Prometheus Helm Chart: A Production-Ready Guide

You’ve probably seen this pattern already. The team installs Prometheus with Helm, gets a green deployment, opens the UI, and assumes observability is handled. A few weeks later, targets are missing, retention is too short, storage is full, alerts are noisy, and nobody trusts the dashboards during an incident.

That’s the difference between a working install and a production-ready one.

The prometheus helm chart is mature, widely adopted, and flexible enough for serious Kubernetes environments. The problem isn’t Helm. The problem is treating the default chart values like production guidance. They aren’t. Real environments need opinionated choices around chart selection, service discovery, storage, security, upgrade safety, and cost control from the first commit.

Choosing the Right Prometheus Helm Chart

A common production mistake happens before the first install command. Teams pick the first Prometheus chart they recognize, ship it, and only discover the trade-off later when onboarding new services, separating ownership between platform and application teams, or debugging why one workload is scraped and another is invisible.

For Kubernetes platforms, chart choice determines the operating model as much as the initial deployment.

The standalone prometheus chart from prometheus-community is mature and widely used. The Artifact Hub package page shows current chart details, version history, and compatibility information. That chart still has a place. I recommend it for narrow use cases such as a single-purpose Prometheus deployment, lab environments, or teams that intentionally want to manage scrape configuration without the Prometheus Operator.

For day-one production readiness, kube-prometheus-stack is usually the better starting point.

Prometheus vs kube-prometheus-stack chart comparison

Why I default to kube-prometheus-stack

The stack gives you a coherent monitoring baseline: Prometheus, Alertmanager, Grafana, exporters, and the Operator-managed CRDs that let teams declare scrape targets and alert rules in Kubernetes-native objects. That matters once more than one team touches observability.

In client environments, the standalone chart usually breaks down in familiar ways. Someone edits scrape jobs by hand. Another team adds a service and assumes it will be discovered automatically. Alert rules drift between clusters. Upgrades become tense because local chart changes are hard to reason about. None of that shows up on day one. It shows up during growth, incidents, and ownership changes.

kube-prometheus-stack handles those situations better because it standardizes how monitoring is extended.

Practical rule: If the cluster supports customer-facing workloads, shared services, or an on-call rotation, start with kube-prometheus-stack unless you have a documented reason not to.

The real difference is operational ownership

The standalone chart encourages direct Prometheus administration. Teams install Prometheus, then keep adding configuration around it. That can work, but it tends to concentrate monitoring knowledge in a small group of engineers.

kube-prometheus-stack supports a cleaner split. Platform engineers maintain the base stack, guardrails, and upgrade path. Application teams add ServiceMonitor, PodMonitor, and PrometheusRule resources for their own services. That model scales better in multi-namespace clusters and reduces the chance of one team breaking scrape coverage for another.

There is a trade-off. The stack introduces CRDs and the Prometheus Operator, which means more moving parts and more care during upgrades. That is still the right trade for production in most cases because it gives you consistency, delegation, and a much safer way to grow monitoring over time.

Choose the standalone chart only if you want that lower-level control and are prepared to own the extra configuration burden. Otherwise, choose kube-prometheus-stack and build on the model that matches how Kubernetes platforms are run.

Helm Installation and Essential Configuration

A production Prometheus rollout usually fails long before the first outage. The warning signs show up in the install itself. The chart goes in with defaults, every component stays enabled, retention is guessed, storage is left vague, and nobody checks what the stack will cost once scrape volume grows. I see this pattern in client environments all the time.

The fix starts with discipline. Use a dedicated namespace, keep the release name stable, and install from a reviewed values.yaml instead of CLI flags copied from a README. That one habit makes upgrades, diffs, and incident response much cleaner.

1. Add the repository

   Copy

   helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
   helm repo update
   

2. Create the namespace

   Copy

   kubectl create namespace monitoring
   

3. Install the stack with your own values

   Copy

   helm install monitoring prometheus-community/kube-prometheus-stack \
     --namespace monitoring \
     -f values.yaml
   

For production, the first values.yaml should already express intent. It does not need to cover every future requirement, but it should define what stays on, what stays off, how long data is kept, and where Prometheus stores it.

grafana:
  enabled: true

alertmanager:
  enabled: true

prometheus:
  enabled: true
  prometheusSpec:
    retention: 15d
    resources:
      requests:
        cpu: "1"
        memory: 4Gi
      limits:
        cpu: "2"
        memory: 8Gi
    storageSpec:
      volumeClaimTemplate:
        spec:
          storageClassName: gp3
          accessModes:
            - ReadWriteOnce
          resources:
            requests:
              storage: 50Gi

prometheus-node-exporter:
  enabled: true

kube-state-metrics:
  enabled: true

prometheus-pushgateway:
  enabled: false

These choices are deliberate.

