fidzu : k8s

Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals that live entirely outside that narrow window: how many messages are waiting in a queue, how long the last batch job took, how many active WebSocket connections a pod is holding. When the built-in metrics are not enough, a metrics exporter bridges that gap.

This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus - and ultimately the HorizontalPodAutoscaler - can consume it.

What a metrics exporter actually does

An exporter is a small HTTP server with a single responsibility: expose application state as text on a /metrics endpoint. Prometheus scrapes that endpoint on a regular interval, stores the time-series data, and makes it available for queries, alerts, and autoscaling rules.

In some cases you can instrument your application directly - embedding the Prometheus client library and exposing /metrics from within the same process - rather than running a separate exporter. A standalone exporter makes more sense when the data source is external to your application or when you do not control the application code.

The format Prometheus expects is plain text - one metric per line, with a name, optional labels, and a numeric value. Client libraries handle the serialization for you, so in practice you only need to decide what to measure and call the right function when that value changes.

Choosing what to measure

Before writing any code, it helps to decide what kind of signal you are dealing with. The Prometheus data model has three main types:

• Counters only ever increase. They are the right tool for totals: requests served, jobs processed, errors encountered. Never use a counter for a value that can go down.

• Gauges represent a current snapshot of a value that can rise and fall freely. Queue depth, active connections, and cache size are all gauges.

• Histograms record the distribution of observed values, such as request latency. They let you calculate percentiles (p99, p50) rather than just averages.

Once you know which type fits your signal, choose a name that follows the convention <namespace>_<name>_<unit> in snake_case. A job processor might expose worker_jobs_processed_total (counter), worker_queue_depth (gauge), and worker_job_duration_seconds (histogram). Clear names save everyone debugging time later.

Setting up the project

The Go Prometheus client is the most common choice for exporters in the Kubernetes ecosystem, largely because the same library powers most of the official Kubernetes components. Start by creating a module and pulling in the dependency:

mkdir my-exporter && cd my-exporter
go mod init example.com/my-exporter
go get github.com/prometheus/client_golang/prometheus
go get github.com/prometheus/client_golang/prometheus/promhttp

Registering metrics

Create main.go. The first thing to do is declare the metrics and register them with Prometheus's default registry. Registration tells the library that these metrics exist so they appear in the output even before the first observation is recorded:

package main

import (
 "log"
 "net/http"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

var (
 jobsProcessed = prometheus.NewCounterVec(
 prometheus.CounterOpts{
 Name: "worker_jobs_processed_total",
 Help: "Total number of jobs processed, partitioned by status.",
 },
 []string{"status"},
 )

 queueDepth = prometheus.NewGauge(prometheus.GaugeOpts{
 Name: "worker_queue_depth",
 Help: "Current number of jobs waiting in the queue.",
 })

 jobDuration = prometheus.NewHistogram(prometheus.HistogramOpts{
 Name: "worker_job_duration_seconds",
 Help: "Time spent processing a single job.",
 Buckets: prometheus.DefBuckets,
 })
)

func init() {
 prometheus.MustRegister(jobsProcessed, queueDepth, jobDuration)
}

prometheus.MustRegister panics on a duplicate registration, which makes misconfigurations obvious at startup rather than silently at runtime. If you are embedding this exporter inside a library that other packages will also instrument, prefer prometheus.Register and handle the error yourself.

Collecting real values

With the metrics registered, the next step is to keep them current. You can either continually update the data as the data change, or run your own internal refresh loop. The pattern below shows a polling loop - a goroutine that periodically reads from whatever data source your application owns and updates the registered metrics. Replace the simulated values with real calls to your database, internal API, or message broker:

import (
 "math/rand"
 "time"
)

func collectMetrics() {
 for {
 // Replace these with real reads from your application.
 depth := float64(rand.Intn(50))
 queueDepth.Set(depth)

 start := time.Now()
 time.Sleep(time.Duration(rand.Intn(200)) * time.Millisecond)
 jobDuration.Observe(time.Since(start).Seconds())
 jobsProcessed.WithLabelValues("success").Inc()

 time.Sleep(5 * time.Second)
 }
}

The polling interval (here five seconds) should be shorter than Prometheus's scrape interval so that each scrape sees a fresh value. The default scrape interval in most cluster deployments is fifteen seconds, which gives you comfortable headroom.

