2026 Cloud Predictions - Part 2

Industry experts offer predictions on how Cloud will evolve and impact business in 2026. Part 2 covers FinOps, Sovereign Cloud and more.

COST OPTIMIZATION

Cost optimization will be focused as cloud cost is accelerated: With cloud and infrastructure costs soaring, companies will place a strong emphasis on the cost-effectiveness of observability in 2026. The economic climate is pushing enterprises to demand more value from monitoring and APM investments, favoring solutions that are efficient and budget-friendly over those packed with unnecessary features. We anticipate a shift in how observability tools are evaluated, with total cost of ownership (TCO) and predictable pricing models becoming the top criteria for decision-makers.

In practice, this means businesses will gravitate toward platforms that can handle large data volumes without runaway costs (through better data compression, smart sampling, or usage-based pricing caps). Open-source and open-standards-based stacks (like OpenTelemetry with object storage backends) are also attractive for controlling costs, as they help avoid expensive vendor lock-in. Moreover, organizations will look to optimize what data is collected and retained, aligning observability with value generation. Every telemetry data point stored should provide actionable insight, and if it doesn't, it's an opportunity to cut data storage costs.

In summary, cost optimization is becoming a first-class goal of observability strategies. Teams will seek to do more with less, maintaining full visibility into systems while keeping the monitoring budget under control in an era of accelerated cloud spending.
Sam Suthar
Founding Director, Middleware

FINOPS

As organizations continue reporting disappointing results from early AI experiments, 2026 will mark a reset where companies return to the basics. Companies are beginning to realize that the AI hype cycle without defined outcomes or financial governance is risky and successful leaders will build FinOps roadmaps that optimize their AI usage for their unique business goals and measurable ROI. In the year ahead, leaders must remember that FinOps isn't just about cutting costs: it's about bridging the gap between finance, engineering, and business teams to work around shared goals, ensuring that AI investments deliver a measurable and lasting impact without stunting innovation. Companies that treat FinOps as a key strategic step rather than an afterthought will be the ones turning their 2025 AI investments into 2026 success stories.
Jay Litkey
SVP of Cloud & FinOps, Flexera

AGENTIC FINOPS

Agentic FinOps to drive cloud cost efficiencies: Agentic FinOps represents the next evolution of Cloud Financial Operations (FinOps) by integrating autonomous AI agents into cost management workflows. FinOps will encompass LLMs and related services alongside traditional cloud offerings. The focus will shift from simply reporting and recommending to actively executing optimizations.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

SOVEREIGN CLOUD

The "For What?" Year of the Sovereign Cloud: Until now, sovereign cloud has long been treated as a necessary ideal — important, but not yet fully defined, scoped, and prioritized. Despite strong government commitments, progress has been slowed by the absence of clear regulations to drive adoption. In 2026, that will begin to change. Nations will start aligning sovereign cloud initiatives with their broader digital strategies, tying deployments to innovation goals in startups, academic research, and AI ecosystems. This will be the year sovereign cloud shifts from concept to purpose-driven implementation.
Kevin Cochrane
CMO, Vultr

GEO-ALIGNED CLOUD

Geo-Aligned Cloud Ecosystems Have Arrived: Cloud strategy will become as much about sovereignty as it is about scale. Enterprise leaders must navigate a complex web of regional data laws and compliance standards that shape where and how they deploy AI infrastructure. Already, a majority of enterprises have adapted their cloud strategies in response to geopolitical pressures. In 2026, that number is expected to climb. Localized compliance frameworks will no longer be an exception; they'll be a core KPI measured against AI and cloud implementation strategies.

In this new era, enterprises will prioritize trusted geography over pure cost efficiency, and multinational organizations will shift toward multi-cloud-by-design architectures, striking a balance between performance and resilience. This evolution has direct implications for the nearly 70% of business leaders who feel unprepared to manage external risks such as regulatory uncertainty and market volatility. The same proportion reports that their current cloud environments evolved "by accident, not by design," and nearly all (95%) say they would redesign their cloud strategies if given the opportunity. 
Dennis Perpetua
Global CTO Digital Workplace Services & Experience Officer, VP & Distinguished Engineer, Kyndryl

MOTION VS. SCALE

The next phase of cloud innovation will be defined by motion, not scale. In 2026, the most successful organizations will treat data as a living, moving entity, flowing freely between clouds and edge environments. The cloud wars won't be about who stores your data, but who can move it fastest, safest, and with the most context.
Dr. Hema Raghavan
Head of Engineering and Co-Founder, Kumo

CONVERGENCE OF CLOUD OBSERVABILITY AND SECURITY

By 2026, cloud observability and security will be inseparable disciplines. As enterprises embrace multi-cloud and cross-network architectures, performance degradation and misconfigurations will often share the same root cause: policy inconsistency. The future lies in continuous posture assurance — monitoring every configuration change against desired state and business intent. Organizations will demand unified visibility that connects cloud performance metrics with compliance posture, turning observability into a governance-grade control layer for cloud operations.
Erez Tadmor
Field CTO, Tufin

CLOUD FOR MAC

Mac will likely become a more leveraged cloud service and will expand beyond development use cases as capabilities like containerization and AI processing are exposed to customers. The cloud for Mac users will lean toward hybrid environments where edge-to-cloud architectures make the most of the capabilities of on-device processing and private cloud infrastructures. This will serve everything from remote work environments to AI inference and Linux workloads on low-cost, high-compute, low-energy resources.
Chris Chapman
CTO, MacStadium

Go to: 2026 Cloud Predictions - Part 3

Related Links

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The Latest

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 ...

Enterprise Cloud Observability: Choosing the Right Starting Point

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 ...

