Ask the Cloud Expert: The Smarter Way to Kubernetes Management

Kubernetes Engine has become the go-to service for deploying, scaling, and managing containerized applications — so much so that containerization is now almost synonymous with Kubernetes. This popularity and wide adoption across organizations of all sizes is well earned. According to the Cloud Native Computing Foundation, 80% of organizations are already running Kubernetes to deploy their software in production environments.

However, as much value as Kubernetes adds, it only does so when implemented, managed, and optimized correctly. Issues such as configuration drift and stuck pods are common, often stemming from incorrect implementations, and can quickly lead to runaway costs and security vulnerabilities. To avoid this, it’s essential to be well-versed in the nitty-gritties of Kubernetes — the common pitfalls, challenges, and best practices, both during implementation and post-deployment.

That’s exactly why we bring you the latest edition of “Ask the Cloud Expert: The Smarter Way to Kubernetes Management.” This edition delves into expert insights on the Kubernetes journey — from implementation to post-deployment optimization.

Featured expert for this edition: Raghu Sharma

Raghu is a Senior DevOps Architect at CloudKeeper, bringing deep experience in cloud infrastructure, with a particular focus on both devops and finops automation—making him the go-to expert for orchestration, containerization, and Kubernetes. Having led multiple initiatives across high-scale, multi-cloud environments, he has gained extensive experience in building and mentoring DevOps teams, as well as managing cloud transformations using tools such as Terraform, Kubernetes, Jenkins Shared Library, ECS, and Fargate, among many others.

Let’s get started!

Part 1: Orchestration Basics & Why Kubernetes Matters

Q1. What is cloud-based orchestration, and why do enterprises prefer Kubernetes for orchestration when hosting their software on the cloud?

Before discussing cloud-based orchestration specifically, it is essential to understand what orchestration is. Orchestration, to simplify, is coordinating multiple automated tasks into a cohesive workflow. For example, you can orchestrate data pipelines, application deployments, server provisioning, or workflow automation across systems.

Orchestration, in the context of cloud computing, refers to using tools and code to automate key tasks required to simplify managing connections between clients and servers, and workloads as well. Orchestration technologies such as Kubernetes, Docker Swarm, Apache Mesos, and HashiCorp Nomad integrate all these into a streamlined workflow, enabling better scalability and reliability.

Kubernetes, also known as K8s, is especially preferred by organisations because :

• Kubernetes is an open-source platform
• Provides load balancing and significantly simplifies multiple-host-based container management
• Portability across cloud providers, thus lifting and shifting from AWS, GCP, and Azure, won’t be a challenge if you choose to switch cloud providers
• It pioneers system reliability by automatically redistributing workloads (pods) to other nodes within the same cluster for high availability and resource efficiency. However, managing workloads across different clusters requires external tools.
• Automation of scaling and deployment — containers are automatically provisioned and fit into nodes to maximize resource efficiency

Q2. Why do SaaS providers and ISVs require orchestration, and what is the importance of software containerization for them? How does Kubernetes fit into this picture?

For SaaS providers and ISVs, it’s best to start with their requirements — and then see how containerization fits into the picture to solve their use cases.

To understand the importance of cloud orchestration for SaaS and ISVs, it’s essential to look at the kind of workloads they run and the scale at which they operate. A SaaS application’s or ISV’s user base can spike or drop suddenly, data processing demands can fluctuate based on customer activity, and new feature rollouts or patches can create sudden surges in traffic — all at speeds that would be impossible for a DevOps team to handle manually. That’s where orchestration steps in.

Orchestration takes care of these challenges holistically by:

• Automatically scaling workloads up or down to handle unpredictable traffic.
• Managing deployment pipelines to ensure faster rollouts with minimal downtime.
• Maintaining high availability with failover and recovery mechanisms.
• Optimizing infrastructure usage to keep performance high while controlling costs.

Coming to containerization, ISVs and SaaS companies must deliver high performance and availability irrespective of fluctuating workloads — and that’s exactly what containerization solves. Because containerization essentially works on the principle of “write once, run anywhere.” Every container contains all the dependencies required to run the software, making it easy to deploy on multiple machines and rethink virtualization at its core.

Problems containerization solves include:

• Portability across different environments (cloud, on-prem, hybrid).
• Faster and more consistent deployments.
• Easier isolation of workloads for security and performance.
• Improved developer productivity by standardizing environments.

Kubernetes then comes into the picture as the one-stop technology for containerization and orchestration. It simplifies container management with features like automated rollbacks, versioned upgrades, and self-healing measures (automatically restarting failed containers or redistributing workloads). With proper configuration, Kubernetes also automates resource management, ensuring maximum uptime while reducing operational overhead — making it the go-to tool for managing SaaS and ISV workloads at scale.

