Cloud-Based Computing Trends: Navigating the Future of IT Infrastructure

Introduction

Cloud computing is no longer a novelty; it’s the backbone of modern digital transformation. From streaming video to powering artificial‑intelligence (AI) assistants, cloud platforms deliver scalable, on‑demand computing power that would have been unimaginable a decade ago. As we enter 2025, this landscape is evolving faster than ever. Generative AI models are shifting workloads, new regulations are raising the bar for security and data sovereignty, and sustainability concerns are forcing providers to rethink power‑hungry data centers. This article navigates the most consequential cloud‑based computing trends shaping the future of IT infrastructure, backed by current research and real‑world examples. You’ll learn how market forces, emerging technologies and strategic practices like FinOps and DevEdgeOps are redefining what it means to run workloads in the cloud.

Modern office with a laptop on a glass desk showing a blue cloud dashboard and glowing upload arrows; hands typing with a phone, a small edge device, and a plant nearby, and a clear city skyline through the window with faint cloud and network icons.

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Understanding cloud‑based computing & market growth

What is cloud‑based computing?

At its core, cloud computing refers to the delivery of computing resources—such as servers, storage, databases, networking, software and analytics—over the internet (“the cloud”). Instead of owning and maintaining physical hardware, organizations rent resources from providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform or regional carriers. This shift enables pay‑as‑you‑go pricing, rapid scalability and global accessibility. Cloud services are generally categorized into infrastructure‑as‑a‑service (IaaS), platform‑as‑a‑service (PaaS) and software‑as‑a‑service (SaaS). More recently, industry cloud platforms have emerged, bundling IaaS, PaaS and SaaS with composable capabilities tailored to specific sectors. Gartner predicts that by 2027 more than 70 % of enterprises will rely on industry cloud platforms, up from less than 15 % in 2023spacelift.io.

Market growth and adoption statistics

The global appetite for cloud continues to surge. According to Gartner’s October 2024 forecast, worldwide IT spending is projected to reach US $5.74 trillion in 2025, up 9.3 % from 2024networkworld.com. The same forecast notes that spending on software will rise 14 % to US $1.23 trillion, while IT services will grow 9.4 % to US $1.73 trillionnetworkworld.com. Much of this investment is directed at cloud and AI projects. A market snapshot published in August 2025 estimates that end‑user spending on public cloud services reached US $723.4 billion in 2025, up from US $595.7 billion in 2024, representing 21.5 % year‑over‑year growthpe. Mordor Intelligence forecasts that the cloud computing market will grow to US $2.26 trillion by 2030, implying a 21 % compound annual growth rate (CAGR).

Adoption is nearly ubiquitous: research compiled by Spacelift shows that 96 % of companies use at least one public cloud, 84 % use private clouds, and 92 % of organizations operate in multicloud environmentsspacelift.io. Enterprises increasingly spread workloads across multiple providers to improve resilience and avoid vendor lock‑in. The same report notes that 60 % of business data is stored in the cloud and 200 zettabytes of data (a trillion gigabytes) will exist globally by 2025. In terms of market share, AWS holds about 32 %, Microsoft Azure 23 %, Google Cloud 11 % and Alibaba Cloud 4 %spacelift.io.

Key cloud trends for 2025

Cloud is not a monolithic technology. Several intertwined trends are reshaping how infrastructure is designed, deployed and managed.

AI‑driven cloud and intelligent edge

AI is no longer just another service running in the cloud—it’s becoming the operating fabric of cloud platforms. The Cloud Native Computing Foundation (CNCF) notes that AI will optimize every aspect of cloud operations, from real‑time resource allocation and automated scaling to intelligent threat detectioncncf.io. Cloud providers such as Google, Microsoft and AWS embed machine‑learning services into their platforms to automate routine tasks and deliver predictive analyticsallcovered.com. Organizations that embrace this paradigm enjoy greater efficiency, cost reductions and performance gains.

Edge computing brings processing closer to the data source, reducing latency for applications like autonomous vehicles, industrial automation and augmented reality. The convergence of AI and edge computing—sometimes called edge‑to‑cloud AI integration—allows AI models to be trained in the cloud and then executed at the edgecncf.io. Analysts expect real‑time edge workloads to proliferate in 2025, with AI workloads dynamically shifting between cloud and edge for optimum performanceallcovered.com.

