Forem: squareops

The FinOps Playbook: 10 Automated Checks Every Cloud Team Should Run Weekly

squareops — Thu, 26 Feb 2026 10:34:34 +0000

Cloud costs don’t spike overnight.
They creep.
A forgotten snapshot here.
An oversized instance there.
A dev cluster running all weekend.
By the time finance notices, the waste is already baked into the monthly bill.
That’s why modern FinOps teams don’t wait for monthly reviews.
They run automated weekly checks.
Cloud cost optimization in 2026 isn’t about dashboards.
It’s about discipline.
Here’s a practical FinOps playbook:
10 automated checks every cloud team should run weekly.

1️⃣ Idle Compute Instances

Check for:

EC2 instances with low CPU (< 15–20%)
Instances with no network activity
Stopped instances with attached volumes

Idle compute is one of the fastest cost leaks.
Automation should flag:

Underutilized workloads
Candidates for downsizing
Termination-ready instances

2️⃣ Unattached Storage (EBS & Disks)

Unattached volumes accumulate silently.
Every week, check for:

Orphaned EBS volumes
Unused persistent disks
Old unattached storage

Storage waste doesn’t look dramatic until it compounds across accounts.

3️⃣ Snapshot Sprawl

Snapshots are cheap individually.
But hundreds of unnecessary backups aren’t.
Automated weekly checks should:

Identify snapshots older than policy threshold
Detect duplicates
Flag snapshots from deleted instances

Lifecycle enforcement reduces silent cost buildup.

4️⃣ Overprovisioned Instances

Most workloads don’t need the instance size they were launched with.
Weekly checks should:

Compare actual CPU & memory usage
Identify oversized EC2 or VM instances
Suggest better-fit instance families

Rightsizing is one of the highest ROI cloud cost optimization actions.

5️⃣ Kubernetes Resource Requests vs Usage

In Kubernetes environments, waste often hides in:
resources:
requests:
cpu: "2000m"
memory: "4Gi"
When actual usage is half that.
Weekly automation should:

Compare pod requests vs real utilization
Identify inefficient workloads
Highlight bin-packing inefficiencies

Kubernetes cost optimization must be continuous.

6️⃣ Idle Load Balancers & Elastic IPs

Often forgotten after deployments.
Automated detection should flag:

Load balancers with minimal traffic
Unassociated Elastic IPs
Legacy networking components

Networking waste is common in multi-account environments.

7️⃣ Non-Production Environments Running 24/7

Dev and staging are notorious cost leaks.
Weekly review should check:

Dev clusters running off-hours
Test environments active overnight
Auto-scaling misconfigurations

Scheduling shutdowns for non-prod can cut 20–40% in those environments alone.

8️⃣ Underutilized Reserved Instances & Savings Plans

Many teams purchase commitments then misallocate them.
Weekly checks should:

Measure RI/Savings Plan coverage
Identify unused commitments
Detect overcommitment risk

Optimizing commitments ensures you're not overpaying twice.

9️⃣ Tagging Compliance & Cost Allocation Gaps

Without proper tagging:

Cost attribution fails
Accountability disappears
Optimization stalls

Weekly automated checks should:

Detect untagged resources
Flag policy violations
Ensure environment classification accuracy

FinOps thrives on ownership.

🔟 Anomalous Spend Spikes

Anomaly detection isn’t optional anymore.
Weekly automation should:

Detect unusual service-level spikes
Flag unexpected region growth
Identify sudden Kubernetes scaling anomalies

The earlier you detect cost anomalies, the smaller the damage.

Why Weekly Matters

Why not monthly?
Because cloud environments change daily.
Deployments happen hourly.
Infrastructure scales automatically.
New services get added constantly.
A monthly review means:
4 weeks of accumulating waste.
Weekly automation means:
Early detection.
Faster remediation.
Smaller financial impact.

Manual Reviews Don’t Scale

In small environments, manual audits work.
At scale, they fail.
Modern cloud environments require automated cost optimization systems that:

Continuously scan for inefficiencies
Surface actionable insights
Provide real-time savings opportunities
Reduce reliance on spreadsheet reviews

FinOps is no longer a reporting function.
It’s an operational discipline powered by automation.

The Real FinOps Mindset Shift

Traditional mindset:
“Let’s review the bill at month end.”
Modern mindset:
“Let’s eliminate waste every week.”
Cloud cost optimization succeeds when:

Engineering owns efficiency
Finance supports governance
Automation detects waste continuously

Without structured weekly checks, even mature cloud teams drift into inefficiency.

