Building Your Cloud Computing Stack: A Guide

A cloud computing stack is the collection of infrastructure technologies that work together to run modern applications: compute, networking, storage, container orchestration, observability, and security. Together, they form the operational foundation for modern cloud infrastructure. According to the CNCF's 2025 Annual Cloud Native Survey, 98% of organizations have now adopted cloud native techniques in some capacity, and Kubernetes runs in production across 82% of those using containers (up from 66% just two years earlier). For most organizations, the question is no longer whether to adopt a cloud-native stack, but how to design one that fits their infrastructure, workloads, and growth plans.

Those situations vary considerably. Cloud providers and neoclouds need software infrastructure that can schedule GPU compute and sell it at scale. Enterprises often need to run virtual machines and containers side by side while modernizing gradually. Teams running legacy VM workloads need a path to cloud native modernization that does not require a big-bang migration. Organizations deploying AI need infrastructure built for continuous inference serving, not just periodic training runs. This guide examines all four contexts and the architectural patterns that consistently emerge across them.

Key highlights:

• A cloud computing stack is the integrated set of infrastructure layers (compute, networking, storage, orchestration, observability, and security) that together enable applications to run, scale, and operate reliably in cloud environments.

• Kubernetes has become the de facto orchestration layer for modern cloud stacks, running in production across 82% of container-using organizations as of 2025, according to the CNCF Annual Cloud Native Survey.

• Effective cloud stack design spans four distinct contexts: cloud providers building compute services, enterprises converging VM and container workloads, teams modernizing legacy infrastructure, and organizations running AI at scale.

• Mirantis provides a Kubernetes-native cloud infrastructure platform through MKE and k0rdent that addresses all four contexts: unified VM and container orchestration, multi-cloud management, and GPU-ready AI infrastructure.

What Is a Cloud Technology Stack?

A cloud technology stack is the integrated set of infrastructure layers (compute, networking, storage, container orchestration, observability, and security) that work together to run, scale, and operate applications in cloud environments.

The term covers a range of practical situations. For cloud providers and neoclouds, a cloud stack is the software substrate that enables selling compute capacity to customers at scale. For enterprises, a cloud stack must unify the management of virtual machines and containers that have accumulated across multiple environments over time. For infrastructure and platform teams, a cloud stack is the foundation on which modernization happens gradually, without forcing wholesale re-architecture of workloads that are still producing business value. For organizations running AI, the same cloud stack must support GPU scheduling, inference serving, and continuous model deployment.

The CNCF 2025 Annual Cloud Native Survey confirms that a cloud computing stack built on Kubernetes is now the operational baseline, not a forward-looking choice. 98% of organizations have adopted cloud native techniques, and Kubernetes production usage has reached 82% among container users. Understanding how these layers work together, and how to choose and configure them for your specific context, is what this guide addresses.

Cloud Computing Stack vs. Traditional Infrastructure

Traditional infrastructure relies on fixed-capacity servers provisioned manually, managed by siloed teams, and upgraded on slow refresh cycles. A cloud infrastructure stack replaces that model with dynamic, software-defined resource allocation that can scale up or down in response to actual demand. The difference compounds over time.

The shift from traditional to cloud infrastructure is not simply a technology change. It requires rethinking how capacity is allocated, how teams are organized, and how management responsibilities are divided. The six-step build process in the next section provides the structured approach that makes that transition tractable.

What Components Are Needed for a Cloud Stack?

A cloud stack needs six core components: compute to run workloads, networking to route traffic between services, storage for stateful data, container orchestration (typically Kubernetes) to schedule and manage workloads, observability tooling to surface performance data, and security controls to enforce access and policy across the environment.

Modern cloud stacks are composed of layers that each serve a distinct, interdependent function. When these layers are well-integrated, the result is infrastructure that is operationally consistent, scalable, and secure. When they are disconnected (managed by separate teams using separate tooling), the result is fragmented operations, duplicated effort, and rising cost.

The CNCF 2024 Annual Cloud Native Survey put containers in production at 91% of organizations, up from 80% the year before, a 14% year-over-year growth rate. At that level of adoption, containers are a standard infrastructure expectation, not an advanced capability. Building a modern cloud environment today means treating containers and their orchestration layer as first-class citizens from the start.