Keep kube-state-metrics enabled unless you have a documented replacement. Without it, teams lose visibility into deployments, replica counts, pod phase changes, and other cluster state that drives useful alerts. That gap often stays hidden until someone asks why Prometheus never warned about a rollout stuck at zero ready pods.

Disable Pushgateway by default. It fits batch jobs and a small set of push-based patterns, but plenty of teams leave it running with no owner and no clear use case. That adds one more component to patch, monitor, and explain during audits. If you need it later for short-lived jobs or synthetic probing, add it intentionally. For external endpoint checks, pair your stack with a Prometheus Blackbox Exporter setup for probe-based monitoring instead of forcing everything through Pushgateway.

Resource sizing needs the same level of intent. Defaults help the chart install successfully. They do not tell you whether Prometheus can survive a noisy cluster, high-cardinality labels, or a wave of new teams adding exporters. In shared environments, memory pressure usually shows up first. Prometheus can limp along with moderate CPU saturation. It behaves much worse once the working set no longer fits cleanly in memory.

A practical starting point looks like this:

• Small cluster with predictable workloads
  Start with moderate requests, then review series count, scrape duration, WAL growth, and query latency after a week of real traffic.

• Shared platform with many namespaces
  Increase memory early and set retention conservatively. Long retention on an undersized PVC is one of the fastest ways to create noisy pages and emergency resizing work.

• High-churn environments
  Budget for more headroom than pod count suggests. Frequent deploys, short-lived workloads, and unbounded labels hurt Prometheus faster than many teams expect.

Storage deserves the same scrutiny. Leaving persistence for later is a mistake. A restart without durable storage wipes history, disrupts alert evaluation, and turns a monitoring incident into an investigation with no timeline. Set a storageClassName, request the PVC size explicitly, and confirm that your class delivers the IOPS profile Prometheus needs. Cheap storage can become expensive once queries stall and on-call engineers start debugging the platform instead of the application.

Keep the file readable as the stack grows. Group settings by operational concern so reviewers can understand changes quickly:

• Core components
  Prometheus, Alertmanager, Grafana, exporters

• Storage
  PVCs, retention, storage class

• Scheduling
  node selectors, tolerations, affinity

• Exposure
  ingress, service types, authentication

• Rules and discovery
  default rules, selectors, namespace scope

That structure holds up under GitOps, where a bad diff review can introduce far more risk than the original install.

Mastering Service Discovery with ServiceMonitors

Prometheus can be healthy while your application metrics are missing. I see this a lot in client clusters. The stack comes up, the Kubernetes targets look green, and everyone assumes scraping is working end to end. Meanwhile, business services are invisible because discovery rules never matched the right objects.

In production, that turns into a quiet failure. Dashboards stay half-empty, alerts never fire for the workloads that matter, and teams trust a monitoring system that is only observing itself.

A big part of kube-prometheus-stack's value is the Operator CRDs, especially ServiceMonitor and PrometheusRule. They let you keep discovery logic in Kubernetes manifests instead of hand-editing scrape jobs. That model is cleaner under GitOps, but it has a sharp edge. Label selection has to be exact, and Prometheus will not warn you in a helpful way when your selectors miss.

Understand the label chain

A ServiceMonitor targets a Kubernetes Service. That Service then routes to Pods through its selector. If any link in that chain is wrong, the target never appears.

Check these three layers together:

1. Pod labels
2. Service selector labels
3. ServiceMonitor matchLabels

Start there first. Do not start by editing Prometheus settings or reinstalling the chart.

Another production detail matters here. Many teams create the ServiceMonitor and forget that the Prometheus instance also has its own selector rules for which monitors it will load. If your chart is configured to look only for monitors with a specific label, a perfectly valid ServiceMonitor can still be ignored.

Example for a custom application

Assume your app exposes /metrics on a port named metrics.