Final Thought

Cloud scale without cost discipline becomes margin erosion.
But cloud scale with structured, automated FinOps checks becomes a competitive advantage.
If you implement just these 10 weekly automated checks, you’ll likely uncover 15–30% of preventable waste within the first few optimization cycles.
Because in the cloud:
What you don’t review weekly you overpay for monthly.

Cloud Cost Optimization in 2026: A Practical Guide for DevOps & FinOps Teams

squareops — Wed, 25 Feb 2026 11:55:25 +0000

Cloud adoption is no longer the competitive advantage.
Cloud efficiency is.
In 2026, nearly every serious tech company runs on AWS, Azure, or GCP. Infrastructure has become programmable, scalable, and globally distributed.
But here’s the uncomfortable reality:
Most organizations still waste 20–35% of their cloud spend.
Not because the cloud is expensive.
But because the unmanaged scale is.
That’s why Cloud Cost Optimization in 2026 is no longer an optional operational strategy.
Let’s break down what’s changed, what’s broken, and how DevOps and FinOps teams can fix it.

Why Cloud Cost Optimization Looks Different in 2026

Five years ago, optimization meant:

Turning off unused EC2 instances
Buying Reserved Instances
Reviewing AWS Cost Explorer once a month

Today, that’s not enough.
Modern cloud environments include:

Kubernetes clusters
Serverless workloads
Multi-account architectures
Multi-cloud deployments
AI/ML infrastructure
Ephemeral environments

Cloud infrastructure is now dynamic by default.
Which means optimization must also be continuous.

**The Biggest Sources of Cloud Waste in 2026

**
1. Overprovisioned Compute
Engineering teams still provision for peak traffic.
CPU averages:

15–30% utilization
Memory underused
Large instance types left untouched

Across hundreds of instances, that’s serious waste.
Rightsizing is still one of the fastest wins in cloud cost optimization.

2. Kubernetes Inefficiencies
Kubernetes now drives a massive portion of cloud spend.
Common issues:

Inflated resource requests
Poor bin packing
Underutilized nodes
Dev clusters running 24/7

Container orchestration without cost governance becomes a silent budget leak.

3. Idle & Orphaned Resources
As organizations scale:

Snapshots accumulate
Unattached volumes linger
Old load balancers stay active
Elastic IPs sit unused

No one intentionally creates waste.
But without automation, it grows.

4. Lack of Cost Ownership

In many organizations:

Engineering deploys
Finance pays
No one optimizes

Without tagging discipline and team-level cost visibility, cloud spend becomes invisible at the workload level.
And invisible costs don’t get fixed.

The 2026 Cloud Cost Optimization Framework

High-performing DevOps and FinOps teams follow a structured approach.
Here’s the practical model.
Step 1: Continuous Visibility (Not Monthly Reports)
Static dashboards are reactive.

In 2026, teams need:

Real-time spend insights
Service-level breakdowns
Workload-level cost mapping
Alerts for anomalies

Visibility must move closer to engineering not remain in finance.

Step 2: Automated Waste Detection
Manual audits cannot keep up with modern cloud velocity.
Automation should detect:

Idle resources
Overprovisioned instances
Inefficient Kubernetes workloads
Underutilized storage
Redundant services

Cloud cost optimization works best when waste is surfaced immediately not discovered weeks later.

Step 3: Continuous Rightsizing
Rightsizing is not a migration task.
It’s an ongoing process.
Best practices:

Track utilization trends over time
Adjust instance types dynamically
Evaluate ARM/Graviton alternatives
Reassess commitments (Savings Plans, RIs) regularly

Optimization should evolve with workloads.

Step 4: FinOps + DevOps Alignment
The biggest shift in 2026?
Collaboration.

FinOps provides:

Cost transparency
Budget forecasting
Financial discipline

DevOps provides:

Infrastructure control
Automation
Performance optimization

When both teams align around shared KPIs, efficiency improves dramatically.

Step 5: Shift from Reactive to Proactive
Traditional model:
Bill increases → investigation → cleanup.
Modern model:
Continuous monitoring → immediate detection → fast remediation.
This is where automated cloud cost optimization platforms make a difference.
Instead of simply reporting spend, advanced solutions continuously scan environments, detect inefficiencies, and recommend actionable savings opportunities.
The key shift is mindset:
From cost tracking
To cost engineering.

