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Re-architecting Affirm's Upfunnel Platform: How We Cut Experiment Cycle Time from Months to Days
Affirm reduced experiment cycle times from months to days by replacing a legacy Python monolith with a rule-based Kotlin microservice.
What: Affirm re-architected its Upfunnel platform to allow non-engineering teams to configure marketing experiments via rules against existing data attributes, rather than requiring custom code deployments for every new experiment.
Why it matters: Decoupling business logic from the core application code significantly increases development velocity and reduces the risk associated with production releases.
Original article
Affirm rebuilt its pre-checkout messaging service from a Python monolith into a Kotlin microservice with a rule engine, cutting P99 latency by 50% and reducing experiment setup time from 2 months to 4 days since adding a new experiment means writing a rule against existing attributes instead of engineering a new code path and waiting for deployment.
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