Exposing the endpoint

Wire the collection loop and the HTTP handler together in main. A /healthz path alongside /metrics gives Kubernetes a liveness probe target without exposing metric data on the health route:

func main() {
 go collectMetrics()

 http.Handle("/metrics", promhttp.Handler())
 http.HandleFunc("/healthz", func(w http.ResponseWriter, r *http.Request) {
 w.WriteHeader(http.StatusOK)
 })

 log.Println("Listening on :8080")
 if err := http.ListenAndServe(":8080", nil); err != nil {
 log.Fatalf("server error: %v", err)
 }
}

Verify the output locally before building the image:

go run .
curl http://localhost:8080/metrics | grep worker_

You should see three # HELP and # TYPE blocks followed by the current metric values. If those lines appear, the exporter is working correctly and is ready to be containerized.

Build a container image

A multi-stage build keeps the final image small and avoids shipping a Go toolchain to production. The first stage compiles a statically linked binary; the second stage copies only that binary into a minimal base. The example below uses Docker, but the same pattern works with any OCI-compatible build tool such as Buildah or Podman:

FROM golang:1.21-alpine AS builder
WORKDIR /src
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 go build -o /exporter .

FROM gcr.io/distroless/static:nonroot
COPY --from=builder /exporter /exporter
EXPOSE 8080
ENTRYPOINT ["/exporter"]

distroless/static:nonroot contains no shell, no package manager, and runs as a non-root user by default, which satisfies most cluster security policies without extra configuration.

Build and push the image, replacing <registry> with your own registry address:

docker build -t <registry>/my-exporter:v1.0.0 .
docker push <registry>/my-exporter:v1.0.0

(Note: Using a CI/CD pipeline to automate this is generally a better pattern than running these commands manually.)

Deploying to the cluster

Two manifests are enough to run the exporter: a Deployment that manages the pod lifecycle, and a Service that gives Prometheus a stable address to scrape. (You might prefer to have Prometheus scrape from every Pod; if that makes sense for your use case, then it's OK to configure instead).

The examples below use the monitoring namespace, which is a common convention when running Prometheus and related components together. Adjust the namespace to match your own cluster setup.

The Deployment sets conservative resource limits appropriate for a lightweight sidecar-style process, and uses the /healthz route for its liveness probe:

apiVersion: apps/v1
kind: Deployment
metadata:
 name: my-exporter
 namespace: monitoring
 labels:
 app.kubernetes.io/name: my-exporter
spec:
 replicas: 1
 selector:
 matchLabels:
 app.kubernetes.io/name: my-exporter
 template:
 metadata:
 labels:
 app.kubernetes.io/name: my-exporter
 spec:
 containers:
 - name: exporter
 image: <registry>/my-exporter:v1.0.0
 ports:
 - name: metrics
 containerPort: 8080
 livenessProbe:
 httpGet:
 path: /healthz
 port: 8080
 initialDelaySeconds: 5
 periodSeconds: 10
 resources:
 requests:
 cpu: 50m
 memory: 32Mi
 limits:
 cpu: 100m
 memory: 64Mi

The Service names the port metrics, which the ServiceMonitor in the next section will reference by that name:

apiVersion: v1
kind: Service
metadata:
 name: my-exporter
 namespace: monitoring
 labels:
 app.kubernetes.io/name: my-exporter
spec:
 selector:
 app.kubernetes.io/name: my-exporter
 ports:
 - name: metrics
 port: 8080
 targetPort: metrics

Apply both:

kubectl apply -f deployment.yaml -f service.yaml

Telling Prometheus where to look

How you configure scraping depends on how Prometheus was installed.

Option 1: Prometheus Operator (ServiceMonitor)

If you installed Prometheus using the Prometheus Operator or the kube-prometheus-stack Helm chart, the operator must be running in your cluster before you create a ServiceMonitor. The release label must match the label selector configured on your Prometheus resource - kube-prometheus-stack is the default for a standard Helm install:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
 name: my-exporter
 namespace: monitoring
 labels:
 release: kube-prometheus-stack
spec:
 selector:
 matchLabels:
 app.kubernetes.io/name: my-exporter
 endpoints:
 - port: metrics
 interval: 15s
 path: /metrics

Option 2: Annotation-based discovery

If your Prometheus uses annotation-based pod discovery instead, you will need a matching scrape_config rule in your Prometheus configuration - check with whoever manages your Prometheus installation to confirm it is in place.