Key Metrics DevOps & FinOps Teams Should Track in 2026

Cloud cost optimization isn’t just about reducing numbers.
It’s about improving efficiency ratios.

Track:

Cost per deployment
Cost per customer
Cost per environment
CPU & memory utilization rates
Kubernetes node efficiency
Idle resource percentage

When efficiency improves, profitability follows.

**Realistic Savings Expectations

**
Organizations implementing structured optimization typically achieve:

15–25% reduction within first 60 days
25–40% reduction over sustained optimization cycles

Savings don’t come from cutting innovation.
They come from eliminating inefficiency.

Common Mistakes to Avoid

Treating optimization as a one-time cleanup
Ignoring Kubernetes costs
Relying only on native cloud dashboards
Not assigning ownership
Delaying automation

Cloud complexity increases every quarter.
Your optimization maturity must increase with it.

A Practical Starting Checklist

If you're leading DevOps or FinOps in 2026, start here:

Audit top 20 cost-driving services
Identify underutilized compute
Review Kubernetes resource requests
Enforce tagging policies
Implement automated waste detection
Schedule monthly cost-performance reviews

Small, consistent improvements compound.

Final Thought

Cloud is no longer a startup experiment.
It’s core infrastructure.
In 2026, the companies that win won’t just scale faster.
They’ll scale efficiently.
Cloud cost optimization is not about spending less.
It’s about spending smarter.
And in today’s margin-conscious environment, that difference matters more than ever.

Kubernetes Cost Optimization: The Hidden Cloud Leak Most Teams Ignore

squareops — Tue, 24 Feb 2026 12:29:35 +0000

Kubernetes was built for scalability.

But for many engineering teams, it has quietly become one of the biggest sources of uncontrolled cloud spend.

The irony?

Kubernetes makes infrastructure more efficient at scale yet without proper cost governance, it can leak thousands of dollars every month.

And most teams don’t even realize it.

This is where Kubernetes cost optimization becomes critical.

Not as a finance exercise.
But as an engineering discipline.

Let’s break down where the hidden cloud leak happens and how high-performing teams fix it.

Why Kubernetes Costs Spiral So Easily

Kubernetes abstracts infrastructure.

That’s its power.

But abstraction also creates distance between engineers and the actual compute bill.

Developers think in:

Pods
Deployments
Services
AWS or GCP charges for:
Nodes
CPU cores
Memory
Storage
Network transfer
That disconnect is where waste begins.

The Hidden Kubernetes Cost Leaks

1. Overprovisioned Resource Requests
In Kubernetes, teams define:

resources:
requests:
cpu: "1000m"
memory: "2Gi"

To avoid performance issues, engineers often overestimate.

The result:

Pods request more CPU/memory than they use
Nodes must allocate capacity for those requests
Cluster autoscaler spins up more nodes Actual usage might sit at 30–40%.

But you’re paying for 100%.

This is one of the largest drivers of Kubernetes waste.

2. Zombie Dev and Staging Clusters
Production gets attention.

Dev and staging rarely do.

Common patterns:

Clusters running 24/7
Test environments not auto-scaled
Old namespaces never cleaned up
Feature branches deployed and forgotten Multiply that by multiple squads and the cost grows silently.

3. Inefficient Node Sizing
Another frequent issue:

Large instance types selected “just in case”
No periodic rightsizing review
No evaluation of ARM/Graviton alternatives
GPU nodes running underutilized If nodes consistently operate below 50% utilization, you’re overspending.

Kubernetes cost optimization starts with node efficiency.

4. Poor Bin Packing
Kubernetes schedules pods based on requests, not real usage.

If requests are inflated:

Pods don’t pack efficiently
Nodes fragment
More nodes are provisioned than needed The cluster looks healthy.

The bill says otherwise.

5. No Visibility at the Pod Level
Cloud billing shows you:

EC2 costs
EBS costs
Network costs
But it doesn’t show:
Which team caused the spike
Which deployment consumes the most CPU
Which namespace wastes the most memory
Without workload-level cost visibility, optimization is guesswork.

Why Most Teams Ignore Kubernetes Cost Optimization

There are three main reasons:

1. It’s Not a Firefighting Issue
Unlike outages, cost waste doesn’t trigger alarms.

No pager goes off because CPU utilization is 22%.

So it gets deprioritized.

2. Ownership Is Blurry
Who owns optimization?

DevOps?
Platform engineering?
Finance?
Individual squads? Without clear ownership, waste persists.

3. Optimization Is Treated as a One-Time Task
Teams often:

Set up cluster autoscaling
Choose instance types
Configure monitoring Then never revisit those decisions.

