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Inefficient Apps Cause Overspending by Millions on Cloud

Enterprises with services operating in the cloud are overspending by millions due to inefficiencies with their apps and runtime environments, according to a poll conducted by Lead to Market, and commissioned by Opsani.

69 Percent of respondents report regularly overspending on their cloud budget by 25 percent or more, leading to a loss of millions on unnecessary cloud spend. Respondents were a mix of 100 companies using the leading public clouds — AWS, Azure, and Google — internal clouds, and "others," that were verified as spending more than $5 million annually on the cloud.

Gartner predicts that by 2022 overall cloud spend will reach more than $330 billion. Current estimates reveal that, even now, billions of this is the result of needless and wasted outlay. Why? Because resources are over-provisioned in order to buy peace of mind, and performance tuning is only happening in scenarios when an SLA isn't met, instead of continuously, as new code is released.

Of the poll respondents, 45 percent are releasing software in weekly, daily or hourly sprints. 65 percent of these companies plan on deploying their mainstream production applications on containers within the next 12 months. However, despite this trend toward DevOps and microservices, only 43 percent of respondents are confident their applications are running efficiently in the cloud, which leads to sub-par user experiences and over-paying for unneeded resources.

Modern enterprises are neglecting the post-release portion of the delivery pipeline — continuous optimization of live cloud apps and their environments.

Survey respondents indicated that:

■ 49 percent cite improving application performance as the most important priority for their organization.

■ 54 percent report that their organization has only optimized their application stack in the event of an emergency.

■ 48 percent point to manual time-consuming processes as the biggest hurdle to application optimization due to complexity; even a simple five container application can have more than 255-trillion resource and basic parameter permutations. It's beyond human scale.

Polled companies were also asked what their biggest priorities were for DevOps moving forward. Options were: reducing cloud spend by more than 30 percent, improving application performance by more than 20 percent, or accelerating release cycles by more than 200 percent:

■ Reducing cloud spend by more than 30%: 39 percent of respondents

■ Getting 20% better app performance: 32 percent of respondents

■ Accelerating release cycles by more than 200%: 23 percent of respondents

And overspending for cloud apps only goes up as services get traction. Take a company currently spending $50mm on the cloud. If it's growing at 20 percent year-on-year, the total cloud spend will be more than $372mm over the next five years. 20 percent of that $372mm is unnecessary spend — that's more than $60mm in overspend.

"Modern enterprises are using the cloud to reduce the costs of operating data centers, scale exponentially, bring value-added services online faster and more efficiently, and enjoy the flexibility of using resources as needed," said Ross Schibler, co-founder and CEO, Opsani. "But, operating in the cloud comes with costs that, if not managed continuously, can climb fast due to over provisioning and a lack of visibility into how live applications are affected by the CI/CD toolchain. Even small changes to live code disrupt tuned applications that lead to weak performance and higher costs."

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Inefficient Apps Cause Overspending by Millions on Cloud

Enterprises with services operating in the cloud are overspending by millions due to inefficiencies with their apps and runtime environments, according to a poll conducted by Lead to Market, and commissioned by Opsani.

69 Percent of respondents report regularly overspending on their cloud budget by 25 percent or more, leading to a loss of millions on unnecessary cloud spend. Respondents were a mix of 100 companies using the leading public clouds — AWS, Azure, and Google — internal clouds, and "others," that were verified as spending more than $5 million annually on the cloud.

Gartner predicts that by 2022 overall cloud spend will reach more than $330 billion. Current estimates reveal that, even now, billions of this is the result of needless and wasted outlay. Why? Because resources are over-provisioned in order to buy peace of mind, and performance tuning is only happening in scenarios when an SLA isn't met, instead of continuously, as new code is released.

Of the poll respondents, 45 percent are releasing software in weekly, daily or hourly sprints. 65 percent of these companies plan on deploying their mainstream production applications on containers within the next 12 months. However, despite this trend toward DevOps and microservices, only 43 percent of respondents are confident their applications are running efficiently in the cloud, which leads to sub-par user experiences and over-paying for unneeded resources.

Modern enterprises are neglecting the post-release portion of the delivery pipeline — continuous optimization of live cloud apps and their environments.

Survey respondents indicated that:

■ 49 percent cite improving application performance as the most important priority for their organization.

■ 54 percent report that their organization has only optimized their application stack in the event of an emergency.

■ 48 percent point to manual time-consuming processes as the biggest hurdle to application optimization due to complexity; even a simple five container application can have more than 255-trillion resource and basic parameter permutations. It's beyond human scale.

Polled companies were also asked what their biggest priorities were for DevOps moving forward. Options were: reducing cloud spend by more than 30 percent, improving application performance by more than 20 percent, or accelerating release cycles by more than 200 percent:

■ Reducing cloud spend by more than 30%: 39 percent of respondents

■ Getting 20% better app performance: 32 percent of respondents

■ Accelerating release cycles by more than 200%: 23 percent of respondents

And overspending for cloud apps only goes up as services get traction. Take a company currently spending $50mm on the cloud. If it's growing at 20 percent year-on-year, the total cloud spend will be more than $372mm over the next five years. 20 percent of that $372mm is unnecessary spend — that's more than $60mm in overspend.

"Modern enterprises are using the cloud to reduce the costs of operating data centers, scale exponentially, bring value-added services online faster and more efficiently, and enjoy the flexibility of using resources as needed," said Ross Schibler, co-founder and CEO, Opsani. "But, operating in the cloud comes with costs that, if not managed continuously, can climb fast due to over provisioning and a lack of visibility into how live applications are affected by the CI/CD toolchain. Even small changes to live code disrupt tuned applications that lead to weak performance and higher costs."

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

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 ...