You can add the following two annotations to the Pod template regardless of which scraping method you use. They are ignored by the Prometheus Operator but picked up automatically by annotation-based setups:

annotations:
 prometheus.io/scrape: "true"
 prometheus.io/port: "8080" # omit if not using annotation-based discovery
 prometheus.io/path: "/metrics" # omit if not using annotation-based discovery

If you are unsure which setup your cluster uses, the ServiceMonitor approach is more explicit and easier to debug.

Verifying the scrape

Port-forward to the Prometheus service and open the targets page to confirm the exporter has been discovered:

kubectl port-forward svc/prometheus-operated 9090 -n monitoring

Navigate to http://localhost:9090/targets. The my-exporter target should appear with state UP. If it shows DOWN, check that the ServiceMonitor's release label matches and that the pod is running:

kubectl get pods -n monitoring -l app.kubernetes.io/name=my-exporter
kubectl describe servicemonitor my-exporter -n monitoring

Once the target is healthy, run a quick query in the expression browser to confirm data is flowing:

rate(worker_jobs_processed_total{status="success"}[2m])

A non-zero result here means the full pipeline is working: your application is producing data, Prometheus is scraping it, and the time-series are stored and queryable.

What comes next

A working exporter is the foundation, not the destination. The natural next step is surfacing these metrics to the HorizontalPodAutoscaler so that your workload scales on the signals that actually drive load, not just CPU. That requires a metrics adapter - the Prometheus Adapter is the most widely deployed option - which registers your custom metrics with the Kubernetes Custom Metrics API. Once registered, any HorizontalPodAutoscaler in the cluster can reference worker_queue_depth or worker_jobs_processed_total directly in its metrics block.

For a walkthrough of that setup, see Autoscaling on multiple metrics and custom metrics. For a catalog of ready-made exporters covering databases, message brokers, and cloud services, the Prometheus exporters and integrations page is a good starting point.

14 Jul 2026 6:00pm GMT

Kubernetes has quietly become the default platform for AI and machine learning. Whether you run notebook servers for data scientists, schedule distributed training jobs, tune hyperparameters, or orchestrate multi-step ML pipelines, those workloads increasingly land on a Kubernetes cluster. Kubeflow is one of the most popular ways to assemble that stack, and it does so the Kubernetes-native way: every capability is exposed as a Custom Resource Definition (CRD).

That design is a gift to cluster operators, because it means ML workloads can be observed and managed with the same primitives as everything else in the cluster. But in practice the specialized ML dashboards that ship with these platforms hide the Kubernetes layer underneath. When a notebook is stuck or a training run fails, the operator is often left dropping back to kubectl to find out what actually happened at the Pod level.

This post introduces the Headlamp Kubeflow plugin, which closes that gap by surfacing Kubeflow's custom resources directly inside a general-purpose Kubernetes UI. It is a worked example of a pattern any CRD-heavy platform can follow: meet operators where they already work, and show them the cluster-level truth.

Headlamp itself is an extensible Kubernetes web UI maintained under Kubernetes SIG UI and licensed under Apache 2.0. It runs as a desktop app or in-cluster, and its plugin system lets anyone add first-class views for custom resources.

Why operators need a different view

Purpose-built ML dashboards help data scientists submit experiments, pipelines, and notebooks. Cluster operators and site reliability engineers (SREs) troubleshoot the Kubernetes resources underneath, and they ask different questions:

• Why is a notebook stuck? Is it ImagePullBackOff, OOMKilled, or a Pod waiting on a PersistentVolumeClaim?
• Which Run resources failed recently across namespaces?
• Which parameter set does a Katib Experiment report as optimal?
• Do TrainJob resources reference the expected TrainingRuntime resources?
• Which batch workloads are running, and what state does Kubernetes report?

The Headlamp Kubeflow plugin helps answer these questions by reading directly from the Kubernetes API server. It shows Pod conditions, Kubernetes failure reasons, and resources across namespaces without requiring an intermediary ML service or database.

What the plugin covers

Kubeflow is modular, and teams often install only the components they need. The plugin discovers the Kubeflow API groups on a cluster and displays only the corresponding sections.

The plugin supports the following component families and API resources:

What you can see

Inspect notebook Pods

The Notebook detail view shows Pod conditions and their reason and message fields. It also shows CPU, memory, and GPU requests and limits; volume mounts and their backing types, such as PersistentVolumeClaim, ConfigMap, Secret, or emptyDir; environment variables that reference Secret or ConfigMap objects; sidecar containers; and node tolerations. This view consolidates information that would otherwise require several kubectl describe commands.