But workloads evolve.

Traffic changes.

Architecture shifts.

Cost optimization must be continuous.

The Real Impact of Ignoring Kubernetes Costs

Let’s put numbers to it.

If your Kubernetes infrastructure costs:

$25,000/month → 30% waste = $7,500/month
$100,000/month → 30% waste = $30,000/month
$250,000/month → 30% waste = $75,000/month
Annually, that’s budget that could fund:
Hiring
Product development
Marketing
Infrastructure upgrades
Instead, it disappears into inefficiency.

How High-Performing Teams Approach Kubernetes Cost Optimization

Elite engineering teams treat cost as a performance metric.

Here’s how they do it.

1. Continuous Resource Request Tuning
They:

Monitor actual CPU and memory usage
Compare usage vs requests
Reduce inflated allocations
Automate recommendations Rightsizing pods improves bin packing automatically.

Cluster and Environment Governance They:

Auto-scale non-production clusters
Shut down dev environments off-hours
Clean up unused namespaces
Enforce lifecycle policies No zombie infrastructure allowed.

3. Node Efficiency Monitoring
They track:

Node utilization trends
Underutilized instance types
Over-fragmentation issues
Spot instance opportunities If nodes sit below 60% average utilization long-term, they act.

4. Cost Visibility at Workload Level
Instead of only looking at cloud provider dashboards, they implement tooling that:

Maps cost to namespace
Maps cost to deployment
Identifies inefficient workloads
Highlights oversized containers This bridges the gap between Kubernetes abstraction and cloud billing reality.

5. Automation Over Manual Reviews
Manual monthly audits don’t scale.

Modern teams use automated Kubernetes cost optimization platforms that:

Continuously scan cluster efficiency
Detect overprovisioned workloads
Recommend rightsizing
Identify idle resources
Provide savings estimates When optimization becomes automated, waste becomes visible immediately.

That’s when real improvement begins.

A Practical Kubernetes Cost Optimization Checklist

If you want to start today:

Review top 10 workloads by CPU request vs usage
Identify underutilized nodes
Audit dev and staging uptime
Enforce strict resource request policies
Enable cluster autoscaler correctly
Evaluate Graviton or ARM-based instances
Implement continuous cost monitoring Even basic improvements can reduce 15–30% of Kubernetes-related spend.

The Mindset Shift

Kubernetes gives you scalability.

But scalability without cost discipline becomes expensive flexibility.

Kubernetes cost optimization is not about cutting resources blindly.

It’s about:

Aligning allocation with real usage
Designing clusters efficiently
Making cost visible to engineering teams The teams that win long-term are not just reliable.

They’re efficient.

Final Thought

If your cloud bill keeps growing while cluster utilization stays flat, you likely have a hidden Kubernetes cost leak.

The question isn’t whether waste exists.

The question is:

Are you measuring it?

Because what you don’t measure in Kubernetes you overpay for.

How Engineering Teams Waste 30% of Their AWS Budget — And How to Fix It

squareops — Mon, 23 Feb 2026 12:57:00 +0000

The cloud was supposed to make infrastructure efficient.
Instead, for many engineering teams, AWS has quietly become one of the largest and least controlled line items in the company’s budget.
And here’s the uncomfortable truth:
Most engineering teams waste 20–40% of their AWS spend without realizing it.
Not because they’re careless.
Not because they lack expertise.
But because cloud waste is silent, incremental, and operationally invisible.
Let’s break down where that 30% disappears and how high-performing teams fix it.

Where the 30% Waste Actually Happens

1. Idle and Forgotten Resources
Every AWS environment accumulates “cloud leftovers.”

Unattached EBS volumes
Orphaned Elastic IPs
Old snapshots
Load balancers from past deployments
Test instances that were never turned off

Individually, these costs seem small.

Collectively, across multiple accounts and teams, they become thousands of dollars per month.
The real problem?
No one owns the cleanup.

2. Overprovisioned EC2 and RDS Instances

Engineering teams often provision for peak load “just to be safe.”
Six months later:

CPU usage averages 15–20%
Memory sits underutilized
Larger instance types are still running
No one revisits sizing decisions

Multiply this across production, staging, QA, and dev environments and you’re looking at massive over-allocation.
Rightsizing rarely happens unless there’s a budget crisis.