2026 Cloud Predictions - Part 2

Industry experts offer predictions on how Cloud will evolve and impact business in 2026. Part 2 covers FinOps, Sovereign Cloud and more.

COST OPTIMIZATION

Cost optimization will be focused as cloud cost is accelerated: With cloud and infrastructure costs soaring, companies will place a strong emphasis on the cost-effectiveness of observability in 2026. The economic climate is pushing enterprises to demand more value from monitoring and APM investments, favoring solutions that are efficient and budget-friendly over those packed with unnecessary features. We anticipate a shift in how observability tools are evaluated, with total cost of ownership (TCO) and predictable pricing models becoming the top criteria for decision-makers.

In practice, this means businesses will gravitate toward platforms that can handle large data volumes without runaway costs (through better data compression, smart sampling, or usage-based pricing caps). Open-source and open-standards-based stacks (like OpenTelemetry with object storage backends) are also attractive for controlling costs, as they help avoid expensive vendor lock-in. Moreover, organizations will look to optimize what data is collected and retained, aligning observability with value generation. Every telemetry data point stored should provide actionable insight, and if it doesn't, it's an opportunity to cut data storage costs.

In summary, cost optimization is becoming a first-class goal of observability strategies. Teams will seek to do more with less, maintaining full visibility into systems while keeping the monitoring budget under control in an era of accelerated cloud spending.
Sam Suthar
Founding Director, Middleware

FINOPS

As organizations continue reporting disappointing results from early AI experiments, 2026 will mark a reset where companies return to the basics. Companies are beginning to realize that the AI hype cycle without defined outcomes or financial governance is risky and successful leaders will build FinOps roadmaps that optimize their AI usage for their unique business goals and measurable ROI. In the year ahead, leaders must remember that FinOps isn't just about cutting costs: it's about bridging the gap between finance, engineering, and business teams to work around shared goals, ensuring that AI investments deliver a measurable and lasting impact without stunting innovation. Companies that treat FinOps as a key strategic step rather than an afterthought will be the ones turning their 2025 AI investments into 2026 success stories.
Jay Litkey
SVP of Cloud & FinOps, Flexera

AGENTIC FINOPS

Agentic FinOps to drive cloud cost efficiencies: Agentic FinOps represents the next evolution of Cloud Financial Operations (FinOps) by integrating autonomous AI agents into cost management workflows. FinOps will encompass LLMs and related services alongside traditional cloud offerings. The focus will shift from simply reporting and recommending to actively executing optimizations.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

SOVEREIGN CLOUD

The "For What?" Year of the Sovereign Cloud: Until now, sovereign cloud has long been treated as a necessary ideal — important, but not yet fully defined, scoped, and prioritized. Despite strong government commitments, progress has been slowed by the absence of clear regulations to drive adoption. In 2026, that will begin to change. Nations will start aligning sovereign cloud initiatives with their broader digital strategies, tying deployments to innovation goals in startups, academic research, and AI ecosystems. This will be the year sovereign cloud shifts from concept to purpose-driven implementation.
Kevin Cochrane
CMO, Vultr

GEO-ALIGNED CLOUD

Geo-Aligned Cloud Ecosystems Have Arrived: Cloud strategy will become as much about sovereignty as it is about scale. Enterprise leaders must navigate a complex web of regional data laws and compliance standards that shape where and how they deploy AI infrastructure. Already, a majority of enterprises have adapted their cloud strategies in response to geopolitical pressures. In 2026, that number is expected to climb. Localized compliance frameworks will no longer be an exception; they'll be a core KPI measured against AI and cloud implementation strategies.

In this new era, enterprises will prioritize trusted geography over pure cost efficiency, and multinational organizations will shift toward multi-cloud-by-design architectures, striking a balance between performance and resilience. This evolution has direct implications for the nearly 70% of business leaders who feel unprepared to manage external risks such as regulatory uncertainty and market volatility. The same proportion reports that their current cloud environments evolved "by accident, not by design," and nearly all (95%) say they would redesign their cloud strategies if given the opportunity. 
Dennis Perpetua
Global CTO Digital Workplace Services & Experience Officer, VP & Distinguished Engineer, Kyndryl

MOTION VS. SCALE

The next phase of cloud innovation will be defined by motion, not scale. In 2026, the most successful organizations will treat data as a living, moving entity, flowing freely between clouds and edge environments. The cloud wars won't be about who stores your data, but who can move it fastest, safest, and with the most context.
Dr. Hema Raghavan
Head of Engineering and Co-Founder, Kumo

CONVERGENCE OF CLOUD OBSERVABILITY AND SECURITY

By 2026, cloud observability and security will be inseparable disciplines. As enterprises embrace multi-cloud and cross-network architectures, performance degradation and misconfigurations will often share the same root cause: policy inconsistency. The future lies in continuous posture assurance — monitoring every configuration change against desired state and business intent. Organizations will demand unified visibility that connects cloud performance metrics with compliance posture, turning observability into a governance-grade control layer for cloud operations.
Erez Tadmor
Field CTO, Tufin

CLOUD FOR MAC

Mac will likely become a more leveraged cloud service and will expand beyond development use cases as capabilities like containerization and AI processing are exposed to customers. The cloud for Mac users will lean toward hybrid environments where edge-to-cloud architectures make the most of the capabilities of on-device processing and private cloud infrastructures. This will serve everything from remote work environments to AI inference and Linux workloads on low-cost, high-compute, low-energy resources.
Chris Chapman
CTO, MacStadium

Go to: 2026 Cloud Predictions - Part 3

Related Links

Hot Topics

The Latest

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 ...

Enterprise Cloud Observability: Choosing the Right Starting Point

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 ...