# Total Cost of Ownership (TCO) of Open vs. Closed AI Models

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# Total Cost of Ownership (TCO) of Open vs. Closed AI Models

When integrating Large Language Models (LLMs) into enterprise applications, the initial development phase is just the tip of the iceberg. To build a sustainable strategy, the Total Cost of Ownership (TCO) must be mapped out. The fundamental choice here is: do you use closed models via an API, or do you host open-weight models yourself?

## 1. Cost Items of Closed Models (API-based)

Using commercial APIs has low entry costs and requires minimal technical infrastructure. However, costs scale linearly or even exponentially with usage.

- API Usage: Billed per million tokens (input and output). With RAG (Retrieval-Augmented Generation), input tokens quickly add up due to the required context.

- Network & Latency: Reliance on external servers can cause delays; data egress costs may play a role.

- Vendor Lock-in: Price increases or model deprecations by the provider are risks beyond your control.

## 2. Cost Items of Open Models (Self-Hosted)

Open models offer control over data, privacy, and performance, but shift costs from operational (SaaS) to infrastructure and expertise.

- Compute & GPU Hosting: Purchasing physical hardware (HPC setups) or cloud-based GPU instances (e.g., H100 or A100).

- Power Consumption & Cooling: A significant variable cost item for on-premises hosting.

- Expertise & MLOps: Costs for specialized engineers for model deployment, fine-tuning, load balancing, and maintenance.

Tip: Is your organization struggling with this trade-off? Consult our [consultancy guide for AI strategy](https://consultancy.llmnet.nl/en/) to calculate your specific use case.

## 3. The Tipping Point: When does the balance shift?

The tipping point between open and closed models depends on token volume. For low volumes (prototyping, internal tools for small teams), the closed API almost always wins. Only with substantial, continuous inference (for example, thousands of queries per hour) or when strict data privacy is required, are the high fixed costs of GPU hosting and expertise offset by the absence of pay-per-token API costs.

## 4. Example Calculation Setup (Monthly TCO)

Use the table below to make your own projections. Fill in real quotes or market averages for the placeholders.

Cost Item | 
Closed Model (API) | 
Open Model (Self-hosted) | 

Token Volume [X] billion/month | 
[API COST AMOUNT] | 
€ 0.00 | 

Cloud GPU (Rental) or Hardware Depreciation | 
€ 0.00 | 
[GPU HOSTING AMOUNT] | 

Power & Datacenter [ESTIMATE] | 
€ 0.00 | 
[POWER/OPEX AMOUNT] | 

Maintenance & MLOps Hours | 
[API MANAGEMENT AMOUNT] | 
[MLOPS SALARY AMOUNT] | 

Total estimated (per month) | 
[TOTAL CLOSED] | 
[TOTAL OPEN] | 

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