3. Kubernetes Clusters Running at Half Efficiency

Kubernetes (EKS) is powerful but it’s also expensive when mismanaged.
Common patterns include:

Dev clusters running 24/7
Nodes over-provisioned for safety
Poor auto-scaling configurations
Workloads not bin-packed efficiently

Container orchestration doesn’t automatically mean cost optimization.
Without continuous review, EKS costs quietly expand month after month.

4. No Cost Visibility or Ownership

This is the biggest structural issue.
In many companies:

Engineering deploys resources
Finance pays the bill
No team owns optimization

Without enforced tagging and accountability:

Teams don’t see their real consumption
Projects don’t track cost efficiency
Leadership lacks actionable insights

Cloud becomes an abstract expense until it becomes a painful one.

Why Teams Don’t Catch This Early

Here’s the hard truth:
Most teams rely on reporting tools not optimization tools.
AWS Cost Explorer tells you what you spent.
It does not:

Continuously detect idle resources
Recommend rightsizing actions
Alert you to newly created waste
Automate remediation

By the time someone manually reviews costs, the waste has already accumulated.
Cloud environments change daily.
Manual reviews can’t keep up.

The Financial Impact of 30% Waste

Let’s make it real.
If your monthly AWS bill is:

$20,000 → ~$6,000 wasted per month
$100,000 → ~$30,000 wasted per month
$500,000 → ~$150,000 wasted per month

Annually, that’s hundreds of thousands, sometimes millions lost to inefficiencies.
For startups, that’s runway.
For enterprises, that’s margin.

How High-Performing Teams Fix It

Top engineering organizations treat cloud optimization as a continuous engineering discipline not a quarterly finance review.
Here’s their playbook.

1. Continuous Idle Resource Audits
Instead of ad-hoc cleanup:

Weekly scans for unattached storage
Automatic alerts for unused IPs
Snapshot lifecycle policies
Cleanup automation pipelines

Waste detection becomes part of operations.

2. Ongoing Rightsizing
Rightsizing is not a one-time migration task.
High-performing teams:

Track CPU and memory trends over time
Identify consistently underutilized instances
Move workloads to appropriate instance families
Evaluate Graviton adoption where possible

Optimization becomes iterative.

3. Strong Tagging and Cost Ownership
Without accountability, optimization stalls.
Best practices include:

Enforced tagging policies (team, project, environment)
Cost dashboards by team
Monthly cost reviews led by engineering
Clear ownership of optimization actions

When teams see their own numbers, behavior changes.

4. Automation Over Manual Reviews
This is where most organizations evolve.
Instead of relying solely on dashboards, they implement automated cloud cost optimization tools that:

Run continuous checks across AWS services
Detect new idle or oversized resources
Provide actionable rightsizing recommendations
Highlight immediate savings opportunities

For example, automated platforms like SpendZero perform dozens of AWS checks across services, identifying inefficiencies within minutes of integration without intrusive agents or risky permissions.
The key shift is this:
Move from “What did we spend?”
To “What are we wasting right now?”

A Simple Framework to Reduce AWS Waste

If you want a practical starting point, use this checklist:

Audit idle resources weekly
Review instance utilization monthly
Implement strict tagging policies
Monitor Kubernetes node efficiency
Automate detection of new waste
Assign ownership to optimization

Cloud cost optimization is not a one-time project.
It’s a system.

The Real Mindset Shift

Cloud waste isn’t an engineering failure.
It’s an operational inevitability unless you design against it.
As teams scale:

Deployments increase
Environments multiply
Infrastructure becomes dynamic

Without automation and accountability, waste compounds.
But with continuous visibility and structured optimization, most teams can realistically reduce 20–50% of unnecessary AWS spend.
Not by cutting innovation.
By eliminating inefficiency.

Final Thought

**
If your AWS bill keeps growing faster than your traffic, customers, or revenue that’s a signal.
Somewhere inside your cloud environment, that 30% is hiding.
The question isn’t whether waste exists.
The question is whether you’re actively detecting it or passively paying for it.

A Complete Guide to Managed Cloud Security Services for AWS, Azure, and GCP

squareops — Mon, 05 Jan 2026 13:09:04 +0000

As organizations accelerate cloud adoption, security has become a shared responsibility—one that requires continuous monitoring, automation, and expert oversight. This is where Managed Cloud Security Services play a critical role. Instead of managing complex security controls in-house, businesses rely on managed services to protect their cloud environments across AWS, Azure, and GCP.

This guide explains what managed cloud security services are, why they matter, and how they help organizations build secure, compliant, and resilient cloud infrastructure.

