Best practices for Kubernetes Troubleshooting (with AI)

Kubernetes environments are inherently complex and dynamic distributed systems. This complexity can make troubleshooting issues a daunting task, often requiring deep expertise.

As companies scale their Kubernetes deployments, they often encounter an increasing frequency and variety of issues. Common problems such as CrashLoopBackOff, ImagePullBackOff, and nodes reporting a NotReady status can significantly disrupt service delivery and negatively impact user experience. Addressing these issues promptly is crucial for maintaining operational efficiency and ensuring a seamless experience for end users on the Kubernetes-powered platform.

In this post, we’ll explore the key challenges of troubleshooting Kubernetes, explain why traditional methods are costly and fall short at scale, and demonstrate how Plural’s AI-powered troubleshooting features can simplify this process — help businesses save time, quickly remediate issues, and improve uptime.

Challenges with troubleshooting Kubernetes

Managing Kubernetes at scale is a daunting task, often requiring a specialized team of DevOps and Site Reliability Engineers (SREs)—a resource that is frequently in short supply.

As the Kubernetes environment scales, the interconnected components—such as Pods, Deployments, Services, Ingress, and more—introduce significant complexity. Troubleshooting issues within this ecosystem involves navigating a maze of YAML configurations, logs, and ambiguous error messages.

Many companies go through the following common pain points when operating Kubernetes at scale:

1: Opaque error messages with poor interpretability

Troubleshooting Kubernetes often involves dealing with dense, low-level information that can leave even experienced DevOps engineers guessing. For instance, a vague CrashLoopBackOff status could indicate anything from app configuration errors, resource issues, application bugs, or microservices dependency failures. An example would be when a pod enters a CrashLoopBackOff state due to insufficient memory, but the error message doesn’t specify whether it's a resource limit issue or a problem with the application itself, making it difficult to identify the root cause quickly.

Tools like Terraform, essential for managing infrastructure alongside Kubernetes, can add another layer of complexity. Terraform often outputs verbose, raw cloud provider errors that provide little actionable guidance, making it harder to connect infrastructure issues with Kubernetes-specific problems.

2: Complex configurations

As Kubernetes environments scale, the complexity of configurations grows exponentially. YAML manifests define the desired state of Kubernetes, but even a single misplaced line can lead to deployment failures or unexpected behavior. For example, an incorrect indentation in a ConfigMap or a missing label in a Deployment manifest can cause pods to fail to start or services to be unreachable. As infrastructure complexity increases, pinpointing these misconfiguration issues can feel like searching for a needle in a haystack.

3: High barrier to entry

Kubernetes troubleshooting is intimidating for junior DevOps engineers, which means that teams often rely on senior staff to resolve issues. However, engineers with extensive experience with Kubernetes are in short supply, and if a senior engineer leaves without properly documenting operational procedures, the team may struggle to troubleshoot issues on their own.

4: Multiple layers of abstraction

Kubernetes operates on multiple layers—Pods, Deployments, Services, Ingresses, and more—each relying on the other. Issues in one layer can quickly affect others, complicating root cause analysis (RCA) and potentially leading to prolonged remediation times. For example, a misconfigured Ingress resource can prevent traffic from reaching the correct Service, which in turn affects the Pods running the application. Similarly, a Deployment issue could lead to Pods being stuck in a pending state, causing the associated Service to fail in routing traffic, even if the underlying network is fine.

The cost of ignoring these challenges

The challenges associated with Kubernetes troubleshooting directly impact the businesses that rely on these platforms, often leading to undesirable outcomes:

• Downtime and revenue loss: Extended system outages can severely affect customer satisfaction, resulting in lost revenue and damage to brand reputation. For example, an unresolved service outage could lead to customers being unable to access critical features, causing a drop in usage or even churn.
• Wasted engineering hours: Teams may spend valuable time manually interpreting logs, events, and configurations rather than focusing on strategic initiatives like improving product features or scaling the infrastructure. For instance, time spent debugging obscure error messages or identifying misconfigured manifests can delay important projects.
• Talent bottlenecks: Dependence on senior engineers for troubleshooting creates strain on your most skilled team members, leading to burnout and extended resolution times. A senior engineer might spend hours fixing a misconfiguration that a junior engineer could have resolved with proper documentation or tooling.

