Kubernetes Platform Setup for Microservices with Observability and Safe Deployments
A SaaS team needed a Kubernetes platform to run multiple services with predictable deployments and clear observability. We designed the cluster architecture, implemented security and autoscaling, and added monitoring and alerts so teams could ship confidently.
Confidential engagement. NDA available upon request.
99.9%
Uptime Target
3
x Faster Deployments
50%
Lower Incident Rate
9
Weeks to Delivery
About the Client
Industry
SaaS
Company Size
60 to 120 employees
Background
A SaaS company moving from VM based deployments to containerized services. They needed repeatable environments and visibility into production behavior.
Operational Challenges
Deployments were risky
Release processes were inconsistent and required manual steps and restarts.
Limited visibility
Logs and metrics were fragmented, slowing incident response.
Scaling constraints
Traffic spikes caused performance issues due to fixed capacity planning.
Security and access concerns
Access control and secrets handling needed standardization and auditability.
The Mission
Build a Kubernetes platform that supports safe deployments, clear observability, and scalable capacity with security best practices.
How We Approached It
01. Design
Week 1 to 2- Cluster and network architecture design
- Security and IAM strategy
- Observability requirements
- Migration and rollout plan
02. Implementation
Week 3 to 7- Cluster provisioning and baseline hardening
- Ingress, autoscaling, and resource policies
- Logging, metrics, and alerting setup
- Secrets management integration
03. Migration and handoff
Week 8 to 9- Service migration with staged rollouts
- Load testing and tuning
- Runbooks and incident response guidance
- Team training and documentation
Vulnerabilities Discovered
0
CRITICAL
2
HIGH
2
MEDIUM
0
LOW
No standardized deployment strategy
Services deployed with inconsistent practices, increasing outage and rollback risk.
Services deployed with inconsistent practices, increasing outage and rollback risk.
Secrets handling was inconsistent
Some secrets were stored in unsafe locations and required centralized management.
Some secrets were stored in unsafe locations and required centralized management.
Resource limits not defined
Missing limits caused noisy neighbor issues and unpredictable performance.
Missing limits caused noisy neighbor issues and unpredictable performance.
Alerting not tied to user impact
Alerts were noisy and not aligned with service level indicators.
Alerts were noisy and not aligned with service level indicators.
How We Fixed It
Platform baseline and policies
Implemented a secure baseline with resource policies, autoscaling, and clear network boundaries.
Observability
Centralized logs and metrics with actionable alerts and runbooks.
Safe deployment patterns
Established repeatable rollouts and rollback strategies that teams could follow consistently.
Measurable Outcomes
Teams shipped more confidently with better visibility and fewer incidents, while the platform scaled smoothly during traffic spikes.
3
x Faster Deployments
50%
Lower Incident Rate
99.9%
Uptime Target
40%
Faster Incident Response
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