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

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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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

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Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

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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 ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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

Related Links

Hot Topics

The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.