Stop Guessing. Predict the True Cost of Your AI Stack.
The AI Infrastructure Cost Calculator: Model TCO for training, inference, and data across cloud and on-prem environments.
The 5x-10x Budget Overrun Problem
Enterprises consistently underestimate AI costs due to hidden expenses like data labeling, model-as-a-service integration fees, continuous GPU underutilization, and network egress. This calculator forces complete clarity before you commit millions.
Sample Cost Breakdown
Why This Calculator Is Different
Move beyond simple cost-per-token estimates. Model the complete financial picture of AI infrastructure deployment.
Cost-Per-Accepted-Output
Not all AI outputs are accepted by users. Calculate true value delivered per completion, not just tokens generated.
GPU Optimization Analysis
Identifies underutilization waste and models performance-scaled throughput for accurate cost projections.
Power & Cooling Modeling
Includes PUE, power density, amortization, and maintenance for accurate on-prem TCO calculations.
Hidden Cost Visibility
Surfaces often-ignored expenses: realistic labeling costs, egress, compliance overhead (20-40%), and redundancy.
How It Works
Model & Scale
Define your model type, user base, and inference volume
Compute Configuration
Choose cloud vs. on-prem and configure GPU infrastructure
Data & Operations
Add training examples, labeling costs, and MLOps platform fees
Get Your TCO Report
Receive detailed cost breakdown and benchmark comparisons
AI Infrastructure Cost Calculator
Models the costs most calculators miss: utilization waste, egress, labeling, compliance, redundancy, and cost-per-accepted-output.
Quick Start: Use a Preset Configuration
Presets load typical configurations. Customize all parameters in the steps below.
Step 1 of 4
Model & Scale Configuration
Number of AI completions requested per day
What percentage of AI completions are actually accepted/used by end users?
Training complexity (parameters, FLOPs, epochs)
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Frequently Asked Questions
How accurate is this AI TCO calculator?
The calculator uses current market rates for GPU pricing, performance-scaled throughput calculations, and industry standard benchmarks. Cost estimates are typically accurate within ±15-20% for cloud deployments and ±20-30% for on-premises, depending on specific vendor negotiations and regional variations.
What makes cost-per-accepted-output different from cost-per-token?
Cost-per-token measures all AI outputs generated, while cost-per-accepted-output only counts completions actually used by end users. Since not all AI outputs are accepted (typical rates: 60-85%), this metric gives a more accurate picture of true value delivered per completion and helps identify optimization opportunities in model quality and user acceptance.
Should I choose cloud or on-premises for my AI infrastructure?
Cloud is generally better for variable workloads, rapid scaling, and when avoiding upfront capital expenditure. On-premises becomes more cost-effective at sustained 24/7 utilization above 70-80%, typically breaking even in 8-18 months when including amortization, power, and maintenance. Consider cloud for inference spikes and development, and on-prem for stable production loads where utilization stays high.
What are the hidden costs of AI infrastructure not included in basic estimates?
Key hidden costs include: realistic data labeling (often 20-40% of training budget based on actual example counts), network egress fees ($0.08-0.12/GB), GPU underutilization waste, MLOps platform subscriptions, compliance and auditing overhead (25-40% for HIPAA/FINRA/FedRAMP), redundancy requirements (15-30% for N+1 or N+2), cooling and power for on-prem (PUE typically 1.2-2.5x raw power), and ongoing maintenance (12% annual of capex).
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