Interactive calculator compares local LLM hardware costs to cloud API pricing over time

The tool frames a classic build-versus-buy decision for LLM inference. It converts upfront hardware costs into a time-to-payback metric by comparing them against the cumulative cost of equivalent API calls. Engineers can adjust workload parameters like tokens per day and context window size to match their expected usage patterns. The model also lets users specify how quickly they expect API prices to fall, which can dramatically extend or shorten the break-even period.

Local inference speed is treated as a critical but uncertain variable. Where no benchmarks exist, the calculator estimates it from memory bandwidth and bytes read per token. This introduces a wide error margin, since real-world performance depends on factors like kernel optimizations and batching strategies that aren’t captured in the simplified model. API speed, by contrast, only affects the time comparison, not the cost calculation, so its impact is narrower.

The interactive interface makes the trade-offs visible. Moving any slider, electricity cost, tokens per day, or API price decline, immediately updates the payback timeline. This exposes how sensitive the outcome is to each assumption. For example, a small change in expected API price decline can shift the break-even point by months or even years. The tool doesn’t declare a winner; it shows how the answer depends on variables that are often overlooked in casual comparisons.

The calculator’s limitations are as instructive as its outputs. It doesn’t account for operational overhead like hardware maintenance, cooling, or software updates. It also assumes a fixed workload, ignoring potential economies of scale if usage grows over time. Most critically, it treats local and cloud inference as functionally equivalent, when in practice they differ in latency, reliability, and feature support. These gaps highlight where real-world decisions still require qualitative judgment beyond the numbers.