What Are Managed Cloud Security Services?

Managed Cloud Security Services refer to the ongoing management, monitoring, and optimization of cloud security controls across public cloud platforms. These services help organizations protect workloads, data, networks, and identities while maintaining compliance and operational efficiency.

Rather than focusing on one-time security setup, managed cloud security ensures continuous protection through automation, real-time visibility, and best-practice enforcement.

Core objectives include:

Reducing security risks and misconfigurations
Improving compliance and governance
Strengthening cloud resilience
Supporting DevOps and cloud-native teams

Why Managed Cloud Security Is Essential

Cloud platforms evolve rapidly. New services, frequent updates, and complex configurations often lead to security gaps if not managed proactively.

Common challenges organizations face:

Misconfigured storage, networks, or IAM policies
Limited visibility across multi-cloud environments
Alert fatigue and delayed incident response
Difficulty meeting compliance requirements

Managed cloud security services address these challenges by embedding security into day-to-day cloud operations.

Managed Cloud Security Services for AWS

AWS offers a powerful set of native security tools, but managing them effectively requires expertise.

Key focus areas include:

Identity and Access Management (IAM): Enforcing least-privilege access
Network Security: Securing VPCs, security groups, and traffic flows
Threat Detection: Monitoring logs and events for suspicious activity
Data Protection: Encryption, key management, and secure backups

Managed cloud security services help organizations continuously optimize AWS security posture while aligning with the AWS shared responsibility model.

Managed Cloud Security Services for Azure

In Azure, security is tightly integrated with identity, networking, and application services. However, complexity increases as environments scale.

Managed services typically cover:

Azure Active Directory security and identity governance
Security posture management across subscriptions
Workload protection for VMs, containers, and PaaS services
Policy enforcement using automated compliance controls

By actively managing Azure security configurations, organizations reduce risk without slowing down innovation.

Managed Cloud Security Services for GCP

GCP emphasizes secure-by-design architecture, but proper implementation is critical.

Key managed security capabilities include:

IAM and service account management
Network segmentation and firewall rules
Audit logging and monitoring
Secure container and Kubernetes environments

Managed cloud security services ensure GCP workloads remain protected while supporting modern, cloud-native development.

Core Components of Managed Cloud Security Services

Across AWS, Azure, and GCP, effective managed cloud security services typically include:

1. Continuous Monitoring & Visibility
24×7 monitoring helps detect risks, misconfigurations, and anomalies before they impact operations.

2. Security Posture Management
Automated checks ensure cloud resources follow security best practices and compliance standards.

3. Identity & Access Control
Strong IAM policies reduce unauthorized access and credential misuse.

4. Incident Detection & Response Support
Faster detection and guided response minimize downtime and business impact.

5. Compliance Enablement
Ongoing alignment with industry and regulatory requirements through automated policies and reporting.

Benefits of Managed Cloud Security Services

Organizations adopting managed cloud security gain measurable advantages:

Reduced operational overhead for internal teams
Improved security consistency across environments
Faster detection of risks and misconfigurations
Scalable security aligned with cloud growth
Better collaboration between DevOps and security practices

Most importantly, security becomes an enabler, not a bottleneck.

Choosing the Right Managed Cloud Security Partner

When selecting a partner, look for expertise that goes beyond tools and dashboards.

Key considerations:

Deep experience with AWS, Azure, and GCP
Strong understanding of cloud architecture and DevOps workflows
Automation-first approach to security
Focus on reliability, performance, and scalability

The right partner helps embed security into cloud operations rather than treating it as a separate function.

Conclusion: Secure the Cloud Without Slowing Down

Modern cloud environments demand continuous security—not occasional audits or manual checks. Managed Cloud Security Services provide the expertise, automation, and visibility needed to protect workloads across AWS, Azure, and GCP while supporting rapid innovation.

For organizations building and scaling in the cloud, security must evolve alongside infrastructure, applications, and teams.

At SquareOps, we help organizations implement cloud-native security best practices as part of reliable, scalable cloud operations—ensuring your infrastructure remains resilient, compliant, and ready for growth.

Ready to Build a Secure & Scalable Cloud?

Strengthen your cloud security without adding complexity.
Partner with experts who understand cloud, DevOps, and reliability.

Microservice Delivery with Jenkins, Helm & ArgoCD on K8s

squareops — Fri, 16 May 2025 05:44:10 +0000

Introduction

**
In the modern software development landscape, microservices and Kubernetes have become a preferred choice for building and deploying applications. The decoupled nature of microservices allows for scalability and maintainability. However, the management and delivery of these services on Kubernetes often present new challenges.