Understanding the components is only the first step. The harder challenge is assembling them into an architecture that stays consistent and scalable as applications and infrastructure grow.

How to Build a Scalable Cloud Infrastructure

Scalable cloud infrastructure does not emerge from deploying components in isolation. It requires a structured approach to standardization, orchestration, and automation, with each layer enabling the next. The operational stakes are real: Spectro Cloud's 2024 enterprise Kubernetes report found that organizations now manage an average of 20 or more Kubernetes clusters across four or more environments, and three-quarters report that infrastructure complexity has hindered adoption. At that scale, ad hoc build decisions compound into significant operational debt.

1. Standardize Infrastructure with Containers

Containers provide the unit of standardization that makes infrastructure portable and reproducible across environments. Packaging application code with its dependencies into a container image eliminates environment-specific deployment failures and produces a consistent artifact that can run wherever the orchestration layer supports it.

Standardizing on containers also makes the rest of the build process tractable:

• Define base images for common runtime environments and enforce their use through image policy at build time. Inconsistent base images are one of the most common causes of environment-parity failures: code passes tests locally because it runs against a different OS or library version than the one in production.

• Store container images in a private registry with vulnerability scanning at push time, not just at deploy time. Catching vulnerable dependencies at push means remediation happens before the image reaches staging or production, where fixing it is significantly more disruptive.

• Use immutable image tags in production to ensure deployed versions are auditable and reproducible across environments. This makes rollback predictable during incidents, because every deployment references a known artifact rather than a floating tag that may have been overwritten since it was last tested.

• Separate configuration from the container image using ConfigMaps and Secrets at the orchestration layer, not baked into the image. Baking configuration into images forces a full image rebuild for every environment change and prevents secrets from being rotated without a complete redeploy.

2. Implement Kubernetes Orchestration

At the center of most modern cloud stacks sits Kubernetes, providing the control plane that schedules containers onto available nodes, manages their lifecycle, and enforces the declared state of the cluster. Choosing a distribution, configuring the control plane for high availability, and establishing namespace and RBAC structures before workloads are onboarded are the first decisions implementation teams face.

The critical design decisions at this stage deserve individual attention. Multi-cluster topology determines whether clusters separate by environment, region, workload type, or tenant. Node pool configuration governs what machine types, taints, and labels control workload placement. Admission webhook design defines what policy runs at deploy time, and ingress configuration determines how traffic enters the cluster and how services communicate internally.

Getting these decisions right before workloads arrive is significantly less expensive than retrofitting them into a running production environment.

3. Automate Infrastructure Provisioning

Manual infrastructure provisioning does not scale. Infrastructure as code tooling (Terraform, Pulumi, Crossplane) combined with GitOps workflows ensures that cluster state is version-controlled, auditable, and consistently reproducible across environments. CI/CD use in production for most or all applications grew 31% year-over-year according to CNCF 2024 data, rising from 46% of organizations in 2023 to 60% in 2024. Automation is now the majority practice.

Automation at the provisioning layer enables:

• Consistent cluster configuration across development, staging, and production without manual synchronization.

• Rapid recovery from node or cluster failures without manual intervention from on-call engineers.

• Audit trails for all infrastructure changes through version control history, satisfying compliance requirements.

• Self-service cluster provisioning for platform teams without creating a bottleneck on a central operations function.

4. Build for Observability and Monitoring

Observability is the feedback loop that keeps infrastructure reliable as scale increases. The CNCF ecosystem provides mature tooling at every layer: Prometheus for metrics, Fluentd or OpenTelemetry for log aggregation, Jaeger for distributed tracing, and Grafana for dashboards. By 2024, 29% of organizations were releasing code multiple times per day, up from 23% the year before — a cadence that makes real-time observability non-negotiable.

Effective observability requires instrumentation at every layer of the stack simultaneously. Application metrics, control plane health, node-level resource utilization, and network latency must all surface in a unified view. Alert quality (precision and meaningful severity tiers) matters more than alert quantity: alert fatigue is one of the leading causes of missed incidents at scale.

5. Design for High Availability

In most production environments, high availability is a design constraint, not a feature that can be added after the fact. It requires redundancy at every layer: control plane nodes distributed across failure domains, worker node pools spanning availability zones, and persistent storage with replication and automated failover.

