The Challenge of Variable Inference Demand
Production machine learning systems rarely experience uniform traffic. Real‑world request rates ebb and flow with daily cycles, marketing events, and viral surges. Provisioning enough hardware to withstand the highest spike results in waste during idle periods, yet provisioning too little risks latency violations. Autoscaling attempts to balance this trade‑off by dynamically adding or removing instances according to demand. But autoscaling is not free: new instances incur a cold‑start delay, and transient spikes may still overload the service before scaling reacts. Moreover, switching instances on and off affects the monthly budget in ways that can be difficult to reason about without a quantitative model.
The calculator above models a simple autoscaling scenario. You specify baseline and peak request rates, the number of hours per day spent at the peak, the throughput capacity of a single inference instance, the cold start time for bringing a new instance online, the hourly cost of that instance, and the number of days per month to analyze. With these inputs, the script estimates how many instances must run at baseline, how many are required during peak windows, the extra latency users may experience from cold starts, and the difference in monthly cost between an autoscaled fleet and a fleet sized for the peak and kept running continuously.
Instance Sizing
The baseline number of instances is computed as , where is baseline requests per second and is per‑instance capacity. Similarly, the peak requirement is . The difference represents additional instances that must be launched during peak hours. The calculator rounds up to ensure throughput meets or exceeds demand, avoiding saturation.
Cost Modeling
Let denote total hours in the month and the daily hours spent at peak. Autoscaling cost is the sum of two parts: baseline instance cost and incremental peak cost , where is the hourly price per instance and the number of days. The total is . By contrast, keeping peak capacity running nonstop would cost . The savings from autoscaling are . These equations allow quick evaluation of whether the complexity of autoscaling pays off in dollars.
Cold Start Latency
When demand rises, autoscaling systems provision new instances, each incurring a cold start delay . Not every request experiences this delay; it affects only those routed to an instance that is still warming up. Assuming a fraction of peak requests hit cold starts, the average additional latency is . This simplistic model offers a first‑order estimate; real systems may see more complex latency distributions due to queuing effects, autoscaling reaction times, and network jitter. Nevertheless, including in the output helps highlight the user experience cost of aggressive scale‑to‑zero strategies.
Requests and Cost per Million
The total number of requests per month can be derived as . Dividing the autoscaling cost by yields cost per million requests, a convenient metric for comparing models or providers. Cloud platforms frequently quote prices in dollars per million tokens or requests; this calculator empowers teams to derive such numbers from their own traffic patterns.
Example Calculation
| Metric | Value |
|---|---|
| Baseline Instances | 1 |
| Peak Instances | 2 |
| Autoscaling Monthly Cost | $1,680 |
| Always‑On Monthly Cost | $1,440 |
| Savings | -$240 |
| Average Cold Start Latency | 7.5 s |
| Cost per Million Requests | $0.56 |
The table illustrates that autoscaling is not universally cheaper. With the provided numbers, running peak capacity around the clock actually costs less because the peak period consumes a large fraction of the day and cold starts add substantial latency. Adjusting the hours of peak traffic, instance cost, or throughput can swing the equation in favor of autoscaling. The calculator encourages experimentation to identify break‑even points.
Design Considerations
Autoscaling strategies vary widely. Some systems maintain a warm pool of idle instances to absorb sudden bursts without cold start delays. Others rely on predictive scaling driven by historical patterns. The calculator assumes a reactive model with no warm buffer, but you can approximate warm pooling by increasing the baseline instance count. It also omits provider‑specific billing quirks such as minimum billing durations or tiered pricing, which may influence the cost comparison.
Beyond GPUs
While the example focuses on GPU‑backed inference, the same reasoning applies to CPU services, edge devices, or hybrid deployments. In serverless environments, pricing often scales linearly with request volume rather than instance‑hours, rendering this analysis less relevant. However, many large language model deployments require dedicated accelerators and thus benefit from careful autoscaling planning.
Limitations
The model presented here simplifies numerous real‑world complexities. It assumes immediate scaling with a fixed cold start time, ignores queuing delays during ramp‑up, and treats request rates as deterministic rather than stochastic. It also assumes that peak demand occurs in a single contiguous window each day. For more accurate predictions, teams should instrument their systems, gather histograms of request arrival times, and simulate autoscaling policies. Nonetheless, this calculator serves as an accessible starting point for understanding how usage patterns translate into infrastructure spend and latency.
Conclusion
Autoscaling can transform the economics of machine learning inference, but only when traffic patterns and hardware characteristics align favorably. By capturing the key variables—request rates, capacity, costs, and latency penalties—this calculator helps practitioners reason about the trade‑offs. Use it to communicate budgets to stakeholders, decide whether to pre‑warm instances, or justify investments in optimization work that reduces per‑request latency.