How Mechademy Cut Hybrid Digital Twin Infrastructure Costs by 87% After Migrating from MongoDB to Tiger Data

Mechademy, which monitors crucial assets for large energy companies, dramatically reduced its hybrid digital twin infrastructure costs by 87% after transitioning from MongoDB to Tiger Data, powered by TimescaleDB on AWS. Initially, Mechademy used MongoDB for its flexibility and rapid iteration capabilities, crucial for developing its Turbomechanica platform that fuses physics-based models with machine learning to create digital twins for industrial assets. However, as the need for scalable diagnostics grew, MongoDB's lack of native time-series data support led to operational and financial strain, with high CPU utilization and expensive maintenance. By switching to Tiger Data, Mechademy leveraged native time-series support, continuous aggregates, and compression to streamline data handling, resulting in a 50× increase in workload capacity and significantly reduced maintenance overhead. This strategic transformation not only cut costs and complexity but also allowed Mechademy to focus on delivering intelligence rather than managing infrastructure.