Step‑by‑step: Deploying AI at the edge

Identify latency‑sensitive workloads. Examples include predictive maintenance, machine‑vision inspection or real‑time personalization.
Train models in the cloud. Use scalable GPUs and frameworks to train and fine‑tune models.
Deploy models to edge devices. Optimize models for low‑power processors and deploy via container orchestration platforms like Kubernetes.
Implement orchestration and monitoring. Use AI‑driven tools to shift workloads between edge and cloud based on network conditions and compute availability.
Secure data flows. Encrypt data at rest and in transit and enforce strict identity and access management across distributed devices.

Hybrid and multi‑cloud strategies

Enterprises are moving away from single‑vendor strategies. All Covered’s 2025 trend report notes that 76 % of businesses migrating to the cloud use hybrid or multi‑cloud architecturesallcovered.com. Spacelift’s statistics reveal that 92 % of organizations use a multicloud approach and nearly 80 % incorporate multiple public cloudsspacelift.iospacelift.io. Hybrid models combine on‑premises infrastructure with public clouds, allowing organizations to keep sensitive data in private environments while leveraging scalable services elsewhere. Multi‑cloud strategies involve using multiple public cloud providers for different workloads to boost resilience and negotiate better pricing.

The benefits are clear: increased flexibility, improved resilience and avoidance of vendor lock‑inallcovered.com. However, hybrid/multi‑cloud deployments also introduce challenges such as integration overhead, data consistency, skills gaps and complex billing. To succeed, organizations must adopt strong cloud governance, automate workload placement and enforce unified identity and access management.

Serverless computing and low‑code/no‑code development

Serverless platforms like AWS Lambda, Google Cloud Functions and Azure Functions abstract away server management, enabling developers to deploy code without worrying about infrastructure. Datadog’s 2024 serverless adoption report (cited by All Covered) found that more than 70 % of AWS users rely on at least one serverless service, and adoption is rising across all major providersallcovered.com. Because billing is based on actual execution time, serverless often reduces costs for irregular workloads and accelerates time‑to‑market. Low‑code/no‑code platforms extend this by letting “citizen developers” build applications through drag‑and‑drop interfaces and visual workflowsallcovered.com. Organizations can thus empower non‑developers to create internal tools and prototypes rapidly.

Step‑by‑step: Embracing serverless and low‑code

Assess workload suitability. Ideal candidates include event‑driven functions, APIs and occasional batch jobs.
Refactor monoliths into microservices. Break large applications into small functions or services that can be deployed independently.
Adopt DevOps automation. Use continuous integration/continuous delivery (CI/CD) pipelines and infrastructure‑as‑code to manage deployments.
Monitor and optimize. Track cold starts, execution times and concurrency limits. Optimize functions and memory allocation to reduce costs.
Leverage low‑code platforms for simple apps. Provide training and governance to ensure that citizen‑developed apps meet security and compliance requirements.

Sustainable cloud practices and green data centers

Cloud adoption’s rapid growth brings environmental challenges. Deloitte forecasts that data centers will consume about 536 terawatt‑hours (TWh) of electricity in 2025—roughly 2 % of global power consumptionblog.se.com. The International Energy Agency (IEA) warns that as generative AI workloads grow, data‑center electricity use could double to around 1,000 TWh by 2026blog.se.com. IDC predicts the world’s data volume will expand to 175 zettabytes by 2025blog.se.com, further increasing energy demands. To mitigate these impacts, cloud providers are investing in renewable energy, energy‑efficient hardware and innovative cooling technologies. For example, Schneider Electric highlights immersion cooling systems that can cut data‑center construction costs by up to 50 % while reducing energy useblog.se.com.

Sustainability efforts also drive cloud purchasers to favor providers with strong carbon‑reduction commitments. Buyers increasingly ask for transparency around water usage effectiveness and carbon footprints. To support sustainability, organizations should:

Optimize workloads. Shut down unused resources and right‑size instances through FinOps practices.

Choose green regions. Deploy workloads in data centers powered by renewable energy.

Implement advanced cooling. Consider liquid and immersion cooling solutions to reduce HVAC loadsblog.se.com.

Track emissions. Use dashboards that measure energy use and carbon emissions per workload.