Inspect hyperparameter tuning

The Katib views show the tuning algorithm, search space, every Trial with its live status, and the current best Trial with its metric values and parameter assignments. They also show the early-stopping configuration and the number of Trial resources that stopped early, so you can follow the search without leaving the cluster UI.

Inspect pipeline state without the backend database

The Pipelines views read Kubernetes API resources directly and do not query the Kubeflow Pipelines API service or backend database. You can inspect stored pipeline state even when that service is unavailable. The Pipeline detail view compares the latest and previous PipelineVersion specifications in a side-by-side YAML diff. Run views show state and duration, RecurringRun views show human-readable schedules, and the artifacts view aggregates pipelineRoot values from recent Run resources.

Map ML resources

The plugin registers a Headlamp map source that renders Notebook, Profile, PodDefault, Experiment, Pipeline, SparkApplication, and TrainJob resources as graph nodes. It draws edges between supported resources based on .metadata.ownerReferences. Headlamp also shows inline summaries for these resource types when you hover over them.

Try it

The Kubeflow plugin README explains installation and local-cluster setup, including a lightweight CRD-only path for evaluation. Because the plugin discovers installed API groups, you can use it with an existing modular Kubeflow installation or create an evaluation cluster with only the CRDs and sample resources.

Apply the pattern to other platforms

Kubeflow illustrates a broader pattern. Platforms often model domain-specific workflows with custom resources. Their dashboards focus on those workflows, while Kubernetes operators also need the state of the underlying API resources and Pods. A CRD-driven plugin in a general Kubernetes UI can expose that state without making operators switch between unrelated tools.

The plugin uses the Apache 2.0 license and is developed under Kubernetes SIG UI. To report a problem or contribute an improvement, use the Headlamp plugins repository's issue tracker or pull requests.

13 Jul 2026 8:00pm GMT

1. Before you start: know what is changing

Kubernetes Dashboard and Headlamp both show what is running in a cluster, but they work differently. When Headlamp runs on the desktop, it uses your existing kubeconfig to connect to one or more clusters and can be extended with plugins. When Headlamp runs inside a cluster, it uses a Kubernetes ServiceAccount to access the API and follow RBAC rules. Kubernetes Dashboard, in contrast, only runs in-cluster and always relies on service account tokens. Understanding these models early helps you choose the right setup and permissions.

1.1 How Kubernetes Dashboard works

Dashboard is a web app that runs inside your cluster.

• You install it in the cluster, often with Helm.
• You usually run one Dashboard per cluster.
• You often reach it with kubectl port-forward or an ingress.
• You log in with a Bearer token. That token is often from a service account.
• It includes forms that help you create resources.
• It leans on tables and lists for navigation.

It feels like this: a UI that lives with the cluster.

1.2 How Headlamp works

Headlamp acts more like a Kubernetes client with a UI.

• It can run on your desktop or in a cluster.
• It reads your kubeconfig, like kubectl does.
• It can show more than one cluster in one place.
• It favors YAML when you create or change resources.
• It includes list views and a visual map.
• You can add features with plugins.

Headlamp is a UI that follows your identity, not your cluster.

1.3 What stays the same

Many workflows will feel familiar:

• Browse workloads and resources
• Filter by namespace
• Inspect YAML, events, and status
• View logs
• Take actions your RBAC allows

1.4 What changes

A few things will feel different:

• Login shifts from pasted tokens to kubeconfig (and sometimes SSO).
• Creation shifts from forms to "apply YAML."
• Multi-cluster becomes normal, not a special case.
• The map view helps you see how resources connect.

2. Pre-migration checklist

This checklist helps you avoid surprises during the switch. It makes sure Headlamp can use the same identity and permissions you already trust in Kubernetes. It also gives you a quick way to prove the migration worked before you turn off Dashboard.

2.1 Write down what you use today

List the basics:

• Which clusters you use (dev, staging, prod)
• Which namespaces you touch most
• What you do most often (view, edit, scale, delete, debug)
• How you access Dashboard today (port-forward or ingress)
• How you log in (service account token, and which RBAC bindings)

This is your baseline.

2.2 Check that kubeconfig works

Headlamp uses kubeconfig, especially on desktop. Make sure yours works before you install anything.

