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SaaS / HR TechSouth Asia12 Weeks

HR & Payroll SaaS Startup

An 8-person engineering team running a multi-tenant HR & Payroll SaaS product was stuck in a cycle of manual deployments, alert fatigue, untracked cloud spend, and no visibility into what was actually failing in production. Within 12 weeks, we transformed their platform into a stable, automated, cost-efficient system their team could confidently operate and grow.

Client name and identifying details withheld at their request. References available during consultation.

40%
Cloud Cost Reduction
60%
Fewer Production Incidents
Faster Release Cycles
0
Manual Deployments Remaining

!The Challenge

When we first engaged with this team, they had a working product with real paying customers — but their infrastructure was held together with manual processes and tribal knowledge. Every release required a senior engineer to SSH into production servers, run scripts in a specific order, and pray nothing broke. There was no rollback mechanism. If something went wrong, the only option was to SSH back in and manually revert changes — a process that could take hours.

Their AWS environment had grown organically over two years with no tagging strategy, no budgets, and no cost visibility. Resources were left running from old experiments. Dev environments were provisioned manually and never cleaned up. Their monthly cloud bill had grown by 60% over 12 months, but no one could explain where the money was going.

Monitoring was in place — sort of. They had CloudWatch alarms configured, but the thresholds were set arbitrarily and fired constantly. Engineers had learned to ignore alerts because 80% of them were noise. The 20% that mattered were lost in the flood. Two genuine production incidents in the previous quarter had gone undetected for over an hour because of this.

With a pipeline of enterprise prospects requiring SOC 2-aligned practices and a growing customer base expecting 99.9% uptime, the status quo was no longer viable. They needed to professionalise fast — without disrupting existing customers or derailing their product roadmap.

Before vs After

AreaBeforeAfter
DeploymentsManual SSH to productionFully automated CI/CD via GitHub Actions
Release FrequencyOnce every 2 weeksMultiple times per day
RollbacksManual, hours of effortOne-click, under 5 minutes
Cloud SpendUntracked, growing 5% monthlyTagged, budgeted, 40% lower
Alerts200+ noisy alerts, mostly ignoredActionable alerts only, triaged by runbook
InfrastructureManually provisioned, undocumentedFully in Terraform, version-controlled
EnvironmentsProduction onlyDev, staging, production — fully isolated

Tech Stack

CI/CD
GitHub Actions, Docker, Amazon ECR
Compute
AWS ECS Fargate, Application Load Balancer
Infrastructure as Code
Terraform, AWS S3 + DynamoDB (state)
Monitoring & Alerting
Prometheus, Grafana, AWS CloudWatch, PagerDuty
Cost Management
AWS Cost Explorer, Budgets, resource tagging
Security
AWS IAM least-privilege, Secrets Manager, VPC isolation

What We Did

CI/CD Pipeline — End to End

We built a full GitHub Actions pipeline covering linting, unit tests, Docker image builds pushed to Amazon ECR, and zero-downtime rolling deployments to ECS Fargate. Every pull request triggers a full test run. Merges to main deploy automatically to staging, and production releases require a one-click approval gate. Engineers went from dreading deployments to shipping multiple times a day with full confidence.

Infrastructure as Code Migration

All existing manually-provisioned AWS resources were audited, documented, and migrated into modular Terraform. We used S3 + DynamoDB for remote state with locking to enable safe collaboration. Every environment — dev, staging, production — is now a Terraform workspace, ensuring consistency and eliminating the "it works in dev but not in prod" problem entirely.

Cloud Cost Optimisation

We ran a full AWS cost audit, identifying over 30 resources with no active purpose — forgotten EC2 instances, unattached EBS volumes, unused Elastic IPs, and idle RDS snapshots. After cleanup, we right-sized all remaining instances based on actual utilisation data from the past 90 days, implemented a comprehensive resource tagging strategy, and configured AWS Budgets with alerts at 80% and 100% thresholds. Monthly spend dropped 40% within the first month.

Observability Rebuild

We stripped out the noisy CloudWatch alarm configuration and replaced it with a structured observability stack. Prometheus scrapes application and infrastructure metrics. Grafana dashboards give the team real-time visibility into latency, error rates, and resource utilisation — the four golden signals. Alerts are routed through PagerDuty with severity levels and runbooks attached to each, so on-call engineers know exactly what to do when something fires.

Environment Separation & Security Hardening

Production, staging, and dev were isolated into separate VPCs with strict security group rules. IAM roles were audited and rebuilt on least-privilege principles. Secrets were migrated from hardcoded environment variables into AWS Secrets Manager, with automatic rotation enabled for database credentials. This work directly supported their enterprise sales pipeline by demonstrating SOC 2-aligned practices.

Key Engineering Decisions

Decision: ECS Fargate over Kubernetes

An 8-person team doesn't need the operational overhead of managing Kubernetes. ECS Fargate gives them container orchestration with auto-scaling without the control plane complexity. They can migrate to EKS later when the team and scale justify it.

Decision: GitHub Actions over Jenkins

The team was already using GitHub. Adding Jenkins would introduce another system to maintain, secure, and update. GitHub Actions eliminated that overhead entirely and keeps CI/CD configuration version-controlled alongside application code.

Decision: Prometheus + Grafana over a SaaS observability tool

At their scale and budget, paying for Datadog or New Relic would have consumed a significant portion of their cloud budget. Self-hosted Prometheus + Grafana on ECS gave them enterprise-grade observability at near-zero marginal cost.

Engagement Timeline

Week 1–2
Discovery & Audit
Full audit of existing AWS environment, codebase, deployment process, and alerting configuration. Identified all waste, risks, and quick wins.
Week 3–4
Terraform Migration
Migrated all existing infrastructure to Terraform. Established remote state, workspace structure, and tagging standards.
Week 5–6
CI/CD Pipeline Build
Built GitHub Actions pipelines for all services. Deployed staging environment. First automated deployment to staging executed.
Week 7–8
Cost Optimisation
Removed waste, right-sized instances, implemented budgets and tagging. Cloud spend reduced 40% by end of week 8.
Week 9–10
Observability Stack
Deployed Prometheus + Grafana. Rebuilt all alerts with runbooks. Eliminated noise. First clean on-call rotation completed.
Week 11–12
Security Hardening & Handover
IAM least-privilege rollout, secrets rotation, VPC isolation. Team training sessions. Full documentation delivered.

Results Delivered

Zero manual deployments — fully automated CI/CD live
40% reduction in monthly AWS cloud spend
60% fewer production incidents within 8 weeks
Release cycle reduced from 2 weeks to same-day
Actionable alerting — noise eliminated entirely
All infrastructure version-controlled in Terraform
Dev, staging, production environments fully isolated
Enterprise security posture — SOC 2 alignment achieved

"We went from dreading every release to shipping multiple times a week with full confidence. ESSEMVEE didn't just fix our infrastructure — they gave us the systems and knowledge to own it ourselves. The ROI was visible within the first month."

Co-Founder & CTO

HR & Payroll SaaS · South Asia · Name withheld on request

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