Escaping the "Snowflake Tax": How we cut data costs by over 50% at New Relic

"Managed data warehouses bundle storage, compute, and query into a single model, which means you pay for the package whether you need all of it or not. As our data volumes grew, costs didn't scale proportionally. Every new product launch or increase in event volume triggered step-function cost jumps."

"The guiding principle behind our new stack was clean separation of storage, compute, and query so that each layer can scale independently. This architecture allowed us to maintain performance and reliability while achieving significant cost reductions."

The migration from Snowflake was driven by the need to address cost inefficiencies and vendor lock-in. The previous model resulted in disproportionate cost increases as data volumes grew. The new architecture focuses on separating storage, compute, and query functions, allowing for independent scaling. This change enabled the migration of over 1,000 datasets without disrupting product delivery and achieved a 35-52% reduction in annual data platform costs. The new platform enhances observability and efficiency.

Read at New Relic