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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...