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2025 Cloud and FinOps Predictions - Part 3

As part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how Cloud, FinOps and related technologies will evolve and impact business in 2025. Part 3 covers FinOps.

FOCUS ON COST MANAGEMENT

Cloud cost management strategies will be increasingly driven by emerging technologies like AI and containerization. As more organizations adopt technologies like AI and containerization, robust visibility into their cloud costs will become even more important to make better decisions. With the use of edge content networks and more storage, organizations in 2025 will increasingly need a better handle on cloud cost management.

Engineering and finance teams will increasingly need to understand how to normalize the cost data from many providers and then understand unit economics. AI features are exciting but often are associated with significant and variable costs. Many of those costs will be worthwhile, but without real-time monitoring of their unit economics, cost management teams will struggle to help their companies maximize those AI features' reach and economic impact.

Another factor here is that more companies are shifting to outcome-based pricing for AI products, which means pricing will be tied directly to the outcomes delivered by AI agents. As a result, you can't set prices without understanding the cost per AI agent outcome, making a modern cloud cost management strategy essential.
Bill Buckley
SVP of Engineering, CloudZero

FINOPS SHIFT FROM COST MANAGEMENT TO STRATEGIC VALUE

2025 will mark the end of operational FinOps, shifting from operational cost managers to strategic value enablers. This transformation will be driven by two factors: the increasing complexity of hybrid cloud environments and the C-suite's growing focus on technology ROI. Organizations that fail to make this transition risk being left behind as cloud costs continue to accelerate. This will create disruption for FinOps teams that are used to being operators and not strategists, and will challenge the notion of DIY/Native tooling usage.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

REAL-TIME INSIGHTS

Higher demand for real-time insights: Many teams are still struggling to predict cloud costs, with many lacking the right tools or knowledge to make early estimates. That could be a wake-up call for leaders with a false sense of how prepared their teams are to manage cloud costs. The good news is that we expect to see continued advancements in monitoring tools in the coming year, which should improve real-time insights. The best tools will continue to offer easy-to-leverage ETL and normalization to allow a single pane of glass across all costs. They will also, as AI spending increases, make it easier and easier to bring in non-cost spending, like revenue, to understand unit economics.
Bill Buckley
SVP of Engineering, CloudZero

CLOUD VALUE OPTIMIZATION

The focus will shift from cloud-first to cloud-right as organizations demand deeper insights into workload economics across their entire cloud fabric. Understanding unit costs — especially in private cloud environments — will become crucial for strategic decision-making. We'll see the emergence of new metrics and KPIs designed specifically to measure cloud ROI and effectiveness across hybrid environments.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

CLOUD UNIT ECONOMICS

More companies will turn to cloud unit economics to understand their cloud spend: Global cloud spending is poised to reach at least $824 billion in 2025. For SaaS companies, cloud spending is in the top three areas of overall expenditure, and AI is driving up cloud costs faster than expected. In fact, for 73% of companies, cloud costs consume at least 6% of revenue. Organizations need to find a way to change the status quo. As a result, they will increasingly turn to cloud unit economics to create more profitable pricing and packaging — including using dynamic pricing to change prices based on current market demands. To do this effectively, organizations must understand how efficient their cloud cost spending is and how much revenue is spent on cloud costs.
Bill Buckley
SVP of Engineering, CloudZero

HYBRID CLOUD FINOPS

FinOps is evolving, with many companies considering on-prem or moving workloads back from the cloud. At FinOpsX, companies were looking at blended costs of on-prem and cloud. Oracle has now joined the big three of Microsoft, Google and AWS, and it'll be interesting to see who else jumps in.
Dana Hernandez
Analyst, GigaOm

AI REALITY CHECK

AI Reality Check - From Gold Rush to Trough of Disillusionment: The AI gold rush will force a fundamental rethinking of cloud economics in 2025. As organizations grapple with unprecedented GPU demands and costs, we'll see public cloud laggards make aggressive moves to public cloud. However, as AI progresses through the Hype Cycle, expect an AI reality check by Q3. Organizations will shift from the exuberance of AI Everything to a more measured focus on tangible business outcomes and ROI. This tension will push cloud providers to innovate new pricing models and optimization levers specifically for AI workloads.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

