Monolith versus microservices
Value in software architecture is mainly linked to cost, both initial and ongoing. Launching a monolithic generative AI project is often more budget-friendly, quicker, and simpler. There are fewer technologies to learn, less operational complexity, and only one application to oversee and maintain. In the early stages or for specific use cases, this simplicity can be a strategic advantage: Features develop quickly, and changes can be thoroughly tested.
As AI systems grow and improve, the monolithic approach begins to yield diminishing returns. The cost of updating parts increases, risks multiply as codebases expand, and full-system redeployments become routine, slowing innovation and raising the chance of outages. Debugging and testing also become more challenging, especially with large and complex pipelines.
Switching to microservices initially increases many costs. Teams need to invest in orchestration platforms, secure inter-service networks, strong observability, and continuous integration pipelines. The required skills (containerization, distributed tracing, and fault tolerance) are expensive. The complexity often overshadows the simplicity of earlier monolithic systems. However, this complexity serves as the entry fee for future benefits such as flexibility, isolation, and rapid scaling. To justify these costs and complexities, there must be a readily apparent and lasting reason for evolving components independently and building in the flexibility to scale specific capabilities.
