Introduction
Helm charts made Kubernetes adoption easier — and at the same time, they introduced one of the most dangerous habits in modern DevOps: “install & forget.“
A chart installs successfully. Pods are running. Dashboards look green.
So teams move on.
Weeks or months later, problems start appearing:
- Unexpected restarts
- Performance degradation
- Security findings
- Exploding cloud bills
- Painful upgrades
The root cause is rarely Kubernetes itself.
It’s almost always default Helm values shipped for demos — not production.
This article explains why “install & forget” Helm charts silently hurt production systems, what usually goes wrong, and how to approach Helm responsibly.
Why Helm Defaults Are Not Production Defaults
Helm charts are designed to:
- Be generic
- Work across environments
- Install successfully with minimal configuration
They are not designed to reflect your production reality.
Most charts assume:
- Small scale
- Single tenant
- Minimal security constraints
- No strict cost controls
- Short-lived or test workloads
Using defaults in production is equivalent to running a database with:
- No resource limits
- No backups
- No authentication hardening
- No lifecycle planning
It “works” — until it doesn’t.
Common Problems Caused by Default Helm Values
1. Resource Requests and Limits Are Missing or Unsafe
Many charts either:
- Don’t define requests/limits at all, or
- Define extremely low defaults
This leads to:
- Pod eviction under pressure
- CPU throttling
- Noisy neighbor issues
- Unpredictable autoscaling behavior
Production impact:
Applications become unstable during load spikes — exactly when you need them most.
2. Security Is an Afterthought
Default Helm values often:
- Run containers as root
- Skip PodSecurityContext hardening
- Disable network policies
- Expose services unnecessarily
Why?
Because secure defaults break installs in some environments — so charts stay permissive.
Production impact:
Security teams find issues after deployment, and fixes become disruptive.
3. Persistence and Storage Are Poorly Planned
Many charts:
- Use default storage classes
- Don’t tune volume sizes
- Ignore backup and restore strategies
This is common with:
- Databases
- Message queues
- Stateful services
Production impact:
Storage costs grow silently, backups are missing, and recovery becomes manual and risky.
4. Logging and Metrics Are Over-Enabled
Charts frequently enable:
- Verbose logs
- Multiple metrics exporters
- Debug endpoints
This feels harmless — until you see the bill.
Production impact:
Cloud logging and monitoring costs increase month over month with no clear visibility.
5. Upgrades Become Nightmares
When you don’t fully understand or customize a chart:
- Version upgrades introduce breaking changes
- Deprecated values go unnoticed
- Stateful upgrades fail mid-way
Production impact:
Teams delay upgrades, increasing security and stability risks.
Why “It’s Working Fine” Is a Dangerous Signal
One of the biggest mistakes teams make is assuming:
“It’s running fine, so defaults must be okay.”
Most Helm-related problems:
- Appear under load
- Surface during incidents
- Explode during upgrades
- Show up in cloud bills, not dashboards
By the time you notice, fixing defaults becomes a migration project.
A Better Way to Use Helm in Production
Helm itself is not the problem.
Unexamined defaults are.
Here’s a safer approach:
1. Treat values.yaml as Production Code
- Review every section
- Delete unused features
- Explicitly set critical values
- Version control environment-specific overrides
If you haven’t read the full values.yaml, you’re not done.
2. Define Non-Negotiables
Every production Helm deployment should explicitly define:
- Resource requests and limits
- Security context
- Persistence settings
- Upgrade strategy
- Logging and metrics scope
If a chart doesn’t support these cleanly, that’s a red flag.
3. Test Failure Scenarios Early
Don’t just test:
helm install
Also test:
- Pod restarts
- Node pressure
- Rolling upgrades
- Rollbacks
Production failures rarely come from happy paths.
4. Minimize, Don’t Maximize
Avoid enabling:
- Every exporter
- Every sidecar
- Every optional feature
Production systems benefit more from clarity than capability.
When Defaults Might Be Acceptable
Defaults are fine when:
- You’re testing locally
- You’re running short-lived demos
- You’re evaluating a tool
They are not fine when:
- Data matters
- Costs matter
- Security matters
- Uptime matters
Which is… most production systems.
Final Thoughts
Helm charts didn’t make Kubernetes chaotic.
Uncritical adoption did.
“Install & forget” feels fast — but it creates slow, expensive problems later.
If you’re running Helm charts in production:
- Read the values file
- Customize intentionally
- Remove what you don’t need
- Own the configuration
Defaults are someone else’s assumptions.
Production is your responsibility.
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