Part 2: Challenges in Kubernetes Adoption

Q3. What challenges do DevOps teams typically face when setting up containerization, particularly with Kubernetes?

Kubernetes, while now almost a given for organizations looking to containerize their workloads on the cloud, comes with its own steep learning curve. DevOps engineers face difficulties at every stage of configuring a K8s cluster — the root cause being the complex, multi-layered configurations and the need to master new abstractions that differ significantly from traditional infrastructure practices. Issues such as troubleshooting stuck pods, handling configuration drift, managing RBAC policies, and optimizing resource allocation further add to the challenge, making Kubernetes harder to grasp initially.

Some of the top challenges a DevOps team is likely to face when getting started with Kubernetes containers are:

1. Networking Challenges

Networking is complex and difficult to troubleshoot. The multiple networking layers in Kubernetes add to this complexity, creating many moving parts that make management even more challenging.

Some networking-related issues I can think of include VPCs not being configured properly, which can lead to IP address exhaustion. As pods scale easily and exponentially, the VPCs must also be configured to scale accordingly.

2. Ensuring Cluster Uptime

K8s containers don’t exist for long periods in practice. Thanks to the automations written for scaling, they are frequently created and terminated depending on workload fluctuations. If you encounter a bug in such a distributed environment, debugging becomes particularly challenging. DevOps teams often struggle with setting up centralized tracing, logging, and robust alerting for CPU/memory usage across nodes. Without these, ensuring uptime is guesswork.

3. Security

Incorrect pod communications and misconfigurations are among the leading causes of Kubernetes-related security incidents. The challenge lies in correctly implementing RBAC (Role-Based Access Control), network policies, and secrets management. However, good network segmentation policies and proper service-to-service communication rules can mitigate most security loopholes.

To add to this point, Kubernetes provides multiple security measures, but many DevOps engineers hesitate or don’t implement them. The main reason is the steep learning curve combined with the sheer number of security options—so many choices and alternatives can easily overwhelm people.

4. Managing Storage

If we take the example of a K8s cluster hosted on AWS, EBS (Elastic Block Store) volumes can quickly run out of space if not managed properly. Since Kubernetes storage is not inherently persistent, it’s best practice to store persistent data (build files, logs, databases) in cloud provider-managed storage services. But this creates another problem: log files, temporary cache data, and crash dump files can quickly bloat storage usage, leaving you paying for inefficient storage.

The right approach is to use Kubernetes-native features like the Container Storage Interface (CSI), StatefulSets, Persistent Volumes (PVs), and Persistent Volume Claims (PVCs) to take better control over what data goes into persistent storage — and avoid wasting money and space.

Q4. While deploying Kubernetes, what are the critical, non-negotiable aspects that organizations should never overlook?

While setting up a Kubernetes cluster, your priority should be to maximize uptime by ensuring high availability, robust security mechanisms, proper logging and visibility tools, as well as implementing CI/CD pipelines in case you need to update build dependencies for the software you’ve orchestrated.

The following are some of the best practices you need to consider when setting up your K8s clusters:

1. Use Canary and Blue-Green deployment strategies: Unlike the running joke, you shouldn’t test in production. By following Blue-Green and Canary deployments, you can extensively test your software before releasing it into production, ensuring safer and faster rollouts.
2. Put CI/CD configuration in place: A strong CI/CD pipeline ensures that your deployments are automated, reliable, and repeatable, reducing manual effort and minimizing downtime during updates.
3. Ensure fault tolerance from the first deployment: Use Pod Anti-Affinity and Node Affinity rules to spread pods across nodes or availability zones, preventing a single point of failure.
4. Implement resource requests and limits: Define clear CPU and memory requests/limits for each pod to avoid resource starvation, noisy-neighbor issues, and unexpected crashes in production.
5. Secure by default: Enforce RBAC policies, enable Pod Security Standards (or PodSecurityPolicy if still in use), and restrict root access. Secrets should be stored securely using tools like Kubernetes Secrets or external vaults.
6. Centralize logging and monitoring: Use tools like Prometheus + Grafana, ELK/EFK stacks, or OpenTelemetry to gain visibility into cluster health, performance bottlenecks, and anomalies. Centralized logging simplifies debugging and enhances uptime.

Part 3: Performance & Cost Optimization

Q5. What are the most common performance issues encountered with Kubernetes systems, and how can they be tackled effectively?

Before diving into the performance issues themselves, we must take a step back and understand the reasons behind them. They stem from the steep learning curve Kubernetes has, and while dealing with the complexities of the system itself and keeping the costs in check, teams often don’t provision enough resources, or even if they do, not the right kind and services needed.

