Vinay Hiremath

The cost-latency Pareto frontier: why most LLM systems are expensive and slow Every architectural decision in an LLM-backed system moves you along a cost-latency curve. You cannot optimize both at the same time. It's a frontier. You pick a point on it. But most teams don't think this way. They optimize cost in one meeting, latency in another, and quality in a third. Then they ship, and they're surprised when the system is expensive, slow, or both. Here's the framework: 1. Map the frontier Run experiments. Vary model size, context length, retrieval depth. Measure cost, latency, and quality for each configuration. Plot the results. This is your frontier. 2. Define your constraints Cost budget per request. Latency SLO. Quality floor. These are non-negotiable. If you don't have them, you can't make decisions. 3. Pick your point Given your constraints, where on the frontier do you need to live? If cost is the binding constraint, move toward cheaper models. If latency is binding, move toward smaller models and less retrieval. 4. Design the architecture Model routing. Context pruning. Retrieval depth. Caching strategy. Everything flows from the point you picked. The architecture is the implementation of the trade-off. 5. Measure and adjust The frontier shifts. Model pricing changes. Latency profiles evolve. Re-measure quarterly. If you've moved off your target point, re-architect. This is what production-grade architecture looks like. It's not "use the best model." It's "use the model that fits the constraints." The constraint is the decision. The model is just the tool. --- If you're preparing for interviews and want structured breakdowns like this, I've built a focused playbook for experienced engineers. (Link in comments)

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