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4 Insights into Modern Cloud Inefficiency

Bill Buckley
CloudZero

For the last 18 years — through pandemic times, boom times, pullbacks, and more — little has been predictable except one thing: Worldwide cloud spending will be higher this year than last year and a lot higher next year. But as companies spend more, are they spending more intelligently? Just how efficient are our modern SaaS systems?

CloudZero's new report, How Cloud Efficient Are Software Companies In 2024?, found that most companies have limited (or nonexistent) cloud cost management (CCM) programs, which prevent them from making a substantial profit.

This is particularly concerning in the context of AI spending, which will only exacerbate cloud inefficiency. AI, which has gripped the SaaS world as firmly as it has the public imagination, is notoriously difficult to manage. Immature CCM programs will buckle under its complexity and fail to maximize the profitability of AI-driven applications.

The good news is that companies can take some simple steps to remediate these issues. Let's look at some of this survey's key findings and how companies can reverse the trend of inefficiency and maximize their cloud profitability.

Most Companies Don't Proactively Manage Their Cloud Costs

One of the survey's most troubling findings is that 61% of companies don't have a formalized CCM program. This is consistent with The State of FinOps 2024, a report by the FinOps Foundation, which showed that 62% of companies are in the least mature stage (the "Crawl" stage) of FinOps. Formalized CCM spans numerous functions, from the most straightforward budgeting and forecasting to the most complex unit economics calculation, but most fundamental to CCM is cost allocation.

Cost allocation means assigning appropriate costs to individual customers, products, features, teams, microservices, etc. Complete cost allocation shows companies precisely what's driving their spending — and, by extension, where they're most (and least) efficient. The report found that just 9% of companies have complete or near-complete cost allocation.

If you don't know what's driving your spending, it's impossible to drive meaningful efficiencies. Upon instituting a formalized CCM program, companies reduce their cloud costs by 30% in the first year. Low allocation and infrequency of formalized CCM programs would suggest that companies are leaving profit on the table — and the next key finding confirms it.

COGS Inefficiency: Companies Are Leaving Profit on the Table

Roughly three-quarters of survey respondents said their cloud expenses account for at least 20% of their cost of goods sold (COGS), and more than a quarter (28%) said cloud costs account for more than half of their COGS. Since COGS is a key factor in gross margin (i.e., profitability) calculations, and companies that institute CCM programs tend to reduce their cloud costs by 30%, companies are leaving a lot of profit on the table.

A company with $100 million in revenue and $25 million in COGS would have a gross profit of 75% — good, in SaaS terms, but not elite. Now, imagine that their cloud costs represent 50% of their COGS — $12.5 million. A 30% reduction would lower their cloud costs to $8.75 million and their overall COGS to $21.25 million. Their gross profit would grow to 78.75% — near-elite.

Organizations Aren't Using the Most Powerful CCM Methods

An absence of strong CCM also means companies tend not to use its most powerful approaches — namely, software code optimization. While about half of companies take advantage of simple CCM methods — enterprise discounts, bulk purchasing discounts — just 28% of companies practice software code optimization. Software code optimization entails ad-hoc code fixes that make the software run more efficiently and at the highest scale levels, saving companies millions of dollars.

Software code optimization requires well-allocated, real-time, highly granular cost data and systems to notify the correct engineers when costs spike. Given that just 31% of companies have formal CCM programs, it's not surprising that roughly the same portion uses software code optimization.

Elite Cloud Efficiency Rate (CER): 92%+

Cloud Efficiency Rate (CER) is a universal benchmark for cloud cost efficiency. It compares your revenue to your cloud spend and shows you how much of every revenue dollar you keep versus how much you send to cloud providers. A company with an 80% CER sends $0.20 of every revenue dollar to its cloud providers; a company with a 90% CER sends $0.10 of every dollar to its cloud providers. The report shows that the top-quartile CER is 92%, meaning the most cloud-efficient SaaS companies send just $0.08 of every revenue dollar to their cloud providers.

CERs also tend to worsen as companies scale and add engineers. Angel/bootstrapped companies reported the highest median CER (92%), with every other category reporting significantly worse median CERs (80% across public, debt/private equity, and venture capital). Companies with 11–25 engineers have the highest median CER (87%), with 51–100 (75%) and 100+ (80%) representing significant CER declines.

Increase Your Profitability with CCM

Organizations that want to grow, maintain a high pace of innovation, and increase their cloud efficiency need to compare their CER to industry benchmarks, at minimum. To drive elite CER, they will need sophisticated CCM programs. This involves engagement from the engineering function, precise budgeting, complete allocation, and clear unit economics.

Technical teams buy cloud resources and manage their costs, so they're positioned to have the most positive impact on cloud efficiency. Providing technical teams with relevant, real-time cloud cost data will empower them to make better infrastructure and code decisions. This will make innovations more durable and result in a healthier bottom line for the business.

Methodology: CloudZero, in partnership with Benchmarkit, a B2B SaaS research firm, conducted the report. More than 700 cloud operations and finance professionals at SaaS companies throughout North America were surveyed on all things cloud spending.