Coming to the performance issues themselves, here are the following:

1. CPU Memory Throttling: A result of incorrectly defined CPU and memory request limits. So, when the system scales or load spikes, it leads to performance degradation since the operating system kills the process with something known as Out Of Memory Kill.
2. Pod Scheduling Issues: CrashLoopBackOff, which causes frequent container crashes, ImagePullBackOff, Unschedulable Pods, Liveness and Readiness probe errors are some of the errors that occur as a result of misconfigurations.
3. Networking Bottlenecks: Container Network Interface misconfigurations or incorrect implementations frequently result in communication bottlenecks between the client and server.
4. Horizontal Pod Autoscaler Misconfiguration: When autoscaling thresholds are poorly set or based on the wrong metrics, it can either cause under-scaling, leading to downtime, or over-scaling, leading to unnecessary cloud costs.
5. Storage Inefficiencies: If we take the example of a K8 cluster hosted on AWS, EBS volumes can quickly run out. Since Kubernetes storage is not persistent by default, in practice, it stores persistent data—both build files and logs—in cloud provider services. But log files, crash dumps, temporary container files, and image layers can bloat storage, and you end up paying for inefficient storage. However, by using Container Storage Interface, StatefulSets, Persistent Volume, and Persistent Volume Claim, you would have better control over the data you want in persistent storage.
6. Observability and Monitoring Gaps: Without proper logging, tracing, and metric collection, DevOps teams often miss the early signals of performance degradation. This lack of visibility makes debugging and remediation extremely difficult in distributed K8 environments.

Q6. Performance optimization often sounds expensive. Does improving Kubernetes performance always lead to increased costs? How can organizations strike a balance between cost control and performance requirements?

No, not always. Most performance issues aren’t about throwing more money at the problem; they’re about wrong setups. A lot of the time, you get better results by fixing configs instead of provisioning extra nodes.

• The most common culprit is CPU/memory requests and limits set wrong, leading to throttling or wasted capacity.
• Autoscalers left with default thresholds cause over-scaling during short spikes.
• Idle pods and unused namespaces burn resources silently.
• Overprovisioned persistent volumes that nobody checks end up becoming hidden costs.

The balance comes when you set up automation that scales only when demand is real and shuts things down aggressively when not in use. Kubernetes already gives you those levers—you just have to configure them properly.

Part 4: Tooling & Automation for Kubernetes Management

Q7. What tools would you recommend for effective Kubernetes management? Is manual management ever a better option?

Manual management is fine for learning or testing. For production? It’s a trap. At scale, kubectl is firefighting, not management. You might get away with it on a dev cluster, but when you’re running dozens of nodes and hundreds of pods, you’ll spend your day chasing YAML and crashes instead of running workloads.

The right approach is to layer your setup with tools that give visibility, automation, and guardrails:

• GitOps/CD: ArgoCD or Flux lets you treat your cluster like code. Every deployment is version-controlled, auditable, and repeatable. No more “works on my machine” excuses, and no more manually applying YAML that accidentally takes prod down.
• Monitoring: Prometheus + Grafana are the go-to. Prometheus scrapes metrics from your cluster, while Grafana gives you dashboards that show CPU, memory, pod health, and node status at a glance. Without this, you’ll only know your system’s down when your users start yelling.
• Cost tracking: Tools like Kubecost or CloudKeeper Tuner help you catch where money is leaking—idle namespaces, overprovisioned pods, or nodes running under 10% utilization. Kubernetes by default doesn’t care about your AWS or GCP bill, so without cloud cost visibility, you’re flying blind.
• Logging & tracing: ELK Stack, Loki, or OpenTelemetry give you the ability to track what’s happening inside your containers and across distributed services. When a pod crashes at 3 a.m., logs are the only thing between you and a clueless war room.
• Cluster navigation: Lens or K9s make debugging less painful. Instead of squinting at endless kubectl get pods commands, you get a clean view of pods, nodes, and namespaces. It speeds up troubleshooting massively.

So is manual management ever better? Only if you’re experimenting, spinning up a quick proof-of-concept, or teaching a junior engineer what Kubernetes feels like. But once real traffic hits, once downtime means real money, automation + tooling is the only sane way to run Kubernetes.

Part 5: The Long-Term Future of Kubernetes

Q8. Kubernetes is being widely adopted in 2025, but many technologies eventually fade. What do you think the long-term future looks like for Kubernetes?

Kubernetes isn’t going anywhere. The adoption curve has crossed the point of no return. Too many enterprises, ISVs, and SaaS vendors have standardized on it. Entire ecosystems — monitoring, CI/CD, security, networking — have been built with Kubernetes as the assumed baseline.

