Compare serverless pay-per-token pricing against a dedicated, always-on GPU instance and find the monthly cost, cost per request, and break-even traffic volume between them.
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Quick Facts
Method
Per-request serverless pricing vs. a fixed-cost, always-on GPU
Dedicated GPU cost assumes 730 billed hours/month (365 × 24 ÷ 12), the standard cloud-billing month.
Results
Calculated
Serverless cost
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$/month, pay-per-token
Dedicated GPU cost
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$/month, always-on instance
Break-even volume
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Daily requests where costs match
Savings from cheaper option
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$/month vs. the pricier option
Ready
Enter your traffic and pricing assumptions, then press Calculate.
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Understanding ML inference cost: serverless vs. dedicated GPU
Running a machine learning model in production usually comes down to two pricing models. Serverless (pay-per-token) inference bills you per request based on how many tokens you send and receive, with no fixed monthly floor. A dedicated GPU instance bills a flat hourly rate for the machine whether or not it's actively serving traffic. This calculator estimates the monthly cost of each option for the same traffic and finds the break-even request volume where they cost the same.
Dedicated GPU monthly cost = $/hour × 730 hours (a standard billing month, assuming the instance stays on continuously).
Break-even volume = dedicated monthly cost ÷ serverless cost per request ÷ 30, giving the daily request count at which both approaches cost the same.
Below the break-even volume, the fixed hourly cost of a dedicated GPU is spread over too few requests to beat pay-per-token pricing, so serverless tends to be cheaper. Above it, the GPU's per-request cost keeps falling as traffic grows while serverless cost scales linearly with usage, so the dedicated instance tends to win — provided it has enough throughput to keep up with demand.
Understanding the inputs
Enter your expected requests per day and the average tokens per request (prompt plus completion combined — 1,000 tokens is roughly 750 words of English text). For the serverless side, enter the provider's price per 1,000,000 tokens (blend input and output pricing into one average rate, or use the output rate if it dominates your workload). For the dedicated side, enter the GPU's hourly rental cost and its realistic throughput in requests per hour — this determines whether a single GPU instance can actually absorb your traffic.
Interpreting the results
The two highlighted cards show the total monthly cost under each pricing model for your traffic. The break-even card shows the daily request volume at which both options cost the same — compare it with your actual or forecast daily requests to see which side of the line you're on. The last card shows how much the cheaper option saves per month at your current inputs. This is a cost-structure estimate, not a performance benchmark: it does not account for latency, cold starts, GPU utilization below 100%, or engineering overhead of self-hosting.
Frequently Asked Questions
How is the break-even point calculated?
The break-even point is the daily request volume where serverless and dedicated-GPU costs are equal. It is found by dividing the dedicated GPU's monthly cost (hourly rate times 730 hours) by the serverless cost per request (tokens per request divided by 1,000,000, times the price per million tokens), then dividing by 30 days.
Why assume the GPU runs 730 hours a month?
730 hours is the standard cloud-billing approximation for a full month (365 days x 24 hours / 12 months). A dedicated or reserved GPU instance is normally billed for uptime whether or not it is actively serving requests, so this calculator assumes it stays on continuously. If you can autoscale the GPU down during idle periods, your real dedicated cost will be lower than shown here.
What counts as tokens per request?
Use the combined input and output tokens for a typical request: the prompt plus the generated response. Token counts vary by model and tokenizer, but a common rule of thumb is that 1,000 tokens is roughly 750 words of English text.
Does this include other cloud costs?
No. This calculator compares only the two headline costs, per-token serverless pricing and per-hour GPU rental, to estimate a break-even volume. It does not include storage, networking, load balancing, cold-start overhead, or engineering time, all of which can shift the real-world break-even point.
Practical Guide for ML Model Inference Cost Calculator - Serverless vs Dedicated GPU Pricing
ML Model Inference Cost Calculator - Serverless vs Dedicated GPU Pricing is most useful when the inputs reflect the situation you are actually planning around, not a best-case estimate. Treat the result as a decision aid: it gives you a structured way to compare assumptions, spot outliers, and decide what to verify next. For Other work, the most important review lens is baseline behavior, time cost, throughput, constraints, friction, and the decision threshold you care about.
Start with a baseline run using values you can defend. Then change one assumption at a time and watch which output moves the most. If one input dominates the result, spend your verification time there first. If several inputs have similar influence, use a conservative scenario and an optimistic scenario to create a practical range instead of relying on a single exact number.
Before acting on the result, compare the result with recent real-world data instead of ideal targets or one-off examples. This is especially important when the calculator supports a purchase, project plan, performance target, or operational decision. The calculator can make the math consistent, but the quality of the conclusion still depends on current data, clear units, and assumptions that match your real constraints.
When the output looks surprising, slow down and inspect each input in order. A small change in one high-leverage field can move the final number more than several low-leverage fields combined. For ML Model Inference Cost Calculator - Serverless vs Dedicated GPU Pricing, that means you should first confirm the value with the greatest scale, then confirm the value with the greatest uncertainty, then rerun the calculator with conservative and optimistic assumptions. This sequence turns the calculator from a single answer into a practical decision range.
Review Checklist
Confirm every input uses the unit and time period requested by the calculator.
Run a low, expected, and high scenario so the answer has a useful range.
Check whether rounding or a missing decimal place changes the decision.
Update the calculation after each meaningful workflow, schedule, cost, or usage change.