Some of these challenges include

:
**
Configuration Management: Handling the configuration of a large number of microservices can be complex and time-consuming, requiring a robust solution.
Continuous Integration and Continuous Delivery (CI/CD) with Rollbacks and Version Control: Ensuring that applications are always up-to-date and having the ability to quickly revert to a stable previous version in case of issues are essential for maintaining a reliable software delivery pipeline.
Monitoring Multiple Deployments: Keeping track of the status and health of various deployments across the Kubernetes cluster can be overwhelming without proper tools and automation.
Through this blog post, we will showcase an efficient solution to tackle these challenges using Jenkins, Helm charts, and ArgoCD.

Before we dwell deep into the Microservice Delivery Solution it’s crucial to have a robust Infrastructure in place for deploying these services. One popular tool for infrastructure provisioning and management is Terraform. By leveraging Terraform, you can define your infrastructure as code and ensure consistent and repeatable deployments.

At SquareOps, we have created a comprehensive guide on building the infrastructure for microservices on AWS using Terraform. We highly recommend referring to our GitHub repository SquareOps GitHub for detailed instructions and code samples.

In that guide, you will find step-by-step instructions on configuring the necessary resources such as Virtual Private Cloud (VPC), subnets, security groups, EKS cluster and Elastic Load Balancers (ELBs). Additionally, we cover topics such as autoscaling groups, EKS add-ons , Amazon RDS for database management, and integrating AWS services with your microservices architecture. Now , let’s jump back to Helm , Jenkins and ArgoCD for deployment.

Streamlining Microservices with Helm Charts

**
Helm, the package manager for Kubernetes, is at the core of our solution. Helm charts are a collection of files that describe a related set of Kubernetes resources. They provide a method to deploy applications onto the Kubernetes platform smoothly.

We maintain a single Git repository for all services’ Helm charts. Our structure includes a main chart, with each microservice chart added as a dependency. This approach enhances modularity and reusability, allowing each microservice to encapsulate its Kubernetes resources.

.
├── charts
│ ├── microservice-a
│ │ ├── Chart.yaml
│ │ ├── templates
│ │ │ ├── deployment.yaml
│ │ │ ├── service.yaml
│ │ │ └── ...
│ │ └── values.yaml
│ ├── microservice-b
│ │ ├── Chart.yaml
│ │ ├── templates
│ │ │ ├── deployment.yaml
│ │ │ ├── service.yaml
│ │ │ └── ...
│ │ └── values.yaml
│ └── ...
└── main-chart
├── Chart.yaml
├── values.yaml
└── requirements.yaml
view rawhelm-chart hosted with ❤ by GitHub
In this structure:

The charts directory contains a subdirectory for each microservice, which in turn includes its specific Helm chart.

Each microservice chart consists of a Chart.yaml file (the chart description), a values.yaml file (the default configuration values), and a templates directory that contains the Kubernetes resource definitions.

The main-chart directory contains the main Helm chart. It includes a Chart.yaml file, a values.yaml file, and a requirements.yaml file that lists each microservice chart, under /charts directory, as a dependency.

Application Environment Setup with ArgoCD
ArgoCD, a declarative GitOps continuous delivery tool for Kubernetes, ensures that our cluster configuration matches the state specified in the Git repository. It is a powerful tool that simplifies the process of deploying your applications configured with Helm charts.

Here, we’ll illustrate the steps to quickly use the Helm Git repository to create an application on the ArgoCD user interface (UI):

Log into the ArgoCD UI: First, log into the ArgoCD UI using your credentials. The UI is typically accessed via a web browser at the ArgoCD server’s host.
Once logged in, Connect the Git repository for Helm chart under Settings.
Navigate to the “Applications” page and click on the “New Application” button.
Fill in the Application Details and Create the Application:
Application Name: Choose a name for your application.
Project: Select the ArgoCD project where your application will reside.
Sync Policy: Choose ‘Manual’ for the start, you can change it to ‘Automatic’ later based on your requirements.
Repository URL: Select the Git URL of your Helm charts repository.
Revision: Specify the Git revision of your Helm charts repository (for example, a branch name like master or main).
Helm Chart: Enter the path to your main Helm chart within the repository (for example, main-chart).
Values Files: Add the path to your environment-specific values.yaml file in your repository. This file contains configuration overrides for your microservice Helm templates.
ArgoCD will fetch the specified Helm chart from your Git repository, apply the configuration overrides from your values.yaml file, and deploy the application to your Kubernetes cluster. By keeping separate values.yaml files for different environments, we can manage environment-specific configurations efficiently and prevent unnecessary conflicts.

