AI Inference Cost Calculator
Calculate AI inference costs for self-hosted GPUs vs cloud APIs. Compare NVIDIA A100, H100, T4 costs, analyze break-even points, and find the most cost-effective deployment for your ML workloads.
This calculator compares self-hosted GPU inference costs against equivalent API pricing to help you decide the most cost-effective deployment strategy.
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Self-Hosted GPU vs API: Which Is Cheaper?
Running AI inference at scale? Our calculator compares the total cost of self-hosted GPU infrastructure against API-based services like OpenAI and Anthropic. Find your break-even point and choose the most cost-effective deployment strategy.
Understanding Inference Costs
AI inference costs depend on your deployment model. Self-hosted GPUs have fixed hourly costs regardless of utilization, while APIs charge per token. At low volumes, APIs are cheaper. At high volumes, self-hosting can save 50-80%. The break-even point varies by model size and GPU choice.
Cost Per Inference Formula
Self-Hosted Cost/Inference = (GPU Cost/Hour × Hours) ÷ Daily RequestsWhy Compare Inference Costs?
Find Your Break-Even Point
Know exactly how many daily requests you need before self-hosting becomes cheaper than APIs. Make data-driven infrastructure decisions.
Right-Size Your GPU
A100s are expensive but fast. T4s are cheap but limited. Find the GPU that matches your model size and throughput requirements.
Plan for Scale
See how costs change as you grow from 1,000 to 100,000 daily requests. Avoid surprises when your AI product takes off.
Optimize Utilization
Self-hosted GPUs cost the same whether used or idle. Calculate your utilization to ensure you're not paying for unused capacity.
How to Use This Calculator
Frequently Asked Questions
Self-hosting typically becomes cost-effective above 10,000-50,000 daily requests, depending on model size. Consider self-hosting if you have predictable, high-volume workloads, need data privacy, or require custom models. APIs are better for variable traffic, rapid prototyping, or when you lack ML ops expertise.