AI-DRIVEN CLOUD ECONOMICS

AI-Driven Cloud Economics Reshape Infrastructure Decisions — In 2025, organizations will fundamentally reshape their cloud strategies around AI economics. The focus will shift from traditional cloud cost optimization to AI-specific ROI optimization. Organizations will develop sophisticated modeling capabilities to understand and predict AI workload costs across different infrastructure options. This will lead to more nuanced hybrid deployment strategies where companies carefully balance the cost-performance trade-offs of training and inference workloads across cloud providers and on-premises infrastructure.
Haoyuan Li
Founder and CTO, Alluxio

COMPREHENSIVE CLOUD MANAGEMENT PLATFORMS

Platform Consolidation and Integration Propelled by FOCUS: 2025 will be the year of the platform play in cloud management, catalyzed by the widespread adoption of FOCUS (FinOps Open Cost and Usage Specification). As this standard enables unified visibility across public clouds, private infrastructure, and data centers, organizations will reject today's fragmented tooling landscape in favor of comprehensive platforms that can treat their entire hybrid environment as a single cloud fabric. This shift will drive significant market consolidation as vendors race to deliver complete visibility and automation across all technology investments. For the first time, organizations will be able to make truly workload-optimized decisions based on complete economic and performance data.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

REDEFINING FINOPS ROLE

The FinOps role will need redefining. The expectations for FinOps professionals are growing unsustainably broad, which will prompt a need to redefine these roles. For instance, many job descriptions ask FinOps professionals to have DevOps, architecture, and accounting skills — essentially, wearing all hats simultaneously. This could spark debates within the community about whether the role needs more specialization or if companies are setting themselves up for failure by demanding too much from too few people. This could be one reason why the market doesn't seem to have caught up in terms of hiring for FinOps roles. In 2025, organizations must look closer at how they define these roles and reevaluate the talent they seek.
Bill Buckley
SVP of Engineering, CloudZero

Hot Topics

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...

2025 Cloud and FinOps Predictions - Part 3

As part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how Cloud, FinOps and related technologies will evolve and impact business in 2025. Part 3 covers FinOps.

FOCUS ON COST MANAGEMENT

Cloud cost management strategies will be increasingly driven by emerging technologies like AI and containerization. As more organizations adopt technologies like AI and containerization, robust visibility into their cloud costs will become even more important to make better decisions. With the use of edge content networks and more storage, organizations in 2025 will increasingly need a better handle on cloud cost management.

Engineering and finance teams will increasingly need to understand how to normalize the cost data from many providers and then understand unit economics. AI features are exciting but often are associated with significant and variable costs. Many of those costs will be worthwhile, but without real-time monitoring of their unit economics, cost management teams will struggle to help their companies maximize those AI features' reach and economic impact.

Another factor here is that more companies are shifting to outcome-based pricing for AI products, which means pricing will be tied directly to the outcomes delivered by AI agents. As a result, you can't set prices without understanding the cost per AI agent outcome, making a modern cloud cost management strategy essential.
Bill Buckley
SVP of Engineering, CloudZero

FINOPS SHIFT FROM COST MANAGEMENT TO STRATEGIC VALUE

2025 will mark the end of operational FinOps, shifting from operational cost managers to strategic value enablers. This transformation will be driven by two factors: the increasing complexity of hybrid cloud environments and the C-suite's growing focus on technology ROI. Organizations that fail to make this transition risk being left behind as cloud costs continue to accelerate. This will create disruption for FinOps teams that are used to being operators and not strategists, and will challenge the notion of DIY/Native tooling usage.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

REAL-TIME INSIGHTS

Higher demand for real-time insights: Many teams are still struggling to predict cloud costs, with many lacking the right tools or knowledge to make early estimates. That could be a wake-up call for leaders with a false sense of how prepared their teams are to manage cloud costs. The good news is that we expect to see continued advancements in monitoring tools in the coming year, which should improve real-time insights. The best tools will continue to offer easy-to-leverage ETL and normalization to allow a single pane of glass across all costs. They will also, as AI spending increases, make it easier and easier to bring in non-cost spending, like revenue, to understand unit economics.
Bill Buckley
SVP of Engineering, CloudZero

