Why Your Service Mesh Implementation Is Probably Overengineered

The assumption that cloud computing scales infinitely and remains cost-effective at all scales has been challenged by organizations running massive batch processing workloads. Some companies found that compute costs in their public cloud bills remained flat despite aggressive scaling efforts, pointing to performance bottlenecks that predated infrastructure. Others concluded that periodic usage spikes did not justify continuous scaling capabilities.

Systems designed around a single database instance that has grown to handle terabyte-scale datasets face fundamental queries-per-second and latency limitations. Sharding, replication, and caching strategies can extend capacity but introduce significant operational complexity. Organizations at this point typically face a choice between substantial re-architecture or accepting constrained application performance.

Cloud vendor lock-in discussions often occur years after infrastructure decisions become practically irreversible through accumulated integrations and architectural dependencies. By the time organizations recognize lock-in, migrating away would require rebuilding substantial portions of their technology stack. This timing problem means lock-in prevention strategies must be implemented during initial infrastructure design.

Healthcare systems deploying machine learning models for diagnostic support are increasingly choosing on-premises infrastructure over cloud services. The decision often stems from latency requirements, data sensitivity, and regulatory considerations around medical records. One major health system reduced inference time to under 200 milliseconds by deploying models locally while maintaining integration with broader clinical systems.

Organizations supporting systems built 10 to 15 years ago face a peculiar problem: the technical knowledge required to maintain them has scattered across the industry. Documentation is often incomplete, original architects have moved on, and the cost of replacement remains prohibitive. Some teams estimate that maintaining legacy systems consumes 30 to 40 percent of their development capacity.

This is part of an ongoing series. Previous installments are linked below.