2025 Cloud and FinOps Predictions - Part 1

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.
 

CLOUD: ESSENTIAL INFRASTRUCTURE

In 2025, the cloud will evolve into a core platform prioritizing simplicity, security, and compliance. By integrating AI-driven insights and automation, cloud environments will empower diverse teams — not just developers — to drive efficient, secure, and compliant workflows. This growth will solidify the cloud as essential infrastructure, supporting seamless digital operations and driving strategic business growth. 
Gab Menachem
VP ITOM, ServiceNow

AI DRIVES CLOUD UTILIZATION

AI will significantly increase cloud utilization in 2025. The latest advancements in AI hardware, which come at a premium, require the use of hundreds of GPUs for model training. In addition, the demand for these GPUs has led them to be sold out for the next 12 months. If organizations are looking to train their own model or operationalize it with inference, there may be no other choice than to purchase it as a service from a cloud provider. As an alternative, they can utilize existing models and managed services from the cloud providers to make the deployment and management of a model simpler. Regardless of the method, AI is cost prohibitive as a DIY venture. The power of the cloud will enable customers to use the AI services and tools at the scale and flexibility that they need. Purchasing it as a service allows more organizations to access the technology and innovate, benefiting us all.
Jason Bright
Product Marketing Manager at Hyland

AI-DRIVEN AUTOMATION

AI in the cloud moves from simply "spotting things" to actually "doing things." Beyond data crunching and spitting out insights, AI-driven automation that turns insights into actions, automatically optimizes cloud performance and spend, and reduces the insight to action gap becomes the new table stakes by the end of 2025. Agentic AI gains rapid adoption and is integrated into workflows to accelerate AI impact such that the industry begins seeing "near-realtime FinOps" for the first time. And AI begins playing a bigger role in spotting anomalies and making decisions at moments of truth at the edge as organizations continue finding ways to shift left.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

HYBRID CLOUD

In 2025, we will see an even greater push toward diversified IT infrastructure. It's no longer about being all on-prem, all-cloud, or all-SaaS — companies are finding a need to balance across these platforms to drive efficiency and better manage costs. We've had enough time with these technologies to know what works where, and we're getting smarter about budgeting for them.
Chrystal Taylor
Evangelist, SolarWinds

AI DRIVES HYBRID CLOUD

Thanks to AI, Hybrid Cloud is Here to Stay: Only about two years ago, it was a very cloud-only environment with some companies ready to get rid of their data centers altogether. The reality is, many businesses still have over half their data living outside of the cloud, and it will likely stay there based on what makes the most sense for their use case (in high stakes environments such as healthcare, for example). Therefore, hybrid cloud strategies are alive and well, especially with the proliferation of AI. Organizations can maintain on-premises GPU infrastructure for consistent, high-priority workloads while using cloud GPUs for burst capacity. This avoids complete lock-in to cloud providers' premium GPU pricing and grants better control over total cost of ownership for expensive AI infrastructure.
Haseeb Budhani
Co-Founder and CEO, Rafay

MULTI-CLOUD

In 2025, enterprises that initially made big bets on a single cloud hyperscaler will begin to diversify by introducing secondary providers, adding competition, and unlocking capabilities their primary provider may not offer. While the major cloud players still dominate enterprise spend, there will be a noticeable shift toward multi-cloud strategies as businesses seek to complement their existing investments.
Steve Ellis
Division President, Cloud Business Unit, Amdocs

AI DRIVES MULTI-CLOUD

I expect, due to AI-focused development, we'll see engineering and IT teams increasingly using more than one public cloud solution (AWS, Azure, etc.). By adopting this type of multi-cloud approach as part of a DevOps strategy, you can train your distributed AI workloads and models across multiple environments. For instance, there could be a benefit to using Azure's computing power to train one AI model and AWS for another. Or you could keep your legacy cloud workloads on one public cloud and then your AI workloads on a separate public cloud. This approach enables development teams to tailor their cloud environment to the needs of each AI application.
Faiz Khan
CEO of Wanclouds