CLOUD VALUE OPTIMIZATION

The focus will shift from cloud-first to cloud-right as organizations demand deeper insights into workload economics across their entire cloud fabric. Understanding unit costs — especially in private cloud environments — will become crucial for strategic decision-making. We'll see the emergence of new metrics and KPIs designed specifically to measure cloud ROI and effectiveness across hybrid environments.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

CLOUD UNIT ECONOMICS

More companies will turn to cloud unit economics to understand their cloud spend: Global cloud spending is poised to reach at least $824 billion in 2025. For SaaS companies, cloud spending is in the top three areas of overall expenditure, and AI is driving up cloud costs faster than expected. In fact, for 73% of companies, cloud costs consume at least 6% of revenue. Organizations need to find a way to change the status quo. As a result, they will increasingly turn to cloud unit economics to create more profitable pricing and packaging — including using dynamic pricing to change prices based on current market demands. To do this effectively, organizations must understand how efficient their cloud cost spending is and how much revenue is spent on cloud costs.
Bill Buckley
SVP of Engineering, CloudZero

HYBRID CLOUD FINOPS

FinOps is evolving, with many companies considering on-prem or moving workloads back from the cloud. At FinOpsX, companies were looking at blended costs of on-prem and cloud. Oracle has now joined the big three of Microsoft, Google and AWS, and it'll be interesting to see who else jumps in.
Dana Hernandez
Analyst, GigaOm

AI REALITY CHECK

AI Reality Check - From Gold Rush to Trough of Disillusionment: The AI gold rush will force a fundamental rethinking of cloud economics in 2025. As organizations grapple with unprecedented GPU demands and costs, we'll see public cloud laggards make aggressive moves to public cloud. However, as AI progresses through the Hype Cycle, expect an AI reality check by Q3. Organizations will shift from the exuberance of AI Everything to a more measured focus on tangible business outcomes and ROI. This tension will push cloud providers to innovate new pricing models and optimization levers specifically for AI workloads.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

AI-DRIVEN CLOUD ECONOMICS

AI-Driven Cloud Economics Reshape Infrastructure Decisions — In 2025, organizations will fundamentally reshape their cloud strategies around AI economics. The focus will shift from traditional cloud cost optimization to AI-specific ROI optimization. Organizations will develop sophisticated modeling capabilities to understand and predict AI workload costs across different infrastructure options. This will lead to more nuanced hybrid deployment strategies where companies carefully balance the cost-performance trade-offs of training and inference workloads across cloud providers and on-premises infrastructure.
Haoyuan Li
Founder and CTO, Alluxio

COMPREHENSIVE CLOUD MANAGEMENT PLATFORMS

Platform Consolidation and Integration Propelled by FOCUS: 2025 will be the year of the platform play in cloud management, catalyzed by the widespread adoption of FOCUS (FinOps Open Cost and Usage Specification). As this standard enables unified visibility across public clouds, private infrastructure, and data centers, organizations will reject today's fragmented tooling landscape in favor of comprehensive platforms that can treat their entire hybrid environment as a single cloud fabric. This shift will drive significant market consolidation as vendors race to deliver complete visibility and automation across all technology investments. For the first time, organizations will be able to make truly workload-optimized decisions based on complete economic and performance data.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

REDEFINING FINOPS ROLE

The FinOps role will need redefining. The expectations for FinOps professionals are growing unsustainably broad, which will prompt a need to redefine these roles. For instance, many job descriptions ask FinOps professionals to have DevOps, architecture, and accounting skills — essentially, wearing all hats simultaneously. This could spark debates within the community about whether the role needs more specialization or if companies are setting themselves up for failure by demanding too much from too few people. This could be one reason why the market doesn't seem to have caught up in terms of hiring for FinOps roles. In 2025, organizations must look closer at how they define these roles and reevaluate the talent they seek.
Bill Buckley
SVP of Engineering, CloudZero

Hot Topics

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...