Once your application is deployed, you can monitor its status and health directly from the ArgoCD UI. Click on your application to view its details. Here you will see a visualization of your application’s dependencies and related Kubernetes resources. This graph is an incredibly useful feature of ArgoCD that lets you see the state of your application at a glance.

Agro CD
Building and Deploying with Jenkins Pipeline
Jenkins, a popular open-source automation server, is our tool of choice for Continuous Integration and Continuous Deployment (CI/CD). With its multibranch pipeline feature, we can create and manage a pipeline for each branch in our Git repository.

Here’s an example of a Jenkins multibranch pipeline for a Node.js application:

pipeline {
agent any
environment {
ECR_REPO_NAME = 'your-ecr-repo-name'
ECR_URL = 'https://your-ecr-url'
GIT_CREDENTIALS_ID = 'your-git-credentials-id'
}
stages {
stage('Unit Test') {
when { changeRequest target: 'develop' }
steps {
sh 'npm test'
}
}
stage('Docker Build and Push') {
when { anyOf { branch 'develop'; branch 'master' } }
steps {
script {
docker.build("${ECR_REPO_NAME}:${env.BUILD_NUMBER}")
docker.withRegistry(ECR_URL, "ecr:us-west-2:${env.CREDENTIALS_ID}") {
docker.push("${ECR_REPO_NAME}:${env.BUILD_NUMBER}")
}
}
}
}
stage('Approval') {
when { anyOf { branch 'develop'; branch 'master' } }
steps {
input 'Deploy to environment?'
}
}
stage('Deploy') {
when { anyOf { branch 'develop'; branch 'master' } }
steps {
script {
// Determine the environment based on the branch
def env = (env.BRANCH_NAME == 'develop') ? 'development' : 'production'
// Clone the Helm chart repo
git credentialsId: GIT_CREDENTIALS_ID, url: 'https://github.com/your-helm-chart-repo'
// Update the Docker image tag in the values.yaml file
sh """
sed -i 's|image: .|image: ${ECR_URL}/${ECR_REPO_NAME}:${env.BUILD_NUMBER}|' ${env}/values.yaml
"""
// Commit and push the changes
sh """
git add ${env}/values.yaml
git commit -m "Update Docker image tag for ${env} environment"
git push origin HEAD
"""
}
}
}
}
}
view rawpipeline-ecr hosted with ❤ by GitHub
In this Jenkinsfile, the Unit Test stage runs only when a pull request is raised from a branch matching the feature- pattern to the develop branch.

The Docker Build and Push and Approval stages run only for the develop and master branches. the pipeline builds Docker images and pushes them to the Amazon Elastic Container Registry (ECR)

On a successful push to ECR, the pipeline triggers a manual approval step for deployment on the development and production environments, respective to the develop and master branches.

jenkins-pipelines
The Deploy stage includes steps to clone the Helm chart repo, update the Docker image tag in the respective environment’s values.yaml file, and push the changes back to the Git repository. The environment is determined based on the branch name.

Conclusion

**The combination of Kubernetes, Jenkins, Helm Charts, and ArgoCD provides a comprehensive solution to seamless Microservice Onboarding, to Kubernetes efficiently.

At SquareOps Technologies, we specialize in implementing advanced pipeline workflows that includes DevSecOps, performance testing, Blue-green deployments, Rollbacks, DB migrations, Notifications etc.

We can offer an in-depth, customized implementation to expedite your microservice onboarding process. Contact us today to discover how we can optimize your microservice delivery.

Source Url: https://squareops.com/blog/microservice-delivery-on-kubernetes-using-jenkins-helm-charts-and-argocd/

SquareOps specializes in DevOps,

squareops — Wed, 02 Apr 2025 10:00:45 +0000

SquareOps is a modern DevOps and Cloud Consulting company enabling startups and enterprises to ship faster, scale seamlessly, and manage cloud infrastructure with confidence. From designing robust CI/CD pipelines to implementing Kubernetes and Site Reliability Engineering (SRE) best practices, SquareOps empowers engineering teams to move from legacy systems to cloud-native environments. With a sharp focus on automation, performance, and reliability, the team at SquareOps combines technical excellence with a deep understanding of business goals—delivering results that drive long-term success.