For stateful workloads, several practices matter beyond infrastructure redundancy:

• Use pod disruption budgets to ensure rolling updates never take too many replicas offline simultaneously.

• Configure readiness and liveness probes carefully. An incorrect probe definition causes more downtime than it prevents.

• Spread replicas across failure domains by configuring pod topology spread constraints or pod anti-affinity rules; the default scheduler packs pods onto the fewest nodes, which defeats the purpose of replicated deployments.

• Test failover regularly in non-production environments before relying on it under production conditions. Most failover failures discovered in production were never tested: runbooks and automation that have not been exercised under realistic load may not behave as designed when an actual incident occurs.

6. Optimize Resource Utilization

Unoptimized resource allocation is one of the leading drivers of cloud cost overrun. The mechanisms for efficient resource management are already in Kubernetes (resource requests and limits, Horizontal Pod Autoscaler, Vertical Pod Autoscaler, cluster autoscaler); their default configurations rarely reflect real workload behavior, and the gap between allocated and actually-used resources translates directly into wasted spend.

Resource optimization is an iterative process. Start with resource requests based on observed usage rather than estimates. Use namespace-level ResourceQuotas to prevent runaway workloads from consuming capacity reserved for other teams. Configure cluster autoscaler to scale node pools down during off-peak periods, not just up during demand spikes. Add cost attribution tooling (OpenCost, Kubecost) so spending stays visible at the team or product level and can be actively managed by the teams generating it.

With the infrastructure built and observable, the next question is whether the underlying architecture decisions will hold up as the platform scales.

Why Cloud Stack Architecture Matters for Scalability

Cloud architecture decisions made when building a cloud stack compound over time. Well-designed enterprise cloud infrastructure becomes more capable and cost-efficient as scale increases. Poorly designed infrastructure becomes more fragile and expensive to operate. According to the Portworx by Pure Storage 2024 Voice of Kubernetes Experts Report, 79% of organizations planning to migrate VM workloads to Kubernetes cite operational simplicity and cost reduction as their primary drivers, a clear signal that the status quo of managing separate infrastructure regimes carries a recognized, ongoing cost.

• Improves Application Performance and Uptime. Kubernetes-native infrastructure eliminates the manual intervention required to restart failed containers, rebalance load, or recover from node failures. 65% of Kubernetes users in the Portworx 2024 report cite faster deployments as the top benefit they have realized, with 54% citing reduced infrastructure costs.

• Supports Scaling Across K8s Workloads. Kubernetes autoscaling operates at multiple levels simultaneously. Pods scale horizontally in response to load, the Vertical Pod Autoscaler adjusts resource requests to match observed consumption, and cluster autoscaler adjusts the node pool itself. Each level operates independently, enabling fine-grained control without manual intervention.

• Reduces Infrastructure Complexity through Automation. Managing VMs and containers on separate platforms creates duplicate operational regimes and inconsistent policy enforcement across the estate. Unified orchestration eliminates that overhead and applies consistent automation across both workload types.

• Helps Control Cloud Spending and Resource Allocation. Kubernetes resource quotas, namespace budgets, and autoscaling work together to prevent the idle-resource waste that drives cloud cost overrun. Cost attribution tooling makes spending visible at the team level so it can be actively managed.

• Enables Hybrid and Multi-Cloud Management Flexibility. 55% of IT professionals already feel locked into their current vendor choices, and 82% of senior decision-makers expect significant market consolidation ahead, per the Spectro Cloud 2024 report. A Kubernetes platform built on open standards lets workloads move between providers and protects against forced vendor transitions.

• Strengthens Security, AI Governance and Operational Visibility. Kubernetes-native security tooling (OPA, Kyverno, Falco, Cert Manager) enables policy as code: security controls defined in version control and enforced automatically at deploy time, not audited reactively.

What Is Included in a Kubernetes-Based Cloud Stack?

A Kubernetes-based cloud stack includes container orchestration, cluster networking, persistent storage via CSI drivers, a unified observability layer (Prometheus, Grafana, OpenTelemetry), and policy-as-code security enforcement. Networking, storage, observability, and security all become Kubernetes-managed resources, configured declaratively and enforced consistently across environments. Increasingly, this is the defining property of cloud-native infrastructure: a Kubernetes service functions as more than a container scheduler. It becomes the control plane for the entire environment.