Application Service

apiVersion: v1
kind: Service
metadata:
  name: payments-api
  namespace: payments
  labels:
    app.kubernetes.io/name: payments-api
spec:
  selector:
    app.kubernetes.io/name: payments-api
  ports:
    - name: metrics
      port: 9090
      targetPort: 9090

ServiceMonitor

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: payments-api
  namespace: monitoring
  labels:
    release: monitoring
spec:
  namespaceSelector:
    matchNames:
      - payments
  selector:
    matchLabels:
      app.kubernetes.io/name: payments-api
  endpoints:
    - port: metrics
      path: /metrics
      interval: 30s

This works because every object agrees on the same label vocabulary. The ServiceMonitor matches the Service metadata label. The Service selects the Pods. The endpoint uses a named Service port, which is safer than relying on a container port number someone may change later during a release.

If you manage discovery through Helm values, keep the Prometheus selector behavior explicit instead of inheriting chart defaults you have not reviewed:

prometheus:
  prometheusSpec:
    serviceMonitorSelector:
      matchLabels:
        release: monitoring
    serviceMonitorNamespaceSelector: {}

That snippet makes two things obvious during review. Prometheus will only load ServiceMonitor objects labeled release: monitoring, and it is allowed to discover them across namespaces. In shared clusters, that is a useful pattern. In regulated environments, narrow the namespace selector instead of leaving it open.

Example for an exporter such as redis-exporter

Exporters usually fail for simpler reasons than applications do. The Service exists, but the port name in the ServiceMonitor does not match the port name exposed by the Service.

apiVersion: v1
kind: Service
metadata:
  name: redis-exporter
  namespace: data
  labels:
    app.kubernetes.io/name: redis-exporter
spec:
  selector:
    app.kubernetes.io/name: redis-exporter
  ports:
    - name: http-metrics
      port: 9121
      targetPort: 9121

---
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: redis-exporter
  namespace: monitoring
  labels:
    release: monitoring
spec:
  namespaceSelector:
    matchNames:
      - data
  selector:
    matchLabels:
      app.kubernetes.io/name: redis-exporter
  endpoints:
    - port: http-metrics
      interval: 30s
      path: /metrics

This is why I push teams to standardize port names early. metrics, http-metrics, and web used interchangeably across charts create pointless review mistakes and missed scrapes.

The checks that catch most silent failures

These checks solve the majority of discovery issues I see in real environments:

• Release label alignment
  The ServiceMonitor labels must match what prometheus.prometheusSpec.serviceMonitorSelector expects. If that selector looks for release: monitoring, every monitor you want scraped needs it.

• Service labels, not Deployment labels
  ServiceMonitor.selector.matchLabels evaluates Service metadata labels. It does not inspect Deployment labels directly. This trips up teams that copy Pod labels into the wrong place.

• Named ports must match exactly
  The endpoints.port field refers to the Service port name. metrics and http-metrics are different values, and Prometheus will not guess your intent.

• Cross-namespace scraping must be allowed
  If the app runs in payments and the monitor lives in monitoring, namespaceSelector has to include payments. Prometheus also needs permission to watch that namespace.

• Endpoints should be reviewed for scrape cost
  Short intervals on noisy exporters add up fast. A 15s interval across dozens of high-cardinality targets can create cost and performance issues long before anyone notices.

For blackbox checks against external URLs, ingresses, and synthetic probes, use a separate pattern instead of trying to force everything through application metrics. This Prometheus blackbox exporter guide is the approach we recommend when clients need uptime and reachability checks alongside standard scraping.

Service discovery also needs the same security discipline as the rest of the stack. Cross-namespace monitors, broad RBAC, and open metrics endpoints are easy to leave in place after a rushed rollout. If your platform team is tightening access between workloads, this practical Zero Trust security guide is a good reference point for setting policy without breaking observability.

Configuring Persistence Storage and Security

A production Prometheus that loses its TSDB on reschedule is hard to trust. The first real problem shows up during an incident, when the team needs yesterday’s baseline and discovers the server came back empty after a node drain or storage issue.

If metrics history matters for incident review, change analysis, or capacity planning, Prometheus needs persistent storage from the first deployment.

Default storage is rarely enough

The common failure mode is simple. Teams enable persistence, keep the default volume size, then assume retention settings will protect them. They do not. Retention only helps if the disk can hold the data for that period.

For client clusters, I usually start by sizing storage from expected ingest rate and retention target, then add headroom for WAL growth, compaction, and short-term spikes. A small dev cluster may be fine with a modest volume. Production usually is not. If you plan to keep longer history or push data into object storage later, design for that early instead of rebuilding under pressure. This case study on scaling monitoring with Thanos shows the pattern we use when local Prometheus retention stops fitting the operating model.

