The cloud stories enterprise IT leaders actually read.
Volume 343, Issue 1 · 11 articles from 2 issues
Power, not chips, may limit cloud growth in 2027, pushing providers to rethink servers, storage, networking and cooling around AI.
Analytics Insight argues that electricity may constrain cloud growth before chips do, with the IEA projecting data center power use to more than double to about 945 TWh by 2030, driven mainly by AI.
Servers are splitting between accelerators such as GPUs and custom chips for heavy AI work and Arm-based CPUs aimed at lower cost per watt, while storage and networking advances like faster flash tiers, Ultra Ethernet and co-packaged optics determine how well those chips are used.
In 2027, cloud success will depend on making power, data and compute work together as one system.
A primer on cloud architecture components and deployment models, with 2027 priorities of AI infrastructure, cost, resilience and ownership.
Analytics Insight describes cloud architecture as the way compute, storage, networking, applications, data and security combine into one system, where good design makes apps fast, secure and economical and poor design brings downtime and surprise costs.
It covers compute options from VMs and Kubernetes containers to serverless and AI chips, storage choices such as object storage and managed databases, and public, private, hybrid and multicloud models suited to different workloads and rules.
Priorities for 2027 include AI infrastructure, cost management, tested resilience and clear ownership.
AI is driving cloud demand while FinOps, sovereignty and workload placement make enterprise cloud strategy more complex in 2026.
Analytics Insight says cloud has entered a new phase in which AI drives much of the new demand while cost, security, data control and national rules shape architecture.
Gartner expects IaaS spending to reach $287.3 billion in 2026, up 29.3%, with AI-optimized infrastructure at $42.3 billion and AI inference spending of $23.3 billion ahead of $19 billion for training.
FinOps is becoming essential to control models, tokens and GPU use as enterprises balance performance, security, sovereignty, cost and workload placement.
Virginia's centralized identity foundation shows how states use identity, not the network perimeter, to secure hybrid cloud workloads.
Virginia's IT agency consolidated dozens of Active Directory instances into one, synchronized it with Microsoft Entra ID, and added Okta single sign-on, a foundation that now authenticates staff to more than 1,000 enterprise applications so agencies migrating to the cloud do not need to rebuild IAM each time.
CIO Michael Watson says single sign-on improved the user experience and reduced phishing incidents.
Forrester's Geoff Cairns explains why zero trust and hybrid cloud are pushing states to build security around identity rather than the network perimeter.
Identity is now the top cloud threat per CSA, with AI-driven attacks newly ranked; protection must pair prevention with tested recovery.
Analytics Insight reports that identity and access management ranks as the leading cloud threat in the Cloud Security Alliance's 2026 survey, while AI-enhanced attacks and AI system compromise entered its top threats for the first time.
A single misconfigured bucket or exposed key can compromise an environment, yet few organizations have one view of who or what can reach each resource across providers, and service accounts, API keys, automated workflows and AI agents often get less scrutiny than employee accounts.
That leaves gaps in ownership, access reviews and credential removal, so effective protection pairs prevention with tested recovery.
CIOs weigh neoclouds for GPU, edge and sovereign workloads, but demand proof on compliance, resilience and total cost.
CIO.com examines how enterprises are assessing alternative clouds, or neoclouds, that specialize in AI-native compute, sovereign and edge deployments, with CloudBolt's Kyle Campos saying organizations are rebalancing away from all-in hyperscaler strategies to avoid lock-in, attracted by cheaper storage, better edge performance and faster GPU access.
Smartsheet CIO and CISO Ravi Soin expands use only within a defined perimeter, noting many neoclouds fail early on compliance for regulated workloads.
NCC Group's Nigel Gibbons urges evaluating fit, form and function, and CIOs want proof on auditability, resilience and total cost.
Craig McLuckie argues AI agent harnesses must be built as distributed applications, not desktop tools wrapped in containers.
Stacklok's Craig McLuckie argues that most coding-agent harnesses assume one user, one machine and one process that is simultaneously UI, agent loop, sandbox, credential store and session database, a design that breaks down when organizations need hundreds of governed sessions that survive node failures.
Applying Kubernetes' lesson that a monolith in a container is still a monolith, he proposes separating the agent loop from clients, execution environments, tool catalogs and supporting services.
The approach enables versioned rollouts, durable session state that survives worker loss, governed tool access and multiple client types, illustrated with the open source Mecatl harness.
NTT DATA research finds cloud adoption has outpaced maturity, and that gap now decides whether enterprise AI scales.
NTT DATA's Charlie Li writes in TechRadar Pro that cloud adoption alone no longer delivers transformation as AI moves into execution, citing NTT DATA research with more than 2,300 senior decision-makers showing fewer than half of organizations are satisfied with cloud's impact on innovation or modernization.
Many embraced cloud for cost or migration speed without embedding it into operating models, governance and strategy.
Li argues cloud is now the execution layer for AI, and organizations pulling ahead invest in modernization, architecture and governance that enable business reinvention.
Physical damage to AWS Gulf regions shows multi-AZ design has limits and that DR plans must cover the full cloud supply chain.
David Linthicum uses AWS's admission that war damage in Bahrain spanned multiple availability zones and exceeded what its regional services were designed to withstand to challenge assumptions about cloud resilience, noting that with restoration updates not expected until 2027, some data may never return.
He notes many companies hit by 2025 outages relied on clouds only indirectly through SaaS providers.
He urges enterprises to map their full dependency chain down to regions and zones, and to plan explicitly for unlikely disasters in business continuity and disaster recovery rather than assuming the provider handles everything.
Six guardrails for running AI apps on Kubernetes, covering probes, scaling metrics, streams, credentials, telemetry and rollback.
This Cloud Native Now piece says Kubernetes is a strong runtime for AI but cannot judge answers, track model quotas or keep live streams alive across Pod termination.
Production also brings variable latency, costly external calls and prompt or model changes that alter behavior without a new image.
It proposes six guardrails forming a release contract across platform and app: separate probes with liveness never tied to a remote model provider, scaling on metrics other than CPU, graceful streaming, secure credentials, AI-aware telemetry and safe rollback.
Explains IaaS, PaaS and SaaS trade-offs in control and responsibility, with a focus on UAE businesses' governance and compliance needs.
Analytics Insight explains the three cloud service models as AI workloads, cloud-native apps and automation make architecture a strategic choice.
IaaS offers maximum infrastructure control but requires more technical management, PaaS removes operating system and infrastructure duties so developers can focus on building apps, and SaaS delivers complete applications.
Written for UAE companies, the piece says the real question is how much infrastructure to manage, with data governance, security, compliance, regional infrastructure and cost management mattering in hybrid and multicloud environments.