Client Story: From Monolith to Microservices in 3 Months for an Automotive Marketplace Company

Overview

As automotive marketplaces strive to deliver personalized, scalable, and real-time digital experiences, old monolithic systems have become barriers to progress. One such company worked with Atsky to modernize its platform, moving from a single-tier monolith to a containerized, microservices-based architecture on AWS. The result? 30% faster feature releases, better system resilience, and a smoother path to partner integrations, all within 90 days.

The Client

A North American automotive wholesale marketplace enables real-time auctions between used car dealers and buyers. With ambitious growth goals and an expanding partner network, including credit unions and OEM data providers, their existing system couldn't keep up.

Business Model: B2B/B2C vehicle auction & financing platform
Users: 10,000+ active dealers and agents
Monthly Volume: 30,000+ car listings
Original Stack: Monolithic Django + Postgres hosted on EC2

The Challenge

The client’s monolithic platform created several bottlenecks:

Slow Feature Releases: Monthly releases due to fear of regressions
Scalability Issues: One service failure could impact the entire application
Partner Inflexibility: Integrations with financing APIs were fragile and slow
Developer Frustration: Tight coupling made onboarding and testing painful

Their goal was to replatform in under 3 months without affecting active users or revenue.

Our Approach

Phase 1: Application Decomposition

We ran a domain-driven analysis to break the monolith into independently deployable services. Key bounded contexts included:

Auction Engine
Dealer Onboarding & Identity
Vehicle Listings & Pricing
Financing & Credit Flow
Notification & Analytics

Each service was designed around clear API contracts with async communication via event buses.

Phase 2: Infrastructure Modernization

Migrated from EC2 to Amazon EKS (Elastic Kubernetes Service)

Split Postgres DB into service-specific schemas with read replicas

Introduced Kafka and EventBridge for service separation

Centralized auth using AWS Cognito

Phase 3: CI/CD Automation & Observability

Set up GitHub Actions and ArgoCD for service-level deployments

Canary deployments and feature flag rollouts via LaunchDarkly

Observability stack with Prometheus, Grafana, and Loki

Full-stack tracing using OpenTelemetry and AWS X-Ray

The Results

KPI

Before

After

Impact

Release Frequency

Monthly

Weekly (some daily)

30%+ faster feature rollouts

Service Resilience

Single point of failure

Isolated failures

System-wide uptime improvement

Partner Integration Time

6+ weeks

<2 weeks

Faster monetization

Developer Onboarding Time

3–4 weeks

1–2 weeks

Simplified service ownership

New Revenue Features

Delayed

Continuous delivery

Enabled credit union integrations

RTO / RPO

Recovery Time Objective (RTO): ~10 minutes
- Once microservices were implemented on Amazon EKS, the platform began to benefit from:
- Kubernetes self-healing (automatic pod restarts) Service-level health checks Blue/Green or Canary deployments via ArgoCD
- With separate service boundaries, a service can fail independently without affecting the entire system.
- In the event of a complete EKS node failure or regional service impairment, critical functionality is resumed within ~10 minutes due to multi-AZ failover alongside auto-scaling.
Recovery Point Objective (RPO): ~0 to 5 minutes
- Through layer decoupling and data replication using RDS read replicas, the system captures:
- Near real-time backups Utilization of WAL (write-ahead logging) or point-in-time recovery Streaming data via Kafka/EventBridge ensures data in-flight is either persisted or retried
- For non-transactional services (notifications, analytics), a combination of retries and idempotent design helps mitigate data loss.
- Before modernisation, monolith deployment complexity would likely have led to RTO exceeding 1-2 hours. RPO suffered due to no event replay or partial backup mechanism.

Business Impact

Launched a new Buy Now financing option powered by OfferLogix APIs

Rolled out real-time dealer bidding and live auction pricing

Reduced mean time to recovery (MTTR) for bugs by 60%

Created dedicated squads with end-to-end ownership of microservices

Built a platform culture ready for scale, partners, and experimentation

“This wasn’t just a tech upgrade. We moved from one big bottleneck to a nimble, scalable platform, and we’re now shipping weekly without fear.” — CTO, Automotive Marketplace Company

Tech Stack Highlights

Backend: Node.js, Python (FastAPI), Kafka

Container Orchestration: Amazon EKS, Helm

Database: PostgreSQL with read replicas

Auth: AWS Cognito, OAuth 2.0

CI/CD: GitHub Actions, ArgoCD, LaunchDarkly

Monitoring: Prometheus, Grafana, Loki, OpenTelemetry

Security: IAM policies, Secrets Manager, OPA for policy as code

Why It Matters

The shift to microservices isn’t just about fancy terms; it’s about speed, resilience, and flexibility. For this client, modernization wasn’t only technical; it unlocked new business capabilities: