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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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.
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 RequestsKnow exactly how many daily requests you need before self-hosting becomes cheaper than APIs. Make data-driven infrastructure decisions.
A100s are expensive but fast. T4s are cheap but limited. Find the GPU that matches your model size and throughput requirements.
See how costs change as you grow from 1,000 to 100,000 daily requests. Avoid surprises when your AI product takes off.
Self-hosted GPUs cost the same whether used or idle. Calculate your utilization to ensure you're not paying for unused capacity.
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.
T4 (16GB): Quantized 7B models only. A10G/L4 (24GB): 7B-13B models with quantization. A100 40GB: Up to 34B models. A100 80GB: Up to 70B models. H100: Best performance for all sizes, required for 180B+ models. Always consider quantization to fit larger models on smaller GPUs.
Low utilization means you're paying for idle GPU time. Consider: batching requests for better throughput, using serverless inference for variable workloads, downsizing to a smaller GPU with sufficient capacity, or running the GPU fewer hours per day if traffic is predictable.
Estimates are based on published cloud pricing and typical inference performance. Actual costs vary by region, spot vs on-demand pricing, negotiated rates, and model-specific optimizations. Use these as a planning baseline and validate with actual benchmarks before committing.
Serverless (like AWS SageMaker Serverless): Best for unpredictable traffic, scales to zero, but ~30% premium. Dedicated/Reserved: 30-70% cheaper for consistent workloads but requires capacity planning. Choose based on your traffic patterns and operational preferences.