Building Cost-Effective AI Production Systems: A Taco Bell Approach to Cloud Computing
This article explores building cost-effective AI production systems. Drawing parallels to Taco Bell's simplified menu, the author advocates for constructing complex systems using simple, industry-standard components (like S3, Postgres, HTTP). The focus is on minimizing cloud computing costs, particularly network egress fees. By using object storage with zero egress fees (like Tigris) and dynamically scaling compute instances up and down based on demand, costs are dramatically reduced. The importance of choosing dependencies to minimize vendor lock-in is stressed, with an example architecture provided using HTTP requests, DNS lookup, Postgres or object storage, and Kubernetes, allowing for portability across cloud providers.
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