The Portworx 2024 Voice of Kubernetes Experts Report found that 86% of organizations deploy their cloud native platforms in hybrid cloud environments. Managing that hybrid footprint consistently requires the same orchestration layer to operate across on-premises and public cloud infrastructure. The same report found that 71% of respondents said a unified platform for virtual machines and containers would greatly benefit their organization — not a future-state aspiration, but a near-term infrastructure priority.

Kubernetes Orchestration and Container Management

Scheduling containers onto nodes is the starting point, not the full scope, of a mature Kubernetes orchestration implementation. Namespace-based workload isolation, role-based access control, admission webhooks for policy enforcement, and multi-cluster federation for workloads that span environments are all part of running Kubernetes at production scale.

For many organizations, VM-to-container migration is already in motion. As of the Portworx 2024 report, 58% of Kubernetes-using organizations plan to move some VM workloads to Kubernetes management, and 65% plan to do so within two years. Unified orchestration of VMs and containers under a single control plane is not a future-state planning exercise. For most, it is already an active requirement.

Open source projects like KubeVirt enable VMs to run as Kubernetes-native workloads, and platforms built for distributed container management extend that model across multi-cluster, multi-cloud topologies. Teams can run VMs and containers side-by-side, migrate workloads forward at their own pace, and manage the entire estate from a single interface.

Networking and Service Communication

Within a Kubernetes-based cloud stack, networking is the layer through which workloads communicate internally and expose endpoints externally. Pod networking via CNI plugins, service discovery, ingress routing, and network policy enforcement are its core responsibilities. At scale, service mesh tooling (Istio, Linkerd, Cilium) adds mutual TLS, traffic shaping, and network-layer observability without requiring changes to application code.

Network policy also functions as a security boundary. Defining egress and ingress rules at the namespace or pod selector level limits the blast radius of a compromised workload and provides a declarative audit trail of intended communication patterns across the cluster.

Persistent Storage and Data Management

Few areas of Kubernetes have matured as quickly as storage. Container Storage Interface (CSI) drivers allow any storage backend (cloud block storage, NFS, Ceph, Portworx) to be provisioned and managed as a Kubernetes resource. StorageClasses define the characteristics (performance tier, replication factor, retention policy) of volumes, and dynamic provisioning eliminates manual pre-allocation.

For AI and data-intensive workloads, storage performance and data locality are as important as raw capacity. Model weights, training datasets, and inference logs have different throughput requirements, access patterns, and retention policies. An object store optimized for a recommendation model's training data ingestion, for example, may perform poorly when the same cluster uses it for low-latency inference serving with sub-100ms SLAs.

Monitoring, Logging and Observability

A Kubernetes-based cloud stack generates observability data at multiple layers simultaneously: container resource utilization, pod scheduling events, node health, API server latency, and application-level metrics. The CNCF ecosystem provides well-integrated tooling: Prometheus for metrics collection and alerting, Grafana for visualization, OpenTelemetry for standardized telemetry instrumentation, and Fluentd or Loki for log aggregation.

CNCF 2025 data shows that 47% of organizations cite cultural changes within development teams as their top container adoption challenge. Tooling alone does not solve that. Alert routing, on-call runbooks, and post-incident review processes determine whether the data the platform produces translates into faster recovery.

Security, Governance and Policy Enforcement

No single control governs security across a Kubernetes cluster. RBAC handles identity and action permissions at the API server level, forming the foundational layer that everything else builds on. Pod Security Admission and admission webhooks enforce runtime constraints at the workload level. Policy as code tools like Open Policy Agent and Kyverno extend this further: security rules are defined in version control and automatically enforced at deploy time across every cluster in the estate.

Certificate management (Cert Manager), secrets management (Vault integration or Kubernetes Secrets with encryption at rest), and runtime threat detection (Falco) round out the security layer. In regulated environments, policy enforcement must also generate compliance evidence: audit logs, configuration snapshots, and policy violation reports that satisfy external auditors.

Knowing which components belong in a cloud-native platform and how to secure them is prerequisite knowledge. Knowing which platform to choose for a specific delivery context, such as a multi-tenant SaaS product, requires a different frame.

