as the industry has evolved, that commitment has become a liability.

Matched correctly, infrastructure becomes cheaper and more reliable at once. With public, private, and bare metal under one roof and servers configurable across hundreds of variations, OVHcloud® is built to let teams place each workload on the right tier rather than forcing everything through one.

Eliminate the structural cost traps

Many of the most persistent factors that drive up cloud spend accrue around compute, not the headline rate itself. Each has a fix that leaves performance untouched:

None of these factors ensures performance; most don’t even influence it. Removing them reduces costs without impacting performance. And that is where pricing models matter as much as pricing levels: OVHcloud charges nothing for ingress or egress, bills by the minute, and lets customers size to the workload instead of rounding up into bundles.

Use open standards to preserve optionality

The most expensive lock-in is architectural. A contract can be renegotiated, but a workload rebuilt on a provider's proprietary services cannot move without re-engineering it first:

The takeaway is simple: Optionality is a practical cost-control and resilience mechanism, and it is the reason building on open foundations keeps the freedom to move where it belongs.

About OVHcloud®

OVHcloud US is a subsidiary of OVHcloud, a global cloud leader and Europe’s top cloud provider, operating over 500,000 servers across 46 data centers on four continents to serve 1.6 million customers in over 140 countries. As a pioneer in trusted and sustainable cloud solutions with an excellent price-performance ratio, OVHcloud has spent over 20 years leveraging an integrated model that ensures total control of its value chain — from server design and data center construction to the orchestration of its fiber-optic network. This unique approach allows OVHcloud to independently meet all customer needs while providing the benefits of an environmentally conscious model, characterized by resource efficiency and one of the lowest carbon footprints in the cloud industry. OVHcloud offers latest-generation solutions that combine high performance, predictable pricing, and data residency to support scalable growth.

Kubernetes is a registered trademark of The Linux Foundation. AWS is a registered trademark of Amazon.com, Inc. or its affiliates. OpenStack is a registered trademark of the Open Infrastructure Foundation. Docker is a registered trademark of Docker, Inc.