Lead School
When exam season hits, millions of students can't wait for infrastructure to catch up. Lead School needed surge-proof, compliant, fully automated infrastructure across thousands of schools.
The Challenge
Lead School was scaling its EdTech platform across thousands of schools in India, but a monolithic architecture was limiting development speed and preventing the platform from scaling cost-effectively during exam periods. Manual infrastructure setup created deployment bottlenecks, observability was minimal, and the platform needed to meet strict compliance requirements around student data — all while keeping infrastructure costs under control.
Our Solution
GYSP migrated the monolithic architecture to microservices on Docker Swarm, AWS ECS/Fargate, and EKS. Infrastructure-as-Code with Terraform replaced all manual provisioning, and GitHub Actions-powered CI/CD pipelines automated the full deployment lifecycle. A full observability stack — Grafana, Loki, Prometheus, and OpenTelemetry — was built to provide distributed tracing and end-to-end performance monitoring. Infrastructure hardening and regulatory compliance frameworks were implemented to ensure 100% readiness for student data handling.
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Get free briefKey Deliverables
- Monolith-to-microservices migration on Docker Swarm, AWS ECS/Fargate, and EKS
- Terraform IaC replacing all manual infrastructure provisioning
- GitHub Actions CI/CD pipelines automating the full deployment lifecycle
- Full observability stack: Grafana, Loki, Prometheus, and OpenTelemetry
- Distributed request tracing for fast root-cause identification
- Infrastructure hardening and incident response frameworks
- 3x scalability headroom engineered for exam period demand spikes
Services Delivered
- DevOps Engineering
- Infrastructure as Code
- Observability
- DevSecOps
Tech Stack
Frequently Asked Questions
How did GYSP prepare Lead School's infrastructure for exam-period demand spikes?+
GYSP engineered 3× scalability headroom using Kubernetes auto-scaling on AWS EKS — calibrated specifically to Lead School's peak load patterns rather than a generic scaling profile. Combined with the migration from a monolithic architecture to independently scalable microservices, high-demand components like assessment engines and live session services could scale without dragging the entire platform. Load testing was performed pre-exam season to validate thresholds.
What is the benefit of migrating from a monolith to microservices for an EdTech platform?+
A monolith means all components scale together — a traffic spike to the assessment module forces the entire platform to scale, wasting resource on low-demand components. Microservices allow each component to scale independently, dramatically reducing infrastructure costs outside peak periods while maintaining headroom for exam surges. Deployment also becomes safer — a change to one service cannot take down unrelated features.
How did GYSP implement distributed tracing for Lead School?+
GYSP deployed OpenTelemetry as the instrumentation layer, with traces collected and visualised through Grafana Tempo. This gave the engineering team end-to-end visibility into request flows across all microservices — so when a user experienced latency, the trace immediately identified which service in the chain was the bottleneck, reducing mean time to root cause from hours to minutes.
What does compliance-ready infrastructure mean for platforms handling student data?+
Compliance-ready means the infrastructure enforces data protection controls — encryption at rest and in transit, access logging, IAM-based least-privilege access, and documented incident response — that satisfy applicable regulations for handling student personally identifiable information (PII). GYSP implemented and documented these controls during the infrastructure build, so Lead School could demonstrate compliance to partners and regulators without a separate remediation programme.
Work with GYSP
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