Why We Use Separate Tech Stacks for Personalization and Experimentation
Spotify EngineeringJanuary 7, 2026
Why It Matters
Separating these stacks allows engineering teams to optimize for specific performance and reliability needs. It reduces architectural complexity, ensuring that ML-driven personalization doesn't compromise the statistical validity of A/B testing frameworks.
Key Takeaways
- •Spotify maintains distinct technical stacks for personalization and experimentation to address their unique operational requirements.
- •Personalization systems are optimized for low-latency model inference and high-throughput content delivery.
- •Experimentation infrastructure focuses on statistical validity, randomized assignment, and unbiased metric analysis.
- •Decoupling these domains prevents architectural complexity and avoids the pitfalls of a monolithic 'one-size-fits-all' solution.
- •Independent stacks allow teams to scale infrastructure based on specific data lifecycles and performance bottlenecks.
Keywords
PersonalizationExperimentationA/B TestingMachine LearningInfrastructureScalabilityData Lifecycle
Content Preview
The technical and practical rationale for a clear separation between these domains.
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