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Production Modernization

Scaling to 5M+ Users with Zero-Downtime Migration.

Coretus migrated a Series-B FinTech monolith into an event-driven microservices architecture on AWS—improving reliability, cutting p95 latency from 400ms → 45ms, and reducing cloud spend by $12k/month.

Timeline
8 Months
Delivery Model
Managed Squad
Risk Profile
Zero Downtime
Key OutcomesVerified Metrics
90%
Faster p95 Latency
400ms
$12k
Monthly Savings
$45k/mo
0
Downtime Events
Daily incidents
45ms
p95 API
99.99%
Uptime
Daily
Deploys
Client Context

Series-B FinTech platform supporting consumer wallets, card payments, and real-time ledger operations across high-frequency peak windows.

Traffic Reality

Extreme burst traffic for ~2 hours/day (payments + transfers + reporting) that previously forced always-on vertical scaling.

Success Criteria

No downtime, predictable performance during peak, measurable cloud savings, and clean operational handover with runbooks and dashboards.

Executive Summary

The legacy Node.js monolith was collapsing under peak loads—causing outages, ledger contention, and expensive “scale-up-to-survive” infrastructure.

Coretus deployed a dedicated modernization squad and executed a phased Strangler Fig migration. We stabilized performance first, then extracted core services into an event-driven architecture with strict observability and progressive traffic shifting—delivering reliability and performance without pausing feature development.

Primary Objective
Eliminate database locks and reduce AWS OpEx while maintaining continuous feature delivery.
Zero-downtime rollout
Audit-ready ledger integrity
Production observability gates
Constraints We Designed For
No “Big Bang” Rewrite

Platform had to keep running while we modernized—zero disruption to customers.

Feature Delivery Must Continue

We modernized in parallel with the product roadmap—without slowing releases.

Auditability & Security

Access controls, traceability, and ledger correctness were non-negotiable.

Hard Production Deadline

Stability required before a major peak event—migration plan had strict checkpoints.

The Challenges

Monolith Bottleneck

One Node.js deployment handled auth, ledger writes, notifications, and reporting. Peak loads caused CPU spikes and cascading failures—locking the database and degrading the entire platform.

DB contention during peak writes
High tail latency (p95 ~400ms)

Cost & Reliability Bleed

To survive spikes, the team ran oversized instances 24/7. The result: a $45k/month bill and frequent instability that impacted customer trust and operational workload.

Always-on vertical scaling
Daily incidents & manual mitigation
Why This Mattered
Customer Trust

Outages during peak transaction windows directly impacted retention and brand confidence.

Operational Load

On-call escalations became routine, slowing feature delivery and draining engineering focus.

Cloud Efficiency

Paying for capacity 24/7 to serve a short burst window created avoidable monthly burn.

Zero-Downtime Protocol

Engineering the Safe Passage.

Modernizing a live FinTech core is like changing an airplane engine mid-flight. We use a Safety-First Deployment Strategy to ensure your users never feel the transition.

Canary Traffic Shifting

We route 1% of live traffic to the new service while monitoring real-time p95 stability. Scaling only occurs once 100,000+ transactions pass without error.

Parallel Shadow Writes

Data is written to both legacy and new ledgers simultaneously. We verify bit-for-bit parity in the background before the new system becomes the "Source of Truth."

Automated Rollback Gates

Our CI/CD pipelines are hard-coded with "Circuit Breakers." If error rates spike above 0.05%, traffic automatically reverts to the legacy monolith in under 2 seconds.

The Unit We Deployed

A focused modernization pod designed to stabilize production first, then execute a controlled migration with measurable checkpoints.

Tech Lead
Architect
2× Backend
Golang
1× Frontend
React
1× DevOps
Terraform

From Monolith to Mesh

BEFORE

The Bottleneck

Shared DB: Single point of failure. One heavy query could stall critical transaction flows.
Vertical Scale: Oversized instances running 24/7 to survive a 2-hour daily peak.
Risky Releases: Monolith deploys increased blast radius and slowed iteration speed.
AFTER

The Mesh

Event Driven: Services communicate via durable events with idempotency + retries to prevent duplicate ledger writes.
Elastic Scale: Serverless + autoscaling compute matched real usage, reducing baseline cost.
Observability Gates: p95 latency, error rate, and saturation metrics controlled rollout decisions.
Solution Architecture

We implemented a phased Strangler Fig migration behind an API Gateway. Core domains were extracted into independent services with clear boundaries and event contracts.

