Cost & FinOps / ROI Frameworks
Total Cost of Ownership calculator for LLM deployment
This calculator estimates the total cost of ownership (TCO) for large language model deployments, comparing API usage, self-hosted infrastructure, and fine-tuning approaches. It helps enterprise AI buyers and platform engineering leads evaluate costs based on usage, model scale, and operational factors.
Enterprise teams deploying large language models face critical choices that impact cost and operational complexity. This calculator quantifies total cost of ownership for three common LLM deployment options: API-based consumption, self-hosted infrastructure, and fine-tuning of base models.
Input your expected usage, deployment scale, and infrastructure considerations to compare costs on a case-by-case basis. Use this data to support budgeting, procurement, and architectural decisions.
Inputs
Average number of tokens you expect to send and receive each day using API calls.
Cost charged by API provider per 1,000 tokens processed. Typically ranges from $0.0004 to $0.02 based on model and tier.
Number of GPUs provisioned for hosting the LLM on-premises or in cloud infrastructure.
Cost to run one GPU per hour, factoring hardware depreciation, power, cooling, and cloud pricing.
Average hours per day the GPUs will be utilized to serve the LLM.
Amount of training data used for fine-tuning the base LLM model.
Estimated GPU hours needed to complete the fine-tuning training process.
Cost per GPU hour specifically for fine-tuning workloads, which may differ from serving costs.
Number of tokens processed daily from the fine-tuned model when deployed.
Number of GPUs allocated to serve the fine-tuned model.
Cost per GPU hour for serving the fine-tuned model in production.
Average number of hours per day GPUs will serve the fine-tuned model.
Results
(daily_api_tokens / 1000) * api_cost_per_1k_tokens * 30self_hosted_gpu_count * gpu_hourly_cost * self_hosted_utilization_hours_per_day * 30fine_tune_compute_hours * fine_tune_gpu_hourly_costpost_fine_tune_gpu_count * post_fine_tune_gpu_hourly_cost * post_fine_tune_gpu_utilization_hours_day * 30fine_tune_cost + fine_tuned_serving_monthly_costSummary: Choose the most cost-effective LLM deployment
Cost Below $5,000API deployment is likely the most cost-effective for usage below these levels. Self-hosting or fine-tuning may provide savings at higher scale depending on infrastructure costs.
Note
This calculator does not include additional operational costs such as data storage, model maintenance, security compliance, or human labor. Actual costs can vary significantly based on workload patterns and vendor pricing updates.
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