Cloud security and innovative protections

Security remains one of the top concerns for cloud decision‑makers. All Covered lists the leading threats as misconfiguration, unauthorized access and insecure interfaces. Spacelift’s research echoes this, noting that misconfiguration leads to 68 % of security issues, followed by unauthorized access (58 %) and insecure APIs (52 %)spacelift.io. The evolving compliance landscape—with frameworks like Europe’s Network and Information Systems Directive 2 (NIS2) and the Digital Operational Resilience Act (DORA)—adds complexityallcovered.com.

To counter these threats, providers and enterprises are turning to AI‑powered security solutions capable of analyzing massive telemetry streams and detecting anomalies in real time. Emerging technologies such as confidential computing isolate sensitive workloads in hardware‑based trusted execution environments so that data remains encrypted even during processingallcovered.com. Best practices for securing cloud workloads include:

Zero‑trust architecture. Assume no implicit trust within or between networks; enforce authentication and least‑privilege access across users and workloads.
Automated policy enforcement. Use tools to continuously scan infrastructure for misconfigurations and apply fixes automatically.
Encryption everywhere. Encrypt data at rest, in transit and in use (via confidential computing). Manage keys through secure key‑management services.
Continuous monitoring. Employ AI‑driven threat detection and incident response to stop attacks before they spread.allcovered.com
Regulatory compliance. Stay abreast of evolving regulations and implement governance frameworks that map controls to standards like ISO 27001, SOC 2 and regional laws.

Quantum computing as a service

Quantum computing is inching from labs into mainstream through cloud‑based delivery models. The CNCF notes that industry giants IBM, Google, Microsoft and Amazon are democratizing access to quantum computing, allowing organizations to experiment without buying costly hardwarecncf.io. Quantum services can accelerate drug discovery, materials science and cryptography. In 2025, expect more “quantum‑as‑a‑service” offerings integrated into cloud platforms. Although practical applications remain limited today, early adopters should begin experimenting to understand algorithms and potential use cases.

DevEdgeOps, FinOps and operational excellence

Traditional DevOps practices designed for centralized cloud environments need adaptation for edge computing. The CNCF introduces DevEdgeOps, which combines the automation and agility of DevOps with the unique requirements of edge environmentscncf.io. This approach addresses challenges like intermittent connectivity, diverse device hardware and physical security. It emphasizes containerization, immutable infrastructure and automated updates across distributed nodes.

Meanwhile, FinOps has emerged as a discipline for managing cloud costs. As consumption‑based pricing and multi‑cloud complexity grow, organizations risk cloud waste. Effective FinOps involves:

Visibility. Collect and analyze cost and usage data across providers.

Optimization. Identify underutilized resources, schedule shutdowns and choose reserved or spot instances.

Governance. Set budgets, enforce spending policies and allocate costs to teams or projects.

Collaboration. Align finance, operations and engineering teams to balance cost, performance and innovation.

Industry cloud platforms & verticalization

Industry cloud platforms package core cloud services with pre‑built modules for specific verticals—healthcare, manufacturing, finance, retail and more. Gartner predicts that adoption of these platforms will skyrocket, with 70 % of enterprises using them by 2027spacelift.io. Generative AI accelerates the trend because industry models require domain‑specific data and compliance frameworks. For example, retail clouds may offer integrated order‑fulfillment, inventory management and personalization; healthcare clouds may include HIPAA‑compliant electronic health record storage and AI‑powered diagnostic tools. Enterprises should evaluate industry platforms that align with their regulatory and operational requirements.

Real‑world examples of cloud adoption

Multi‑cloud case studies

The theoretical benefits of multi‑cloud strategies are validated by real enterprises:

Goldman Sachs – performance‑based workload allocation. The investment bank uses a primary/secondary cloud model: trading systems run on AWS to leverage its mature ecosystem, while AI/ML workloads run on Google Cloud to benefit from advanced training and analytics capabilities. This arrangement improves analytics and modeling speeds by 40 %fluence.network. Key practices include containerization with Kubernetes for portability, strict data access policies and dynamic performance monitoring.

Walmart – hybrid multi‑cloud for retail. The retail giant operates with a hybrid model combining on‑premises systems and public clouds. This approach supports both physical stores and e‑commerce platforms. Walmart uses unified service‑mesh technology to provide consistent communication across environments, processes data locally at stores to reduce latency and employs failover automation to ensure durability.