MULTI-CLOUD CHAOS

The Forecast Calls for Multi-cloud Chaos: By 2025, multi-cloud environments will become the "new normal," but with a twist — organizations will be navigating clouds with as much finesse as a game of Twister. A recent Gartner report found that by 2024, over 75% of midsize and large organizations will have adopted a multicloud or hybrid IT strategy, but managing these clouds will resemble herding digital cats.
Ravi Ithal
GVP and CTO, Proofpoint DSPM

ALTERNATIVE CLOUD PROVIDERS

AI - The Catalyst For The Alt-cloud: AI will become smarter and more dependable in the next year, but businesses will require agile, scalable, open, composable ecosystems to unlock its full potential – something Big Tech's cloud titans aren't capable of delivering. Enterprises will increasingly look to alternative cloud providers to supply the kind of infrastructure that supports the rapid deployment of new AI models without skyrocketing overheads. These open ecosystems will supplant the monolithic, rigid, and costly single-vendor paradigm that has disproportionately favored enterprises operating closer to the traditional tech heartlands, leveling the playing field for AI innovation across all regions of the world.
JJ Kardwell
CEO, Vultr

DISTRIBUTED CLOUD

Enterprises Rethink Cloud Choices Amid New Regulations: 
As businesses seek greater flexibility and control, 2025 will see a shift away from legacy cloud providers toward more adaptable, distributed cloud solutions. Cloud concentration risk, privacy laws and data management regulations will be the primary drivers. Distributed cloud will enable companies to move compute and data closer to users and improve performance while staying responsive to compliance needs as regulations evolve. The focus will be on finding cloud solutions that balance innovation with agility.
Ari Weil
VP of Product Marketing, Akamai

Go to: 2025 Cloud and FinOps Predictions - Part 2

Related Links

Hot Topics

The Latest

Three Strikes and They're Gone: Why Proactive Operations is Now a Customer Retention Strategy

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

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 ...

IT Leaders Must Establish a Network Data Architecture Practice

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 ...

Data Center Outages Are Decreasing but More Costly

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 ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

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 ...

TLS Certificate Expiration Is Becoming an Observability Problem

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 ...

2025 Cloud and FinOps Predictions - Part 1

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.
 

CLOUD: ESSENTIAL INFRASTRUCTURE

In 2025, the cloud will evolve into a core platform prioritizing simplicity, security, and compliance. By integrating AI-driven insights and automation, cloud environments will empower diverse teams — not just developers — to drive efficient, secure, and compliant workflows. This growth will solidify the cloud as essential infrastructure, supporting seamless digital operations and driving strategic business growth. 
Gab Menachem
VP ITOM, ServiceNow

AI DRIVES CLOUD UTILIZATION

AI will significantly increase cloud utilization in 2025. The latest advancements in AI hardware, which come at a premium, require the use of hundreds of GPUs for model training. In addition, the demand for these GPUs has led them to be sold out for the next 12 months. If organizations are looking to train their own model or operationalize it with inference, there may be no other choice than to purchase it as a service from a cloud provider. As an alternative, they can utilize existing models and managed services from the cloud providers to make the deployment and management of a model simpler. Regardless of the method, AI is cost prohibitive as a DIY venture. The power of the cloud will enable customers to use the AI services and tools at the scale and flexibility that they need. Purchasing it as a service allows more organizations to access the technology and innovate, benefiting us all.
Jason Bright
Product Marketing Manager at Hyland

AI-DRIVEN AUTOMATION

AI in the cloud moves from simply "spotting things" to actually "doing things." Beyond data crunching and spitting out insights, AI-driven automation that turns insights into actions, automatically optimizes cloud performance and spend, and reduces the insight to action gap becomes the new table stakes by the end of 2025. Agentic AI gains rapid adoption and is integrated into workflows to accelerate AI impact such that the industry begins seeing "near-realtime FinOps" for the first time. And AI begins playing a bigger role in spotting anomalies and making decisions at moments of truth at the edge as organizations continue finding ways to shift left.
Kyle Campos
Chief Technology & Product Officer at CloudBolt