Bill Buckley is SVP of Engineering at CloudZero

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4 Insights into Modern Cloud Inefficiency

Bill Buckley
CloudZero

For the last 18 years — through pandemic times, boom times, pullbacks, and more — little has been predictable except one thing: Worldwide cloud spending will be higher this year than last year and a lot higher next year. But as companies spend more, are they spending more intelligently? Just how efficient are our modern SaaS systems?

CloudZero's new report, How Cloud Efficient Are Software Companies In 2024?, found that most companies have limited (or nonexistent) cloud cost management (CCM) programs, which prevent them from making a substantial profit.

This is particularly concerning in the context of AI spending, which will only exacerbate cloud inefficiency. AI, which has gripped the SaaS world as firmly as it has the public imagination, is notoriously difficult to manage. Immature CCM programs will buckle under its complexity and fail to maximize the profitability of AI-driven applications.

The good news is that companies can take some simple steps to remediate these issues. Let's look at some of this survey's key findings and how companies can reverse the trend of inefficiency and maximize their cloud profitability.

Most Companies Don't Proactively Manage Their Cloud Costs

One of the survey's most troubling findings is that 61% of companies don't have a formalized CCM program. This is consistent with The State of FinOps 2024, a report by the FinOps Foundation, which showed that 62% of companies are in the least mature stage (the "Crawl" stage) of FinOps. Formalized CCM spans numerous functions, from the most straightforward budgeting and forecasting to the most complex unit economics calculation, but most fundamental to CCM is cost allocation.

Cost allocation means assigning appropriate costs to individual customers, products, features, teams, microservices, etc. Complete cost allocation shows companies precisely what's driving their spending — and, by extension, where they're most (and least) efficient. The report found that just 9% of companies have complete or near-complete cost allocation.

If you don't know what's driving your spending, it's impossible to drive meaningful efficiencies. Upon instituting a formalized CCM program, companies reduce their cloud costs by 30% in the first year. Low allocation and infrequency of formalized CCM programs would suggest that companies are leaving profit on the table — and the next key finding confirms it.

COGS Inefficiency: Companies Are Leaving Profit on the Table

Roughly three-quarters of survey respondents said their cloud expenses account for at least 20% of their cost of goods sold (COGS), and more than a quarter (28%) said cloud costs account for more than half of their COGS. Since COGS is a key factor in gross margin (i.e., profitability) calculations, and companies that institute CCM programs tend to reduce their cloud costs by 30%, companies are leaving a lot of profit on the table.

A company with $100 million in revenue and $25 million in COGS would have a gross profit of 75% — good, in SaaS terms, but not elite. Now, imagine that their cloud costs represent 50% of their COGS — $12.5 million. A 30% reduction would lower their cloud costs to $8.75 million and their overall COGS to $21.25 million. Their gross profit would grow to 78.75% — near-elite.

Organizations Aren't Using the Most Powerful CCM Methods

An absence of strong CCM also means companies tend not to use its most powerful approaches — namely, software code optimization. While about half of companies take advantage of simple CCM methods — enterprise discounts, bulk purchasing discounts — just 28% of companies practice software code optimization. Software code optimization entails ad-hoc code fixes that make the software run more efficiently and at the highest scale levels, saving companies millions of dollars.

Software code optimization requires well-allocated, real-time, highly granular cost data and systems to notify the correct engineers when costs spike. Given that just 31% of companies have formal CCM programs, it's not surprising that roughly the same portion uses software code optimization.

Elite Cloud Efficiency Rate (CER): 92%+

Cloud Efficiency Rate (CER) is a universal benchmark for cloud cost efficiency. It compares your revenue to your cloud spend and shows you how much of every revenue dollar you keep versus how much you send to cloud providers. A company with an 80% CER sends $0.20 of every revenue dollar to its cloud providers; a company with a 90% CER sends $0.10 of every dollar to its cloud providers. The report shows that the top-quartile CER is 92%, meaning the most cloud-efficient SaaS companies send just $0.08 of every revenue dollar to their cloud providers.

CERs also tend to worsen as companies scale and add engineers. Angel/bootstrapped companies reported the highest median CER (92%), with every other category reporting significantly worse median CERs (80% across public, debt/private equity, and venture capital). Companies with 11–25 engineers have the highest median CER (87%), with 51–100 (75%) and 100+ (80%) representing significant CER declines.

Increase Your Profitability with CCM

Organizations that want to grow, maintain a high pace of innovation, and increase their cloud efficiency need to compare their CER to industry benchmarks, at minimum. To drive elite CER, they will need sophisticated CCM programs. This involves engagement from the engineering function, precise budgeting, complete allocation, and clear unit economics.

Technical teams buy cloud resources and manage their costs, so they're positioned to have the most positive impact on cloud efficiency. Providing technical teams with relevant, real-time cloud cost data will empower them to make better infrastructure and code decisions. This will make innovations more durable and result in a healthier bottom line for the business.

Methodology: CloudZero, in partnership with Benchmarkit, a B2B SaaS research firm, conducted the report. More than 700 cloud operations and finance professionals at SaaS companies throughout North America were surveyed on all things cloud spending.

Bill Buckley is SVP of Engineering at CloudZero

Hot Topics

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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 ...

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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 ...