How to Choose the Best Cloud Stack for SaaS Applications

SaaS applications place specific demands on cloud infrastructure that general-purpose stacks may not address without deliberate design. Multi-tenancy, high release velocity, elastic scaling in response to customer demand, and strong compliance posture all require decisions made at the platform level, not patched in after deployment.

The urgency of these decisions has increased. The Spectro Cloud 2024 enterprise Kubernetes report found that 59% of enterprises accelerated cloud-native adoption following Broadcom's acquisition of VMware, meaning a significant portion of the market is actively re-evaluating its cloud platform architecture now. Meanwhile, CNCF 2024 data shows 37% of organizations use two cloud service providers and 26% use three, making multi-cloud portability a baseline SaaS requirement rather than an optional design consideration.

How to Design a Cloud Stack for AI and ML Workloads

Designing a cloud stack for AI and ML requires GPU-aware scheduling, high-throughput storage for model weights and training datasets, autoscaling tuned to inference demand rather than generic CPU thresholds, and observability that surfaces model performance signals alongside infrastructure metrics.

AI infrastructure has become one of the fastest-growing segments of enterprise IT. Gartner projects that end-user spending on AI-optimized IaaS will reach $37.5 billion in 2026, up from $18.3 billion in 2025, a 146% growth rate. The demand driver is not model training. Gartner projects that inference workloads will account for 55% of AI-optimized IaaS spending by 2026, rising to over 65% by 2029, reflecting the shift from building AI models to running them continuously in production.

According to the CNCF 2025 Annual Cloud Native Survey, 52% of organizations do not build or train their own AI models — they are consumers of pre-trained models, running inference to power production applications. The infrastructure challenge is not building AI; it is serving AI reliably and cost-efficiently at scale.

1. Build Infrastructure for GPU-Intensive Workloads

GPU infrastructure is expensive and, without deliberate orchestration, routinely underutilized. Contrary Research's analysis of AI build-out economics cites McKinsey projections that data centers will require $5.2 trillion in AI compute investment by 2030. At that capital scale, inefficient GPU utilization is not a technical footnote. It is a financial risk. Neocloud providers are increasingly competitive on GPU infrastructure by removing the virtualization overhead inherent in hyperscaler environments — a structural advantage that is accelerating enterprise workload migration away from traditional public cloud.

Effective GPU utilization requires explicit orchestration choices:

• Partition GPUs using MIG (Multi-Instance GPU) for workloads that do not require a full GPU, maximizing hardware utilization across teams.

• Use node taints and tolerations to reserve GPU nodes for GPU workloads and prevent CPU-only jobs from consuming expensive GPU capacity.

• Enable gang scheduling for distributed training jobs that require multiple GPUs co-scheduled simultaneously on NVLink-connected hardware; separating linked GPUs causes severe performance degradation.

• Instrument GPU utilization with DCGM exporters and surface those metrics in Prometheus to support right-sizing and workload placement decisions.

2. Use Kubernetes to Orchestrate Workloads

For AI infrastructure, Kubernetes has emerged as the most widely adopted orchestration layer. CNCF 2025 data puts 66% of organizations already hosting their generative AI workloads on it. In practice, it comes down to built-in capabilities: GPU-aware scheduling, node affinity rules, resource quotas for sharing expensive hardware across teams, and autoscaling policies tuned to inference demand spikes.

Multi-cluster management matters because organizations manage an average of 20 or more clusters across four or more environments (Spectro Cloud 2024). AI workloads increasingly need to run portably across this distributed estate. Training runs in one region, inference is served in another, and capacity is federated across on-premises GPU clusters and public cloud. Operationally, a Kubernetes-native control plane that spans environments without cloud-specific abstractions is what makes that model feasible.

3. Optimize Storage and Data Pipelines for AI Applications

AI workloads are storage-intensive in ways that differ from traditional application workloads. Model weights, training datasets, feature stores, and inference logs each have different access patterns, performance requirements, and retention policies. A storage architecture that works well for transactional databases may perform poorly under the sequential read patterns of model loading or the high-throughput writes of training data ingestion.

Key storage design considerations for AI include:

• High-throughput object storage for training datasets and model artifacts, accessible across multiple nodes simultaneously during distributed training.

• Low-latency block storage for model serving where inference latency is a hard requirement and model loading time directly affects user experience.