HYBRID CLOUD

In 2025, we will see an even greater push toward diversified IT infrastructure. It's no longer about being all on-prem, all-cloud, or all-SaaS — companies are finding a need to balance across these platforms to drive efficiency and better manage costs. We've had enough time with these technologies to know what works where, and we're getting smarter about budgeting for them.
Chrystal Taylor
Evangelist, SolarWinds

AI DRIVES HYBRID CLOUD

Thanks to AI, Hybrid Cloud is Here to Stay: Only about two years ago, it was a very cloud-only environment with some companies ready to get rid of their data centers altogether. The reality is, many businesses still have over half their data living outside of the cloud, and it will likely stay there based on what makes the most sense for their use case (in high stakes environments such as healthcare, for example). Therefore, hybrid cloud strategies are alive and well, especially with the proliferation of AI. Organizations can maintain on-premises GPU infrastructure for consistent, high-priority workloads while using cloud GPUs for burst capacity. This avoids complete lock-in to cloud providers' premium GPU pricing and grants better control over total cost of ownership for expensive AI infrastructure.
Haseeb Budhani
Co-Founder and CEO, Rafay

MULTI-CLOUD

In 2025, enterprises that initially made big bets on a single cloud hyperscaler will begin to diversify by introducing secondary providers, adding competition, and unlocking capabilities their primary provider may not offer. While the major cloud players still dominate enterprise spend, there will be a noticeable shift toward multi-cloud strategies as businesses seek to complement their existing investments.
Steve Ellis
Division President, Cloud Business Unit, Amdocs

AI DRIVES MULTI-CLOUD

I expect, due to AI-focused development, we'll see engineering and IT teams increasingly using more than one public cloud solution (AWS, Azure, etc.). By adopting this type of multi-cloud approach as part of a DevOps strategy, you can train your distributed AI workloads and models across multiple environments. For instance, there could be a benefit to using Azure's computing power to train one AI model and AWS for another. Or you could keep your legacy cloud workloads on one public cloud and then your AI workloads on a separate public cloud. This approach enables development teams to tailor their cloud environment to the needs of each AI application.
Faiz Khan
CEO of Wanclouds

MULTI-CLOUD CHAOS

The Forecast Calls for Multi-cloud Chaos: By 2025, multi-cloud environments will become the "new normal," but with a twist — organizations will be navigating clouds with as much finesse as a game of Twister. A recent Gartner report found that by 2024, over 75% of midsize and large organizations will have adopted a multicloud or hybrid IT strategy, but managing these clouds will resemble herding digital cats.
Ravi Ithal
GVP and CTO, Proofpoint DSPM

ALTERNATIVE CLOUD PROVIDERS

AI - The Catalyst For The Alt-cloud: AI will become smarter and more dependable in the next year, but businesses will require agile, scalable, open, composable ecosystems to unlock its full potential – something Big Tech's cloud titans aren't capable of delivering. Enterprises will increasingly look to alternative cloud providers to supply the kind of infrastructure that supports the rapid deployment of new AI models without skyrocketing overheads. These open ecosystems will supplant the monolithic, rigid, and costly single-vendor paradigm that has disproportionately favored enterprises operating closer to the traditional tech heartlands, leveling the playing field for AI innovation across all regions of the world.
JJ Kardwell
CEO, Vultr

DISTRIBUTED CLOUD

Enterprises Rethink Cloud Choices Amid New Regulations: 
As businesses seek greater flexibility and control, 2025 will see a shift away from legacy cloud providers toward more adaptable, distributed cloud solutions. Cloud concentration risk, privacy laws and data management regulations will be the primary drivers. Distributed cloud will enable companies to move compute and data closer to users and improve performance while staying responsive to compliance needs as regulations evolve. The focus will be on finding cloud solutions that balance innovation with agility.
Ari Weil
VP of Product Marketing, Akamai

Go to: 2025 Cloud and FinOps Predictions - Part 2

Related Links

Hot Topics

The Latest

Three Strikes and They're Gone: Why Proactive Operations is Now a Customer Retention Strategy

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

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 ...

IT Leaders Must Establish a Network Data Architecture Practice

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 ...

Data Center Outages Are Decreasing but More Costly

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 ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

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 ...

TLS Certificate Expiration Is Becoming an Observability Problem

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 ...