Traditional troubleshooting methods don’t scale.

DevOps teams managing Kubernetes currently mostly rely on various DevOps toolchains and manual processes to troubleshoot Kubernetes.

Most cloud provider UIs (like the GCP UI) offer only surface-level insights into Kubernetes resources, so in order to sift through Kubernetes events, logs, and Terraform outputs to diagnose issues, they need to rely on CLI commands and custom scripts.

If they’re a little luckier, they might use something like ArgoCD, which is great for managing deployments but lacks rich diagnostic capabilities for troubleshooting more intricate issues. A common scenario is when ArgoCD shows a deployment error without offering useful insights into the root cause, forcing engineers to manually inspect logs or manifests for deeper understanding.

For example, below are some of the most frequent errors seen on Kubernetes environments, their causes, and how teams attempt to resolve them manually.

1: Image pull failures

Image pull failures occur when Kubernetes cannot retrieve a container image from the specified registry. These issues disrupt pod initialization and can arise from a variety of reasons.

Common causes include:

• The image name or tag is incorrect, leading Kubernetes to search for a non-existent image.
• Authentication or permission issues prevent access to a private container registry.
• Network connectivity problems block communication between the Kubernetes cluster and the registry.
• Resource constraints on the node hinder image download or extraction.

Troubleshooting steps involve:

• Confirm the image name and tag in your deployment or pod specifications.
• Check the registry for the image’s existence using tools like Docker Hub or private registry dashboards.
• Manually pull the image using docker pull to verify connectivity and authentication credentials.
• Ensure the Kubernetes node has sufficient resources (storage).

2: Configuration errors

Kubernetes relies heavily on YAML configuration files, making typos and misconfigurations a common issue. These errors can prevent resources from being created or functioning correctly.

Common causes include:

• Typo or syntax errors in YAML files disrupt the deployment process.
• Mismatched API versions create compatibility issues between configurations and the Kubernetes cluster.
• Incomplete or incorrect resource definitions, such as missing required fields or invalid arguments, cause failures.

Troubleshooting steps involve:

• Use kubectl apply --dry-run=client to validate the YAML file before applying it to the cluster.
• Employ YAML linting tools to identify and correct syntax errors.
• Cross-check the API version and resource specifications against Kubernetes documentation.
• Review all environment variables and command-line arguments to ensure they align with application requirements.

3. Permission issues

Kubernetes enforces access control policies through Role-Based Access Control (RBAC) and other mechanisms. Permission issues often lead to failed resource access or operations.

Common causes include:

• Insufficient permissions assigned to the service account used by an application or pod.
• Misconfigured RBAC roles or bindings prevent proper resource access.
• Security context constraints restrict specific actions at the cluster or namespace level.

Troubleshooting steps involve:

• Use kubectl auth can-i command to verify the service account’s permissions for specific operations.
• Inspect RBAC roles, role bindings, and cluster role bindings for misconfigurations.
• Modify security contexts and constraints if necessary, ensuring compliance with your organization’s security policies.

4: API call failures

Applications and Kubernetes core components frequently communicate via the Kubernetes API server. Failures in these REST API calls can disrupt normal operations and degrade cluster performance.

Common causes include:

• The API server is unavailable or overloaded due to high request rates or insufficient resources.
• Network issues between cluster components interrupt API communications.
• Authentication or authorization failures block valid API requests.
• Rate-limiting mechanisms throttle excessive API calls.

Troubleshooting steps involve:

• Check the health of the API server using kubectl get componentstatus and review its logs for errors.
• Diagnose and resolve network connectivity issues between cluster nodes and components.
• Verify and update authentication tokens, certificates, or RBAC settings to restore access.
• Monitor API request rates and adjust limits or optimize the application to reduce API calls.