Service Boundaries: Auth, Ledger, Notifications, Reporting
Event Safety: Idempotent consumers + retries + dead-letter handling
Data Integrity: Transactional outbox to keep DB + events consistent
Implementation Highlights
Performance
Caching + query optimization reduced tail latency during burst windows.
Reliability
Progressive rollouts with canary traffic shifting and error-budget guardrails.
Observability
Dashboards, SLOs, and alerting for p95 latency, errors, saturation, and queue lag.
Delivery
CI/CD pipeline with automated checks and rollback hooks to minimize release risk.
Speed Advantage

We Didn’t Start From Scratch.

Migrating authentication and payment flows is risky. We accelerated delivery using pre-audited, reusable service modules—so we could spend time on architecture correctness, observability, and rollout safety.

Auth Core Module
Saved 3 Weeks
Payment Wrapper
Saved 4 Weeks

Time-to-Market Impact

Typical Migration6 Months
Coretus (Accelerated)3.5 Months
Key Principle

Ship stability first, then migrate with measurable gates. Speed comes from disciplined rollout—not risky rewrites.

The Migration Roadmap

Month 1–2: Stabilization

Production audit, caching strategy, query tuning, and observability baseline to stop the bleeding before extraction.

Month 3–5: Decoupling

Extracted Auth + Ledger boundaries, introduced events, routed partial traffic through gateway with strict canary gates.

Month 6–8: Optimization & Handover

Scaled traffic to 100%, decommissioned monolith paths, improved cost posture, and delivered runbooks + KT sessions.

Governance & Quality

  • Daily standups + incident review during stabilization
  • Weekly sprint demos with measurable metrics (p95, errors, saturation)
  • Slack Connect channel for real-time coordination
  • Bi-weekly architecture reviews + ADR documentation
Deliverables
IaC repo + environment playbooks
Dashboards + alerts + runbooks
Architecture diagrams + ADRs
Load test suite + benchmark report
Security & Governance
Access & Secrets

Least-privilege IAM, secure secret management, and environment isolation for safe operations.

Auditability

Traceable event streams + logs for transaction workflows and operational accountability.

Operational Controls

Rate limits, circuit breakers, and alerting to detect anomalies before they become incidents.

Outcomes

Financial Impact
$12k / mo
Reduction in cloud spend
Performance
45ms
p95 API response (from 400ms)
Reliability
99.99%
Uptime during peak windows
Throughput
Handled peak loads without vertical scaling
Delivery
Weekly releases → daily deploys
Incidents
Daily pages → stable operations
Handover
Runbooks + dashboards + KT sessions

“Coretus didn’t behave like a vendor. They challenged risky decisions, executed a disciplined migration plan, and owned the outcome. We shipped stability before our biggest peak.”

VP Engineering
UK FinTech Unicorn

Technology Stack

Application
Backend ServicesGo + Node.js
FrontendReact
API GatewayGateway + Routing
Cloud & Ops
ComputeAWS (Serverless + Autoscaling)
DataPostgreSQL + Redis
Infra as CodeTerraform
AWS
Go
K8s
Postgres

FAQs

Can you modernize without rewriting everything?

Yes. We use the Strangler Fig approach—extracting services incrementally behind a gateway—so the legacy platform keeps running while risk stays controlled.

How do you prevent downtime during migration?

We rely on canary rollouts, progressive traffic shifting, and observability gates (p95 latency, errors, saturation). If metrics regress, we rollback safely.

What do you deliver at the end of the project?

IaC, architecture diagrams/ADRs, dashboards + alerts, runbooks, benchmark reports, and knowledge transfer—so your team can operate and extend confidently.

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If your platform struggles during peak windows, we’ll map a safe modernization plan with measurable milestones and zero-downtime rollout strategy.

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