Good, Fast, Cheap vs Reliable, Observable, Scalable

What the classic project triangle teaches us about building AI systems today

Most of us know the old adage:
Good. Fast. Cheap. Pick two.

In AI system design, the trade-offs show up differently. Instead of quality, time, and cost, you’re balancing:

• Reliability: the system works as expected.

• Observability: you can see what’s happening inside.

• Scalability: it grows with demand.

When you build software fast and cheap, you often sacrifice:

• Reliability — skipped validation and error handling.

• Observability — missing logging, tracing, or monitoring.

• Scalability — shortcuts that collapse under real workloads.

The catch is simple. Saving time and money up front usually shifts the cost to later. Fixing reliability, adding observability, and rebuilding for scale is more expensive than doing it right at the start.

AI solutions make this more pressing. Insufficient data or silent failures can do more damage than a missed feature.

Question for you:
When you’ve had to choose between speed, cost, and system resilience, which one did you protect first?