A practical baseline looks like this:

prometheus:
  prometheusSpec:
    retention: 15d
    storageSpec:
      volumeClaimTemplate:
        spec:
          storageClassName: gp3
          accessModes:
            - ReadWriteOnce
          resources:
            requests:
              storage: 50Gi

The storage class matters. Fast block storage is usually the right default for Prometheus because TSDB writes are constant and local disk latency shows up quickly in scrape health and query performance. Do not inherit the cluster default blindly. I have seen teams land Prometheus on a slow general-purpose class, then spend hours chasing “Prometheus is slow” complaints that were really storage problems.

Two mistakes show up often in reviews:

• Setting retention without checking actual disk growth Prometheus will happily fill the volume first.

• Using a storage class with expansion disabled The first storage alarm becomes a migration project instead of a simple resize.

Security belongs in the first deployment

Prometheus stores operationally sensitive data. Exporters and scrape targets often expose pod names, node details, internal endpoints, queue depth, error rates, and other signals an attacker would gladly collect for free.

The production approach is to narrow access in three places. Limit who can reach Prometheus. Limit where Prometheus can scrape. Limit which namespaces and labels your monitoring stack is allowed to discover. If those boundaries are vague at rollout, they usually stay vague until a security review forces cleanup.

A simple starting NetworkPolicy is better than none:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: prometheus-ingress
  namespace: monitoring
spec:
  podSelector:
    matchLabels:
      app.kubernetes.io/name: prometheus
  policyTypes:
    - Ingress
    - Egress
  ingress:
    - from:
        - namespaceSelector:
            matchLabels:
              kubernetes.io/metadata.name: monitoring
  egress:
    - to:
        - namespaceSelector: {}

Treat that policy as a starting point, not a finished design. Tighten ingress to known operators, dashboards, and admin paths. Tighten egress to the namespaces and ports that host approved scrape targets. If your team is standardizing broader cluster controls, this practical Zero Trust security guide is a useful reference for turning observability access into explicit policy instead of inherited trust.

Encryption matters too, but the implementation usually belongs at the storage layer and secret management layer, not as an afterthought in the chart. Use encrypted volumes where your platform supports them. Store remote write credentials and webhook secrets in Kubernetes Secrets managed by your existing secret workflow. Do not leave Alertmanager receivers, bearer tokens, or basic auth credentials scattered through ad hoc values files in Git.

What works in practice

Production Prometheus needs two assumptions from day one:

• Metrics history is part of the operational record Treat it as stateful data with planned capacity, backup expectations, and a recovery plan.

• Scrape access must be intentionally scoped Broad internal visibility creates risk, especially in multi-tenant clusters or regulated environments.

Teams that skip either one usually revisit the design after the first failed resize, surprise data loss, or security review. Building both in early is cheaper than repairing them later.

Sizing Resources High Availability and Cost

A production Prometheus rollout usually starts failing long before Kubernetes marks the Pod unhealthy. Scrapes begin to miss, dashboards slow down, rule evaluations drift, and the storage bill climbs faster than anyone expected. By the time the team notices, they are already debugging monitoring instead of using it.

That is why sizing the prometheus helm chart needs to be treated as an operating model, not a one-time Helm choice. Scrape interval, retention, label cardinality, query load, local disk, and remote write all pull on the same system. The defaults are good enough to get metrics on screen. They are rarely good enough to keep cost and reliability under control in production.

The first sizing mistake I see in client environments is over-scraping low-value targets. Node exporters, kube-state-metrics, app metrics, blackbox probes, and custom exporters all get pushed to the same interval because it feels safer. It usually is not. Fast scrape intervals increase ingest, WAL pressure, compaction work, and disk growth. They also hide a harder question. Which signals need minute-by-minute visibility during an incident?

Start with ingest discipline

Three settings drive most of the operational outcome.

Scrape interval controls how much data Prometheus has to ingest and store. Reserve short intervals for workloads where a few minutes of delay would materially slow incident response.

Retention decides how much data stays on local disk. Local retention is expensive if you keep stretching it to cover both recent triage and long-term reporting.

Cardinality is where otherwise healthy clusters get into trouble. Labels like pod, path, session, customer_id, or unbounded status fields can turn a small metrics set into a memory and storage problem quickly.

