Microservices Migration Cost Model
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Determine the additional cost premium of migrating to a microservices architecture given uncertainties in refactoring scope.
NOTEAbout This Model
## Determine the additional cost premium of migrating to a microservices architecture given uncertainties in refactoring scope. This model estimates **Migration Cost Premium** based on uncertain inputs: Refactoring Scope Uncertainty, Microservices Cost Multiplier. Fixed values: Monolith Cost Base. Run the simulation to see the distribution of possible outcomes.
CONSTANTMonolith Cost Base
100000
Baseline cost estimate for the existing monolithic system or initial migration estimate.
monolith_cost_base
VARIABLERefactoring Scope Uncertainty
beta(10, 10)
Refactoring scope is notorious for expanding once the migration begins; modeled as a percentage of base cost.
refactoring_scope_uncertainty
VARIABLEMicroservices Cost Multiplier
triangular(1.5, 2.25, 3)
Industry data suggests microservices architecture adds 1.5x to 3x complexity and infrastructure overhead.
microservices_cost_multiplier
FORMULAAdjusted Monolith Cost
monolith_cost_base * (1 + refactoring_scope_uncertainty)
Estimated costs for keeping and maintaining the existing monolith during the migration period.
adjusted_monolith_cost
FORMULAMicroservices Migration Cost
monolith_cost_base * microservices_cost_multiplier * (1 + refactoring_scope_uncertainty)
Estimated costs for microservices architecture, factoring in the inherent complexity multiplier and refactoring scope.
microservices_migration_cost
FORMULAMigration Cost Premium
microservices_migration_cost - adjusted_monolith_cost
The cost premium of migrating to microservices compared to maintaining the monolith.
migration_cost_premium
OUTPUTMigration Cost Premium
migration_cost_premium
The additional cost of migrating to microservices compared to maintaining the monolith. Positive values mean migration is more expensive upfront; weigh against long-term operational savings.
What is Carlo?
Carlo is a visual tool for Monte Carlo simulation. Model uncertainty by dragging probability distributions, connecting them visually, and running thousands of scenarios instantly.