5: Terraform state issues

Terraform is commonly used to manage Kubernetes infrastructure, but its state file—a single source of truth for resource management—can become a source of problems.

Common causes include:

• Corrupted or inconsistent state files disrupt Terraform’s ability to manage resources.
• Concurrent modifications to the state file create race conditions.
• Backend storage issues, such as connectivity problems or misconfigurations, compromise state file access.

Troubleshooting steps involve:

• Use Terraform’s state subcommands to inspect and resolve inconsistencies.
• Enable state file-locking mechanisms to prevent simultaneous changes.
• Diagnose and repair backend storage issues, ensuring stable access to the state file.

By addressing these errors methodically and understanding their root causes, teams can improve their Kubernetes troubleshooting capabilities. However, these manual methods are still time-intensive and prone to human error. This creates the need for scalable, automated solutions when managing infrastructure at scale.

How Plural solves Kubernetes troubleshooting with AI

Plural simplifies Kubernetes troubleshooting by leveraging LLMs to provide actionable insights, automated diagnostics, and precise fix recommendations across all your clusters. By analyzing the full context of your underlying infrastructure–including your source code, Kubernetes API state, logs, and observability data, Plural’s AI empowers teams to resolve issues quickly and efficiently at scale.

1: Automated issue detection

Plural continuously monitors the health of all managed Kubernetes clusters to proactively identify and flag issues as they arise, reducing the need for manual intervention.

• Service failure detection: Automatically detects pod restarts, failed deployments, or stuck states.
• Knowledge graph: Maps dependencies between Kubernetes resources (e.g., linking pods to services or ingresses), providing detailed insights into application interactions.
• Context-aware analysis: Highlights only the most relevant errors, filtering out noisy logs or unnecessary information.

For example, when a pod enters a CrashLoopBackOff state, Plural not only identifies the pod but also pinpoints the specific configuration or resource causing the issue.

2: Intelligent error analysis

Leveraging large language models (LLMs), Plural AI analyzes complex errors and translates them into actionable insights.

• Natural language explanations: Translates Kubernetes API and Terraform errors into plain English, making troubleshooting more accessible to all team members.
• Root cause identification: Combines configuration data, runtime logs, and event streams to isolate the primary source of issues.
• Contextual recommendations: Provides tailored solutions based on the unique setup of each cluster.

For example, in case of an ImagePullBackOff error, Plural can identify issues with pulling the container image from the registry and recommend a fix, all without needing manual troubleshooting.

3: Automated fix suggestions

Beyond diagnostics, Plural provides actionable solutions to address identified issues directly.

• Git repository integration: Links errors to relevant Git repositories, files, and specific lines of code or YAML.
• Fix proposals: Generates updated YAML snippets or Terraform configurations.
• Interactive guidance: Offers step-by-step instructions to help engineers apply fixes confidently.

For example, in a misconfigured pod resource limit, Plural might identify the issue and suggest an updated YAML snippet to adjust the memory and CPU limits, ensuring the pod can start without being throttled or evicted.

4: Interactive troubleshooting

Plural’s AI-powered chat interface makes troubleshooting an intuitive and collaborative process.

• Real-time Q&A: Engineers can ask questions like, “Why is this pod stuck in a Pending state?” and receive actionable answers.
• Explain AI: Engineers can ask AI to explain a specific page like, “cert-manager service” and receive context-specific detailed information about cert-manager specific to your cluster.

By integrating AI into every step of the troubleshooting process, Plural eliminates guesswork, reduces time to resolution, and scales effortlessly to meet the demands of modern Kubernetes environments.

Kubernetes troubleshooting made simple with Plural AI

Troubleshooting Kubernetes doesn’t have to involve endless hours of log analysis, trial-and-error, or over-reliance on senior engineers. Plural is able to translate raw logs and errors into natural language explanations and fixes by leveraging LLMs and deep integration into your Kubernetes stack.

In doing so, Plural saves time, reduces downtime, and empowers engineers of all experience levels to solve challenges efficiently.

Ready to transform your Kubernetes operations? Schedule a demo to experience the power of Plural in action.