If you want one rule of thumb, use Prometheus for recent operational data and move longer history elsewhere. That keeps local queries fast and avoids turning every PVC expansion into a planning exercise. For teams standardizing on object storage and cross-cluster querying, this case study on scaling monitoring with Thanos shows the pattern we usually recommend once a single in-cluster Prometheus stops being enough.

Size for the workload you have, not the demo you copied

A small cluster with moderate scrape volume can run well on conservative resources. Shared platforms, heavy recording rules, and noisy exporters need more headroom than teams expect. Memory pressure usually shows up first. CPU follows during query bursts, compaction, or rule evaluation spikes.

This is a safer starting point than the thin defaults I still see copied into production:

prometheus:
  prometheusSpec:
    replicas: 2
    retention: 15d
    resources:
      requests:
        cpu: "2"
        memory: 8Gi
      limits:
        cpu: "4"
        memory: 16Gi
    podAntiAffinity: "hard"
    remoteWrite:
      - url: https://example-remote-write-endpoint

Use that as a baseline, then adjust from observed ingest rate, active series count, query concurrency, and rule load. Do not set tight memory limits just because the Pod looks quiet on day one. Prometheus often looks fine until cardinality jumps after a deployment, an exporter change, or a new team onboarding to the cluster.

A few practical calls from real environments:

• Keep local retention short enough to stay cheap and predictable Two weeks is a reasonable starting point for many teams. Extend it only if on-call workflows depend on longer local history.

• Tune scrape intervals by target class Critical control plane or latency-sensitive services can justify tighter intervals. Batch jobs, stable internal services, and low-change exporters usually cannot.

• Budget for rule evaluation and ad hoc queries The Prometheus server does more than scrape. Dashboards, alerts, recording rules, and incident-time queries all compete for the same CPU and memory.

High availability starts with scheduling

Running two replicas helps, but only if they can fail independently. I still find “HA” installs with both Pods on the same node group, the same zone, or the same storage failure domain. That setup survives a single container crash. It does not survive the infrastructure events that matter.

Use anti-affinity from the start. Spread Pods across nodes and, where possible, across zones. If the cluster autoscaler or your node pool layout makes that impossible, call it out early. Hidden placement constraints are a common reason HA plans fail the first real outage.

For many production teams, two replicas is the right first step. Three or more can make sense for larger shared platforms, but extra replicas also duplicate scrape load and increase cost. More replicas are not free resilience. They are a trade-off between availability, duplicate ingestion, and operational complexity.

The operational trade-offs are easier to visualize when you see them in motion:

What usually holds up in production

• Run two replicas if Prometheus is part of incident response Pair that with anti-affinity and zone-aware scheduling where the cluster supports it.

• Keep local Prometheus focused on recent troubleshooting Long-term retention belongs in a remote system built for it.

• Reduce cardinality before adding more hardware Bigger Pods hide bad metric design for a while. They do not fix it.

• Treat scrape intervals as a cost decision Every target scraped more often adds storage, CPU, and query pressure.

• Plan for growth before alert noise starts If one cluster is already serving multiple teams, assume read load and exporter count will keep increasing.

The cheapest Prometheus deployment is usually the one with fewer bad metrics, shorter local retention, and a clear remote storage plan. That is the pattern that avoids silent failures, runaway storage growth, and the alert storms that default installs create later.

Managing Upgrades and GitOps Integration

The Prometheus outage that hurts the most is the one you cause during a routine upgrade.

I see this in client environments more often than I should. A chart bump looks harmless, Argo CD or Flux syncs it, and suddenly ServiceMonitor selection changes, a CRD update behaves differently than expected, or Alertmanager config renders in a way nobody reviewed. The stack still exists, but scrape targets disappear or alerts fan out in the wrong direction. That is why Prometheus upgrades need the same change control as ingress, CNI, or storage classes.

Upgrade with a change-review mindset

A safe process is intentionally boring:

1. Update chart metadata locally

   Copy

   helm repo update
   

2. Read the release notes before touching production Focus on CRDs, selector behavior, renamed values, and default changes. Those are the changes that break monitoring without immediate detection.

3. Render and diff manifests Use helm diff in CI or locally. Do not rely on the chart version number to tell you the blast radius.

4. Apply the upgrade from the same values file tracked in Git

   Copy

   helm upgrade monitoring prometheus-community/kube-prometheus-stack \
     --namespace monitoring \
     -f values.yaml
   

The command is not the hard part. The hard part is refusing to treat monitoring as a side install that can drift outside review.

