The Great Rollback: Why We Migrated 20 Microservices to a Modular Monolith
For the last decade, "microservices" was the default answer to every architectural scaling question. But as engineering teams mature, the hangover of distributed systems complexity has set in. Prime Video made waves when they famously saved 90% by reverting to a monolith. Recently, we helped a Series B FinTech startup do exactly the same thing.
Here is the engineering story of how we consolidated 20 sprawling AWS ECS microservices back into a single, high-performance Modular Monolith on EKS—cutting their cloud spend by 50% and doubling their deployment velocity.
The Microservices Trap
When we first audited the client's architecture, we found a classic case of premature optimization. A team of just 15 backend engineers was maintaining 20 different microservices.
While the system looked incredibly sophisticated on an architecture diagram, the reality of operating it was a nightmare:
- Network Latency: A single customer checkout request required synchronous HTTP calls across 5 different services. The cumulative serialization/deserialization and network overhead added 800ms of baseline latency.
- Infrastructure Bloat: Each service had its own CI/CD pipeline, its own ECS cluster allocation, its own RDS instance, and its own base memory footprint. They were paying for 20x the idle compute and connection pooling overhead.
- Deployment Gridlock: Because the services were tightly coupled in functionality (but loosely coupled in infrastructure), deploying a new feature required orchestrated PRs across 4 different repositories. "Independent deployment" was a myth.
Designing the Modular Monolith
The solution was not to write a big ball of mud. Instead, we designed a Modular Monolith. The code would run in a single process, deployed as a single artifact, but internally enforced strict domain boundaries.
Key Architectural Shifts
Network calls between services (HTTP/gRPC) were replaced with direct, type-safe function calls within the same memory space. Latency dropped to zero.
We used workspace tooling (Nx) and strict linting rules to ensure that modules could only communicate through explicitly exported interfaces, preventing spaghetti code.
Instead of 20 small, overprovisioned RDS instances, we consolidated to a single, highly-available Aurora PostgreSQL cluster with logical schema separation.
A single GitHub Actions pipeline deploying a single Docker image to EKS. Feature testing became trivial using local Docker Compose.
The Migration Execution
Migrating live transactional systems without downtime requires precision. We executed the strangler fig pattern in reverse.
- The Monolith Shell: We created the new modular monolith application shell and configured the API gateway to begin routing traffic to it.
- Module by Module: We took the lowest-risk microservices (like Notifications and User Preferences) and moved their logic into the monolith as isolated modules.
- Data Syncing: For each service, we set up AWS DMS (Database Migration Service) to replicate data from the microservice's isolated database to the new unified Aurora cluster in real-time.
- Flipping the Switch: Once data was synced and the module was heavily tested, we updated the API gateway routing layer to point traffic for that domain to the monolith, decommissioning the old microservice.
The Impact
After 12 weeks of methodical migration, the results fundamentally transformed the engineering organization:
- 📉 Cloud Spend: AWS bill dropped by 50% ($42k to $21k/mo) by eliminating network egress costs between VPCs, ALBs for internal routing, and database fragmentation.
- ⚡ Performance: P99 API latency dropped from 950ms to 120ms by removing network hops and serialization.
- 🚢 Developer Velocity: PRs were merged and deployed 3x faster. Developers could finally run the entire application locally on their laptops.
The Takeaway
Microservices solve organizational scaling problems, not technical scaling problems. If you do not have hundreds of engineers working across distinct, autonomous teams, you likely do not need a microservices architecture.
A well-structured Modular Monolith provides the perfect balance of codebase maintainability, deployment simplicity, and raw performance for 99% of growing SaaS companies.
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