Run:

kubectl config current-context

Then try:

kubectl get nodes

If you cannot list nodes, test in a namespace you can access:

kubectl get pods -n <namespace>

If these work, Headlamp can use the same identity and RBAC.

2.3 Pick a rollout plan

There is no need to rush. Most teams choose one of these:

Parallel rollout (recommended)

• Install Headlamp
• Let people try it
• Keep Dashboard for a short time
• Remove Dashboard after the team is ready

Cutover

• Install Headlamp
• Switch docs and links
• Remove Dashboard soon after

Parallel rollout is safer for shared clusters.

2.4 Decide where Headlamp will run

You can use either option. Many teams use both.

Desktop

• Uses your kubeconfig
• Uses no cluster resources
• No port-forward needed
• Multi-cluster works out of the box

In-cluster

• Works well for shared, browser access
• Can be managed like other cluster apps
• Often paired with ingress and SSO

2.5 Note optional dependencies

These are common. You can handle them later.

• metrics-server (for CPU and memory graphs)
• ingress (for an in-cluster URL)
• OIDC / SSO (for browser sign-in)
• cleanup of old Dashboard service accounts and RBAC

3. Choose where Headlamp will run (desktop or in-cluster)

Headlamp can run on your desktop or inside a cluster. Both work well, but they fit different needs. Desktop is the fastest way to start because it uses your kubeconfig and does not run in the cluster. In-cluster is best when you need a shared URL and want the platform team to manage upgrades and access.

Option A: Desktop (user-managed)

Desktop Headlamp runs on each user's machine. It reads the same kubeconfig you use with kubectl. This keeps access tied to each user's identity and RBAC.

Why teams pick it

• No in-cluster service to deploy or expose.
• It uses no cluster CPU or memory.
• It uses your kubeconfig and RBAC.
• It works with many clusters in one app.
• You do not need port-forward for day-to-day use.

Option B: In-cluster (best for shared access)

In-cluster Headlamp is installed as a Kubernetes workload (often via Helm). This lets cluster admins manage it like other in-cluster apps.

• Cluster admins manage install, upgrades, and configuration through the Helm chart and standard Kubernetes tooling.
• Admins control ingress and can set up OIDC login for shared access.
• It supports shared use in team environments.

4. Install Headlamp (desktop and in-cluster)

This section gets Headlamp running. Follow the path you chose in Section 3.

4.1 Desktop install (fastest way to start)

Install Headlamp on your machine. Then open it like any other app. Headlamp reads your kubeconfig and uses the same identity and RBAC rules as kubectl.

Windows

Install with WinGet:

winget install headlamp

Or with Chocolatey:

choco install headlamp

macOS

Install with Homebrew:

brew install --cask headlamp

Linux

Install with Flatpak (Flathub):

flatpak install flathub io.kinvolk.Headlamp

Quick check

1. Launch Headlamp.
2. Confirm you can see a cluster context.
3. Open a namespace you can access and confirm you can list workloads. Headlamp will only show actions your RBAC allows.

4.2 In-cluster install (shared access)

Use this path when you want a shared UI that the platform team can manage. Headlamp supports in-cluster deployment with Helm or a YAML manifest.

Install with Helm

Add the repo and update:

helm repo add headlamp https://kubernetes-sigs.github.io/headlamp/
helm repo update

Create a namespace (example):

kubectl create namespace headlamp

Install the chart:

helm install headlamp headlamp/headlamp --namespace headlamp

Install with a YAML manifest (optional)

Headlamp also provides a YAML manifest you can apply and then adjust to your needs.

Check the install

Confirm the pod is running:

kubectl get pods -n headlamp

Confirm the service exists:

kubectl get svc -n headlamp

Access it (two common ways)

Quick test with port-forward

This is the fastest way to verify the service works:

kubectl port-forward -n headlamp svc/headlamp 8080:80

Then open: http://localhost:8080

Shared access with ingress

If you want a stable URL, expose the service through your ingress controller. Your exact ingress YAML depends on your setup. Headlamp's OIDC callback URL is your public URL plus /oidc-callback, so ingress and TLS settings matter.

4.3 Updating Headlamp

Updates depend on how you installed Headlamp. Package managers upgrade in place. DMG or EXE installs update by reinstalling the newer download.

macOS

If you installed with Homebrew, run:

brew upgrade headlamp

If you installed from a DMG, download the newest DMG and drag Headlamp into /Applications, replacing the old version. DMG installs do not auto upgrade.

Windows

If you installed with WinGet, run:

winget upgrade headlamp

If you installed with Chocolatey, run:

choco upgrade headlamp

If you installed from the EXE, download the newest installer and run it again. EXE installs do not auto upgrade.

Linux

If you installed with Flatpak, run:

flatpak update io.kinvolk.Headlamp

If you installed with AppImage, download the newest AppImage and run that file instead.

If you installed with a tarball, download the newest tarball, extract it, and run the new headlamp binary.

4.4 Notes for in-cluster access (keep it safe)

Treat an in-cluster UI like any other cluster-facing service. Use TLS, lock down who can reach it, and rely on Kubernetes auth and RBAC to control what users can do.

5. Authentication and RBAC

Headlamp uses the Kubernetes API the same way kubectl does. Your cluster still decides who can do what. Headlamp only shows actions your identity is allowed to take.

This section covers two setups: desktop and in-cluster.

5.1 Desktop: use kubeconfig

On desktop, Headlamp reads your kubeconfig and uses the same credentials you use with kubectl. There is no separate token login flow to manage.

Step 1: Confirm your kubeconfig works

Run:

kubectl config current-context

Then test access:

kubectl get nodes

If you cannot list nodes, test a namespace you can access:

kubectl get pods -n <namespace>

If these commands work, your kubeconfig and credentials are valid for Headlamp too.

Step 2: Point Headlamp at the right kubeconfig (if needed)

Headlamp can use the default kubeconfig path. It can also use a custom file path. You can set KUBECONFIG to choose a specific file.

Example:

KUBECONFIG=/path/to/config headlamp

You can also use more than one kubeconfig file at once. On Unix systems, separate paths with :. On Windows, separate paths with ;.

What to expect in the UI

Headlamp adapts to your RBAC permissions. If you do not have permission to edit or delete a resource, Headlamp will not offer those actions.

5.2 In-cluster: shared access needs a sign-in plan

In-cluster Headlamp is shared by many users. You need a clear plan for sign-in and access. Headlamp supports OpenID Connect (OIDC) for a "Sign in" flow.

You will usually choose one of these patterns:

• A. Configure Headlamp with OIDC (built-in).
• B. Put an auth layer in front of Headlamp (common in enterprises).

A. Built-in OIDC (Headlamp)

To use OIDC, Headlamp needs:

• Client ID
• Client secret
• Issuer URL
• (Optional) scopes

Your OIDC provider must also allow Headlamp's callback URL. The callback is your Headlamp URL plus:

• /oidc-callback

Example:

• https://headlamp.example.com/oidc-callback

Ingress note

If Headlamp is behind an ingress or load balancer, make sure it forwards X-Forwarded-Proto. If it does not, Headlamp may generate an http callback URL instead of https. That can break login.

B. Auth layer in front of Headlamp

Some teams protect Headlamp with an identity-aware proxy or a platform auth system. This keeps sign-in consistent across tools. Headlamp docs include an example using OpenUnison, which can deploy Headlamp with hardened defaults and integrate with identity providers.

5.3 RBAC: keep it least privilege

Kubernetes security starts with API authentication and authorization (RBAC). Headlamp respects those rules.

Practical guidance:

• Start with the lowest permissions that still let users do their job.
• If Dashboard used a high-privilege service account token, plan to remove or tighten that access after the move.
• For in-cluster, treat the UI like any other endpoint. Use TLS and limit network access.

5.4 Quick troubleshooting

Desktop: "I do not see my cluster"

Your kubeconfig may not be in the default location. Point Headlamp to the file with KUBECONFIG or a file path.

In-cluster: "OIDC login fails after redirect"

Confirm your provider allows https://YOUR_URL/oidc-callback. If you use ingress, make sure it forwards X-Forwarded-Proto.

6. Manage multiple clusters

Kubernetes Dashboard is usually tied to one cluster at a time. Headlamp is built for multi-cluster work. It is a client that follows your kubeconfig, not a single cluster install. That means you can keep one UI open and switch clusters as you work.

Clusters come from your kubeconfig

Headlamp reads clusters from your kubeconfig files. That means the clusters you can access with kubectl can also show up in Headlamp.

Switch clusters in the UI

Once Headlamp loads your kubeconfig, you can switch clusters using the cluster selector. This makes it easier to move between dev, staging, and prod without changing tools.

Optional: use more than one kubeconfig file

If you keep separate kubeconfig files, you can load them together. Headlamp supports multiple kubeconfig paths in KUBECONFIG.

Unix/macOS/Linux (: separator):

KUBECONFIG=~/.kube/dev:~/.kube/prod headlamp

Windows (; separator):

$env:KUBECONFIG="$HOME\.kube\dev;$HOME\.kube\prod"

Optional: add a cluster from inside Headlamp

You can also add clusters by loading additional kubeconfig files from the UI.

Permissions stay the same

Multi-cluster does not change security rules. Each cluster still enforces its own RBAC. Headlamp shows only what your identity can do in the selected cluster.

7. Navigate and understand resources

If you used Kubernetes Dashboard, this part will feel familiar. Headlamp keeps the same core resource views, but makes it easier to move around and understand what is connected.

Find resources in familiar places

Headlamp groups resources in a way that maps closely to Dashboard:

• Workloads for Pods, Deployments, StatefulSets, and Jobs
• Network for Services and Ingress
• Storage for PersistentVolumes and Claims
• Configuration for ConfigMaps and Secrets
• Nodes for cluster infrastructure

You can filter by namespace at the top of the UI, just like in Dashboard.

Inspect and edit resources

From any list, you can click into a resource to see details:

• Status and conditions
• Events
• Labels and annotations
• The full YAML definition

If your RBAC allows it, you can edit YAML directly from the UI. If it does not, Headlamp shows the resource as read-only. This matches how kubectl behaves.

Use search and filters to move faster

Headlamp adds faster search and filtering across lists. This helps when clusters or namespaces get large. You can narrow views without jumping between pages.

Understand relationships with Map View

Dashboard mostly shows resources as lists. Headlamp also includes a Map View.

Map View shows how resources relate to each other:

• Deployments
• ReplicaSets
• Pods
• Services

This helps when you are troubleshooting. Instead of clicking through several pages, you can see the connections at once. You can spot missing links or broken relationships faster.

When to use lists vs Map View

• Use lists when you know what resource you are looking for.
• Use Map View when you are trying to understand why something is not working.

Both views work on the same data. You are just choosing how much context you want at that moment.

8. Deploy applications with YAML

This is the biggest change for most Kubernetes Dashboard users. Dashboard relied on forms. Headlamp relies on manifests. The goal is not to slow you down. It is to align the UI with how Kubernetes is usually run in practice.

From forms to manifests

In Kubernetes Dashboard, you often deployed an app by filling in a form:

• container image
• replicas
• service type

Headlamp does not include the same wizard. Instead, it lets you apply YAML directly from the UI.

This matches how most teams deploy today:

• manifests live in Git
• CI/CD applies them
• Helm or GitOps tools manage changes

Headlamp fits into that flow rather than replacing it.

Create resources using YAML

To deploy an application in Headlamp:

1. Select a cluster and namespace.
2. Click Create.
3. Paste or upload a YAML manifest.
4. Review it.
5. Click Apply.

The resource appears immediately in the UI.

If the manifest is not valid, Headlamp shows the same errors you would see from the Kubernetes API.

Generate YAML the easy way

If you miss the Dashboard wizard, you can still generate YAML quickly.

For example:

kubectl create deployment nginx \
 --image=nginx \
 --dry-run=client \
 -o yaml > nginx.yaml

You can edit the file if needed, then paste it into Headlamp and apply it.

This gives you a repeatable manifest instead of an object created only through a UI.

What if you use Helm or GitOps?

That works well with Headlamp.

• Install with Helm as usual.
• Deploy with GitOps pipelines as usual.
• Use Headlamp to view, inspect, and debug what is running.

Headlamp does not replace those tools. It gives you visibility into what they create.

What to expect compared to Dashboard

• You will not see a multi-step deploy form.
• You will work more with YAML.
• You gain clarity about what is actually applied to the cluster.
• The same manifest can be reused in CI, Git, or other tools.

9. Deploy and debug workloads

One of the main reasons people used Kubernetes Dashboard was day-to-day debugging. Headlamp covers the same tasks and adds a few useful upgrades.

View logs

You can view pod logs directly in the UI.

To check logs:

1. Open Workloads.
2. Select Pods.
3. Click a pod.
4. Open the Logs tab.

If the pod has more than one container, you can switch between containers. Logs stream live, which helps during rollouts or active incidents.