One pattern works well in production. Pin the chart version, promote it through environments, and keep rollback simple. If staging reveals a selector or CRD problem, production never sees it.

GitOps works best when platform and app ownership stay separate

The kube-prometheus-stack chart fits GitOps well, but only if the repo layout matches team boundaries. Do not put every ServiceMonitor, PrometheusRule, and platform setting in one directory owned by one team. That turns monitoring into a gatekeeping problem.

A practical layout looks like this:

platform/
  monitoring/
    kube-prometheus-stack/
      base/
        helmrelease.yaml
        values.yaml
      overlays/
        production/
        staging/

applications/
  payments/
    monitoring/
      servicemonitor.yaml
      prometheusrule.yaml
  checkout/
    monitoring/
      servicemonitor.yaml
      prometheusrule.yaml

This split keeps the base stack under platform control. That includes retention, storage class, ingress, Alertmanager routing, Grafana exposure, and chart upgrades. Application teams own service-level monitors and alert rules close to their code.

That division prevents a common failure mode. A bad app monitoring change should not require editing the chart release itself, and a platform upgrade should not force every application team into the same pull request.

Keep GitOps predictable during chart changes

A few habits reduce surprises:

• Pin chart versions Avoid floating tags or automatic bumps on every sync.

• Handle CRDs deliberately CRD changes deserve their own review. In regulated environments, I often separate CRD updates from the main chart rollout so rollback decisions stay clear.

• Standardize labels and selectors A mismatched release label or namespace selector can drop targets without an obvious failure event.

• Review rendered manifests in CI Look at what will be applied, not just the Helm values diff.

• Test alert paths after upgrades Rendering succeeds plenty of broken configurations. Fire a test alert and confirm routing still works.

GitOps gives you auditability and repeatability. It does not protect you from bad defaults or bad ownership boundaries. Those still need deliberate design.

For day-two operations, keep a vetted set of PromQL checks in the same workflow your team already uses. This short library of useful Prometheus queries for troubleshooting and validation helps catch missing targets, stalled scrapes, and rule regressions after a sync or chart upgrade.

Your Production Prometheus Checklist

A production-ready prometheus helm chart deployment is mostly about discipline. The tooling is already strong. The difference comes from how intentionally you use it.

Use this checklist before you call the deployment finished:

• Pick the right chart Start with kube-prometheus-stack unless you have a clear reason to stay minimal.

• Treat values.yaml like platform code Set resources, retention, component enablement, and storage from the start.

• Keep kube-state-metrics enabled You need Kubernetes state visibility, not just node metrics.

• Use ServiceMonitors carefully Validate Service labels, named ports, namespace selectors, and release labels.

• Persist data intentionally Don’t rely on ephemeral storage for incident analysis.

• Lock down access Add NetworkPolicies and align storage with your security baseline.

• Design for cost early Tune scrape intervals, reduce unnecessary exporters, and avoid accidental cardinality growth.

• Add HA before you need it Replicas only help when scheduling keeps them apart.

• Run upgrades through GitOps Diff changes, review manifests, and separate platform ownership from application onboarding.

For day-to-day operations after deployment, keep a short library of tested PromQL on hand. This collection of useful Prometheus queries is a good starting point for dashboards, alert triage, and sanity checks.

If your team wants help designing or hardening a production observability stack, CloudCops GmbH works with startups and enterprises to build Kubernetes platforms, GitOps workflows, and Prometheus-based monitoring that stays reliable under real production load.

Continue Reading

Read A Practical Guide to Kube State Metrics

Mar 26, 2026

A Practical Guide to Kube State Metrics

A complete guide to kube state metrics. Learn to install, configure, and use KSM data with Prometheus and Grafana to master Kubernetes observability.

kube state metrics

+4

C

CloudCops

Read Mastering Kubernetes Horizontal Pod Autoscaler

May 1, 2026

Mastering Kubernetes Horizontal Pod Autoscaler

Master the Kubernetes Horizontal Pod Autoscaler. Learn HPA configuration, tuning, Prometheus integration, and best practices for platform engineers.

horizontal pod autoscaler

+4

C

CloudCops

Read AWS CloudWatch vs CloudTrail: Deep Dive Comparison

Apr 12, 2026

AWS CloudWatch vs CloudTrail: Deep Dive Comparison

Compare AWS CloudWatch vs CloudTrail: Understand key differences, use cases, & pricing. Integrate for modern observability & GitOps pipelines.

aws cloudwatch vs cloudtrail

+4

C

CloudCops