BMW Group – governance‑centric multi‑cloud. BMW runs its manufacturing and connected‑vehicle services across Azure and AWS, governed by a centralized platform that manages cost, security and compliance. Automated policies, centralized monitoring and flexible workload placement strengthen vendor negotiations and long‑term cost controlfluence.network.

General Electric (GE) – industrial IoT across multiple clouds. GE distributes analytics, control and storage workloads across cloud providers. Edge processing handles data locally before sending it to the cloud; redundancy across providers reduces data‑loss risk; and region‑specific deployments ensure compliancefluence.network.

JPMorgan Chase – compliant multi‑cloud for financial operations. The bank adopts a hybrid model where critical workloads remain on private systems while others leverage public clouds. AI‑driven tools detect anomalies and remediate incidents in real time. Continuous risk assessments guide changes in cloud usage to satisfy stringent regulatory requirementsfluence.network.

These case studies underscore that there’s no one‑size‑fits‑all approach. Effective multi‑cloud architectures tailor workload placement to business needs, implement robust governance and automation, and maintain flexibility for vendor negotiations.

Additional examples from the industry

While the Fluence case studies provide detailed insights, many other enterprises are embracing cloud and AI. For instance, manufacturers use edge‑to‑cloud AI to monitor equipment health, enabling predictive maintenance and reducing downtime. Healthcare providers adopt industry cloud platforms to store electronic health records securely while running AI diagnostics. Media companies leverage serverless functions to handle spikes in streaming demand, ensuring a smooth user experience. These use cases illustrate the breadth of cloud innovation across sectors.

Navigating challenges and best practices

Cost management and FinOps

Uncontrolled cloud spending—often called “cloud sprawl”—is a major obstacle. Market data shows that 82 % of cloud decision‑makers cite managing cloud spend as their top challengespacelift.io. To gain control, implement FinOps practices:

Centralize visibility. Use cost‑management tools to aggregate spending across accounts and providers.
Establish budgets and alerts. Set thresholds for teams and trigger alerts when usage spikes.
Optimize resources. Choose right‑sized instances, autoscaling groups and reserved capacity where appropriate. Turn off idle resources.
Encourage accountability. Assign costs to projects or departments. This promotes responsible usage and fosters a culture of cost awareness.

Skills gaps and cultural shifts

Operating in a hybrid/multi‑cloud and AI‑driven world demands new skills. Teams need expertise in container orchestration, security automation, FinOps and data analytics. Upskilling is critical: invest in training for DevOps, DevEdgeOps, cybersecurity, quantum algorithms and AI ethics. Cultivate cross‑functional collaboration between developers, operations, finance and compliance teams. Encourage experimentation, but pair it with sound governance.

Security & compliance

In multi‑cloud environments, misconfiguration remains the leading cause of breachesallcovered.com. To address this:

Automate configuration management using infrastructure‑as‑code and policy‑as‑code.

Implement zero‑trust security models and multi‑factor authentication across all accounts.

Continuously monitor logs, metrics and network flows. Use AI to detect anomaliesallcovered.com.

Stay current with regulations, such as GDPR, HIPAA, NIS2 and DORA, and maintain documentation for audits.

Vendor lock‑in and portability

While providers offer native services that accelerate development, over‑reliance can create lock‑in. Mitigate this by adopting open standards (e.g., Kubernetes, OpenTelemetry), containerized microservices and cloud‑agnostic tools. Consider deploying critical workloads across multiple regions or providers to ensure resilience. Balance the convenience of managed services with long‑term flexibility.

Future outlook and predictions

Looking beyond 2025, several forces will shape the cloud landscape:

Public cloud spending will continue to soar. Forecasts suggest that cloud spending could exceed US $1 trillion by 2027. Gartner anticipates overall IT spending to approach US $7 trillion by 2028, with data‑center systems growing 15.5 % annually and server sales tripling by 2028networkworld.com. Cloud will become a required component for competitivenessspacelift.io.

Data explosion and edge proliferation. The global data sphere may reach 200 zettabytes by 2025, and half of all data is projected to be stored in the cloudpacelift.io. Edge computing adoption will accelerate as 5G and IoT networks grow, enabling real‑time AI for manufacturing, logistics and smart cities.