• Data pipeline tooling (Kubeflow Pipelines, Apache Airflow on Kubernetes) that manages the movement of data from raw storage through preprocessing to training and evaluation.

• Versioning and lineage tracking for datasets and model artifacts, supporting reproducibility and satisfying compliance requirements in regulated industries.

4. Design for Scalable Model Training and Inference

Training and inference place fundamentally different demands on infrastructure. Training is compute-intensive, runs for hours or days, and can tolerate higher latency. Inference is continuous, latency-sensitive, and must scale automatically in response to request volume. Designing for both on the same infrastructure requires explicit separation of node pools, scheduling policies, and autoscaling configurations.

AI inference serving at scale requires autoscaling driven by custom metrics (requests per second, GPU memory utilization) rather than generic CPU thresholds. Model lifecycle management needs versioned deployments, canary releases, and rollback triggered by model performance signals rather than application health alone. Per the CNCF 2025 Annual Cloud Native Survey, only 7% of organizations achieve daily AI model deployments; most deploy models only occasionally. The infrastructure gap between occasional deployment and continuous production serving is what MLOps tooling on Kubernetes is designed to close.

5. Implement Observability and Automation for AI Infrastructure

AI workloads require a more sophisticated observability posture than traditional application workloads. Beyond standard infrastructure metrics, AI systems need GPU utilization, inference latency distributions, request queue depth, token throughput, and model accuracy drift signals surfaced in a unified monitoring layer. AI observability tooling must bridge the gap between infrastructure metrics and model performance metrics, a gap that standard APM tooling does not address.

Practical automation for AI infrastructure spans the full workload lifecycle. Retraining pipelines that trigger when model performance degrades past a defined threshold keep accuracy from drifting silently without requiring manual intervention. Inference deployments that scale in response to traffic patterns keep GPU costs aligned with actual demand. Scale-to-zero for models not receiving active requests becomes a meaningful cost lever at large fleet sizes.

For regulated industries, automated compliance reporting that captures model versioning, data lineage, and deployment history in audit-ready form is not optional: it is a prerequisite for production deployment.

Simplify Cloud Infrastructure Management With Mirantis

Across all four infrastructure contexts in this guide, the architectural evidence points in the same direction. Declarative, automated infrastructure reduces the operational variance that makes enterprise cloud infrastructure expensive to run at scale. A consistent orchestration layer (one that spans VMs, containers, and AI workloads across hybrid environments) keeps security policy enforceable and cost attribution meaningful. Organizations that establish these cloud architecture foundations before complexity outpaces tooling are better positioned to scale efficiently and keep infrastructure costs in line with actual demand.

Mirantis k0rdent is a Kubernetes-native distributed container management platform built for all four contexts. Cloud providers and neoclouds use it to schedule GPU resources and manage multi-tenant compute at scale. Gartner projects neoclouds will capture approximately 20% of the $267 billion AI cloud market by 2030, and the software layer that makes that operationally viable is what k0rdent provides. Enterprises managing hybrid VM and container estates get a unified control plane through MKE and k0rdent, running both workload types side-by-side without forced cutover timelines or duplicate toolchains. For organizations deploying AI in production, GPU scheduling, multi-cluster management, automated scaling, and integrated observability are first-class capabilities, not integrations added after the fact.

Book a demo today to see how Mirantis can help enhance your cloud infrastructure stack.

Common Cloud Stack Design Mistakes

Even teams with clear architectural intent run into predictable pitfalls when building a cloud infrastructure platform. Most of them are not one-time failures; they compound over time into operational debt that becomes expensive to unwind.

• Building around vendor-specific services. Locking core infrastructure logic into proprietary APIs or managed services that lack portable equivalents creates migration risk and limits negotiating leverage. 55% of IT professionals already feel locked into their current vendor choices (Spectro Cloud 2024). Open standards and Kubernetes-native abstractions provide the portability layer that keeps options open.

• Underestimating observability requirements. Teams routinely treat monitoring as something to add after the stack is running. In practice, observability tooling needs to be designed in from the start: alert routing, dashboards, and on-call runbooks need to reflect real workload behavior, not generic defaults. Alert fatigue from misconfigured observability is a leading cause of missed incidents at scale.