Exec into running pods

Headlamp also lets you open a shell inside a container.

From a pod view:

• Open the pod actions menu.
• Choose Terminal or Exec.

This opens an interactive session inside the container. It replaces the need to switch back to the terminal for quick checks.

This action follows RBAC rules. If you cannot run kubectl exec, Headlamp will not allow it either.

Check metrics and resource usage

Headlamp can show CPU and memory usage for pods and nodes. This works the same way it did in Dashboard.

A few things to know:

• Metrics require metrics-server to be installed in the cluster.
• If metrics are missing, Headlamp shows a clear notice.
• Once metrics are available, usage appears on pod and node views.

This makes it easy to answer simple questions:

• Is this pod using too much memory?
• Is a node under pressure?

View events when something goes wrong

Events are often the fastest way to understand failures.

In Headlamp, you can:

• View events on resource detail pages.
• See warnings and errors tied to pods, nodes, or deployments.

This is often the first place to look when a workload is stuck or crashes.

How this compares to Dashboard

What stays the same:

• Log viewing
• Event inspection
• RBAC-aware actions

What improves:

• Built-in exec sessions
• Clearer layout and filtering
• Fewer context switches between UI and CLI

10. Remove Kubernetes Dashboard

After Headlamp is working and your team is comfortable using it, you can remove Kubernetes Dashboard. This is the final cleanup step.

Removing Dashboard reduces clutter and avoids keeping unused access paths around.

Confirm Headlamp covers your needs

Before uninstalling anything, make sure:

• Users can access the clusters they need in Headlamp.
• Common tasks work:
  • browse resources
  • deploy with YAML
  • view logs and events
  • exec into pods (if allowed)
• RBAC behaves as expected for different roles.

Once these checks pass, you are ready to remove Dashboard.

Uninstall the Dashboard

If you installed Kubernetes Dashboard with Helm, remove it with:

helm uninstall kubernetes-dashboard -n kubernetes-dashboard

If Dashboard was installed by a manifest or addon, remove it using the same method you used to install it.

After removal, confirm the resources are gone:

kubectl get pods -n kubernetes-dashboard

Clean up access artifacts (recommended)

Many Dashboard setups used dedicated service accounts and cluster-wide roles.

Review and remove anything that was created only for Dashboard access, such as:

• service accounts
• role bindings or cluster role bindings
• old documentation that points users to Dashboard URLs or port-forward commands

This reduces long-lived credentials and unused permissions.

Communicate the change

Make sure your team knows:

• Headlamp is now the primary Kubernetes UI.
• How to access it (desktop or URL).
• Where to go for help if something feels different.

11. Post-migration checklist

This final checklist helps you confirm the migration is complete. It gives you confidence that Headlamp is working as expected and that nothing important was left behind.

Access and visibility

• Headlamp opens without errors.
• Users can access the correct clusters.
• Namespace filtering works as expected.
• Multi-cluster switching behaves correctly.

Authentication and RBAC

• Desktop users access clusters using kubeconfig.
• In-cluster users can sign in using the chosen auth method.
• Users only see actions their RBAC allows.
• No unexpected permission errors appear during normal use.

Core workflows

• Resources load under Workloads, Network, and Configuration.
• YAML can be viewed and edited where permissions allow.
• Applications can be deployed using Create and YAML.
• Logs load correctly for running pods.
• Exec works for users who are allowed to use it.
• Metrics appear if metrics-server is installed.

Operational confidence

• Teams can troubleshoot without switching tools.
• Map View helps explain relationships during debugging.
• Platform or DevOps teams know how Headlamp is installed and managed.

Cleanup confirmation

• Kubernetes Dashboard is no longer running.
• Dashboard-only service accounts and RBAC bindings are removed.
• Internal docs no longer reference Dashboard URLs or port-forward commands.

Team alignment

• The team knows Headlamp is the default Kubernetes UI.
• Onboarding docs point new users to Headlamp.
• There is a clear path for feedback or questions.

You've now completed the move from Kubernetes Dashboard to Headlamp. Your team can use the same Kubernetes access model, work across clusters, and rely on workflows that match how Kubernetes is used today. From here, Headlamp becomes your default UI, whether on the desktop or in shared environments. As your needs grow, you can keep using it as-is or extend it with plugins and new views over time.

If you want to help shape what comes next, join the Headlamp community and contribute at headlamp.dev.

13 Jul 2026 6:00pm GMT