Generative AI everywhere. Generative AI will continue to drive investment: the Gartner IT spending forecast emphasizes that spending growth in software and IT services is largely due to AI initiativesnetworkworld.com. Businesses will integrate AI assistants, coding copilots and generative models into workflows, demanding scalable cloud infrastructure.

Sustainability as a differentiator. Data‑center energy consumption could double by 2026blog.se.com, prompting stricter regulations and investor scrutiny. Providers will compete on carbon‑neutral operations, and organizations will factor sustainability into vendor selection.

Quantum and industry clouds. Quantum‑as‑a‑service will mature, offering specialized computing for research and security. Industry cloud platforms will proliferate, enabling faster deployment of vertical‑specific applicationsspacelift.io.

Decentralized and DePIN clouds. Emerging models such as decentralized physical infrastructure networks (DePIN), which offer compute power outside centralized data centers, could reduce costs and improve resiliencefluence.network. While still nascent, they highlight the industry’s search for alternatives beyond hyperscalers.

Conclusion

Cloud‑based computing sits at the heart of modern IT strategy. In 2025, the convergence of AI, edge computing, hybrid/multi‑cloud architectures, serverless development, sustainability and innovative security is redefining how organizations build and operate IT infrastructure. Adoption is almost universal—nearly all enterprises use public clouds, and multicloud strategies are the normspacelift.io. Market growth remains robust, with public cloud spending surging over US $700 billion and IT spending predicted to reach US $5.74 trillionnetworkworld.com. At the same time, challenges around cost control, vendor lock‑in, skills gaps and sustainability require disciplined strategies. By embracing FinOps, DevEdgeOps, zero‑trust security and sustainable practices, enterprises can harness the full potential of the cloud while minimizing risks.

As technology continues to evolve, staying informed about emerging trends—quantum computing, industry clouds and decentralized infrastructure—will help businesses remain competitive. The future of IT is cloud‑driven, intelligent and green; those who prepare today will thrive tomorrow.

Frequently asked questions (FAQ)

1. What is the difference between hybrid and multi‑cloud?
Hybrid cloud combines on‑premises infrastructure with one or more public clouds, allowing data and workloads to move between private and public environments. Multi‑cloud refers to the use of multiple public cloud providers (e.g., AWS, Azure, Google Cloud) for different workloads. Many organizations implement both strategies simultaneouslyallcovered.com.

2. Why is AI important for cloud computing?
AI optimizes resource allocation, automates scaling, predicts failures and enhances security. Providers integrate AI and machine‑learning services into their platformscncf.ioallcovered.com, enabling organizations to improve efficiency and deliver intelligent applications.

3. How can I reduce my cloud costs?
Adopt FinOps practices: gain visibility into spending, right‑size resources, set budgets, use reserved or spot instances and assign costs to teams or projects. Continuous monitoring and automation can eliminate wastespacelift.io.

4. What are the biggest security threats in the cloud?
Misconfiguration, unauthorized access and insecure APIs lead the listallcovered.com. To mitigate them, implement zero‑trust architecture, automate configuration management, encrypt data and use AI‑powered threat detectionallcovered.com.

5. Will quantum computing replace classical cloud computing?
Not in the near term. Quantum computing offers exponential speedups for certain problems—cryptography, optimization and simulation—but current quantum hardware is limited. Cloud‑based quantum services allow experimentation, but classical computing will remain dominant for most workloadscncf.io.

6. How does sustainability affect cloud adoption?
Data centers consume significant energy—about 2 % of global electricity in 2025blog.se.com—and demand is rising due to AI workloadsblog.se.com. Organizations are increasingly factoring carbon footprints into vendor selection and adopting energy‑efficient hardware, cooling systems and workload optimization.

7. What is DevEdgeOps?
DevEdgeOps adapts DevOps principles to edge computing. It combines automation, containerization and continuous delivery with the specific needs of distributed edge devices—handling intermittent connectivity, diverse hardware and physical securitycncf.io.

Internal links

To dive deeper into related topics, explore the following articles on FrediTech:

Latest Tech Trends Analysis: 2025’s Most Impactful Technologies – covers AI, 5G, edge computing and more.
Beginner’s Guide to AI – a concise introduction to artificial‑intelligence concepts for non‑technical readers.
Cybersecurity – explore articles on protecting your digital assets, including device security best practices.