• Running VMs and containers on separate operational planes. Maintaining distinct toolchains, separate RBAC policies, and siloed teams for VMs and containers doubles operational overhead and makes consistent policy enforcement nearly impossible. 79% of organizations planning VM migration cite operational simplicity as the primary driver. The dual-plane model is a recognized cost that compounds until addressed.

• Overprovisioning GPU resources. GPU hardware is expensive, and the intuition to overprovision for safety is costly at scale. Without deliberate scheduling (MIG partitioning, gang scheduling, DCGM-based utilization tracking), GPU clusters routinely sit underutilized while teams pay full capacity rates. Neocloud infrastructure is purpose-built for this, by eliminating the virtualization and abstraction layers common in hyperscaler environments, it enables the kind of efficient, direct scheduling that keeps GPU costs aligned with actual demand.

• Delaying security policy implementation. Security policy applied retroactively to a running cluster is harder to enforce and more disruptive to implement. RBAC structures, pod security profiles, network policy, and admission webhooks are significantly less expensive to define before workloads are onboarded than to retrofit around applications that were never designed with those constraints in mind.

  Frequently Asked Questions

  Is a cloud computing stack the same as a tech stack?

  No. A tech stack describes the application-layer technologies an organization uses to build software: programming languages, frameworks, databases, and APIs (React, Java, PostgreSQL, and similar choices). A cloud infrastructure stack describes the infrastructure layers that run those applications: compute, networking, storage, container orchestration, observability, and security. The two overlap at the deployment layer, where application code is packaged into containers and handed off to Kubernetes for scheduling, but they represent fundamentally different scopes of decision-making.

  What is the difference between a cloud computing stack and a cloud platform?

  A cloud computing stack describes the complete set of infrastructure layers an organization assembles and operates: compute, networking, storage, container orchestration, observability, and security. A cloud platform, in most vendor usage, refers to a managed offering that provides those layers as a service — AWS, Azure, and GCP are cloud platforms. Organizations often build their cloud computing stack on top of one or more cloud platforms, using Kubernetes and open source tooling to create a consistent operational layer that works across provider boundaries.

  Does a cloud computing stack require Kubernetes?

  Kubernetes is not technically required, but it has become the practical standard. 82% of container-using organizations run Kubernetes in production as of 2025, and it provides capabilities (GPU-aware scheduling, multi-cluster federation, policy as code enforcement, unified VM and container orchestration) that are difficult to replicate with alternative tools at comparable scale. Organizations building new cloud stacks without a Kubernetes layer will encounter significant gaps in automation, portability, and operational consistency as workload complexity grows.

  Can a cloud computing stack run on-premises?

  Yes. A cloud computing stack is defined by its software architecture, not its physical location. Kubernetes-based cloud stacks run equally well on bare-metal on-premises hardware, in private data centers, and across hybrid combinations of on-premises and public cloud infrastructure. 86% of organizations already run their cloud native platforms in hybrid environments, per Portworx 2024 data. The practical requirement is consistent tooling and operational practices across environments — which is exactly what a Kubernetes-native platform provides.

  What cloud stack components are specifically needed for AI and ML workloads?

  AI and ML workloads require several capabilities beyond a standard application cloud stack. GPU-aware scheduling (with support for MIG partitioning and gang scheduling for distributed training) is the foundational requirement. High-throughput storage with low-latency access paths for model weights and inference serving is equally critical. Beyond compute and storage, AI workloads need autoscaling tuned to custom metrics (token throughput, GPU memory utilization), model lifecycle management tooling for versioned deployments and canary releases, and observability that surfaces model performance signals alongside infrastructure metrics.

  How do I migrate from traditional infrastructure to a cloud computing stack?

  The most reliable approach is gradual migration rather than big-bang re-architecture. Start by containerizing new workloads and deploying them through a Kubernetes-based orchestration layer, establishing operational practices with lower-stakes applications before moving business-critical ones. For existing VM workloads, tools like KubeVirt allow VMs to run as Kubernetes-native workloads under the same control plane, so teams can preserve existing applications while building toward unified orchestration. Per Portworx 2024, 79% of organizations migrating VM workloads to Kubernetes cite operational simplicity and cost reduction as their primary drivers — which means the migration itself delivers measurable value before it is complete.