What will change is how much of Kubernetes you actually touch as an engineer:

• Managed K8s (EKS, GKE, AKS): These already remove ~70% of the operational overhead. You don’t worry about the control plane or patching masters — the cloud provider does it. Engineers only focus on workloads, scaling, and cost.
• Higher-level abstractions: Platforms like OpenShift, Rancher, or internal PaaS products hide most of the cluster details. To a developer, deploying looks like “git push” or a simple CLI command, while Kubernetes hums invisibly in the background.
• Commoditization: Just like Linux, Kubernetes will stop being the cool headline tech. It’ll become boring infrastructure — the backbone everything else runs on. You don’t think about Linux when deploying software, and the same will happen with Kubernetes.
• Standardization: The Kubernetes API has become the de facto interface for container orchestration. Expect it to be the stable “language” for workloads, with the ecosystem building tooling around it rather than reinventing alternatives.
• Ecosystem maturity: More tooling for policy enforcement (OPA/Gatekeeper), cost control (OpenCost, Tuner), and security scanning will integrate natively. Running Kubernetes won’t feel like stitching 10 tools together anymore.

So, the long-term picture is that Kubernetes will feel invisible. You won’t obsess over pods, nodes, and YAML the way we do today. But behind the scenes, it will remain the backbone of cloud-native workloads — the control fabric that keeps containers running everywhere. Kubernetes won’t fade. It’ll stop being “new” and instead settle into the stack permanently, the same way Linux or TCP/IP did.

Part 6: Partner vs In-House Expertise

Q9. For organizations facing challenges with Kubernetes, would you recommend relying on a partner-led support model or building strong in-house expertise to address issues?

Both have their place — it depends on where you are in your Kubernetes journey.

• Partner-led support: Ideal for the early stages. Partners bring experience from multiple deployments, helping you avoid rookie mistakes, accelerate cluster setup, configure networking correctly, implement RBAC, and establish monitoring and alerting best practices. They also help set up CI/CD pipelines, logging/tracing, and automated scaling — all while maintaining cost efficiency.
• In-house team: Essential for long-term success. Once clusters scale, day-2 operations become complex — patching nodes, managing cluster upgrades, optimizing resource usage, handling multi-cluster networking, and troubleshooting incidents. You can’t outsource every scaling challenge, CI/CD change, or performance bottleneck. Having engineers deeply familiar with your workloads ensures faster response, better cost control, and security compliance.
• Hybrid model: The most practical approach. Start with a partner to accelerate setup, train your engineers alongside them, gradually transfer knowledge, and eventually allow your team to take full ownership.
• Critical workloads: Security, auto-scaling decisions, cost optimization, and disaster recovery should always ultimately sit with your in-house team.

Bottom line is that partners help you get started fast and avoid pitfalls, but real Kubernetes maturity comes when your engineers are battle-tested — capable of handling scaling, optimization, and operational challenges on their own.

To Sum Up 

If you are deploying software on the cloud in 2025, it is essential to containerize and orchestrate it to ensure high availability and robust performance, even in the face of rapidly changing workloads. This is particularly critical for SaaS providers and ISVs, whose software powers many business-critical applications.

Out of all containerization tools and technologies, Kubernetes stands out for its open-source nature, rich ecosystem of tools, seamless integration with CI/CD pipelines for DevOps, automated scaling capabilities, and portability across cloud providers. However, Kubernetes has a steep learning curve, and many developers often make misconfigurations that lead to performance bottlenecks and cost overruns, resulting in unexpectedly high cloud bills.

To get started with Kubernetes deployment effectively, it is recommended to leverage partner-led support, like CloudKeeper, to establish the right processes, ensure best practices, and accelerate operational readiness.

CloudKeeper Simplifies Kubernetes for You

CloudKeeper’s Kubernetes expertise covers all the bases you need to run, scale, and optimize Kubernetes systems on your cloud. Our team of Kubernetes experts will guide you end-to-end with optimization, intelligent monitoring, visibility, and the setup of observability & governance systems, ensuring maximum performance without incurring exorbitant cloud costs—ultimately maximizing your cloud ROI.

Here’s CloudKeeper’s 3-step Kubernetes Optimization Framework:

1. Audit Your Kubernetes Footprint: We take read-only access to your K8 system and analyze setups, versions, scaling, and workloads to identify optimization opportunities.
2. Tuning for Performance & Efficiency: Based on our assessment, we provide data-driven recommendations and implement best practices that optimize cost while boosting performance, reliability, and security.
3. Measure Results & Refine: After implementing strategies, we continuously monitor the impact and provide ongoing recommendations to ensure your Kubernetes environment performs at its best.

Talk to a Kubernetes expert today!