Real Cost of AI: OpenAI API vs Local Inference with Real Numbers

How much does it cost to use artificial intelligence to process documents? The answer depends radically on how you implement it.

We compared two scenarios with real numbers: using commercial model APIs (OpenAI, Anthropic, Google) vs running AI models on your own local server.

Scenario A: Commercial APIs

AI providers charge per token — the basic processing unit. Reference prices (as of June 2026) for high-capacity models are in the range of $2-10 USD per million input tokens and $10-30 USD per million output tokens.

For an operation processing 1,000 legal documents per month (10 pages average), estimated consumption is 3.5 to 5 million monthly input tokens, plus generated output.

Estimated monthly cost: between $25 and $150 USD depending on the model and processing complexity. Sounds low, but there's a hidden factor we documented in our tokenization study: if your documents are in Spanish, you pay between 22% and 67% more in tokens than an equivalent document in English, simply due to how tokenizers work.

Additionally, with APIs you pay per use with no ceiling. If your volume grows, your cost grows proportionally. And your documents travel to third-party servers for processing.

Scenario B: Local server

A server capable of running AI models has a hardware acquisition cost (GPU with 16GB+ VRAM, CPU, RAM, storage) that amortizes over 24-36 months.

Monthly operating cost reduces to electricity (variable by usage, but in Mexico with CFE commercial rates, we're talking about a few hundred pesos in additional monthly consumption), software maintenance (updates, monitoring), and connectivity.

No per-token cost. No per-language cost. No volume ceiling. And documents never leave your network.

When each option makes sense

APIs make sense when your volume is low (fewer than 100 documents per month), when you lack the technical capacity to maintain a server, or when you need access to the most powerful available model (GPT-4, Claude Opus) and the quality justifies the cost.

Local inference makes sense when your volume is medium or high, when you process sensitive documents (legal, medical, fiscal), when you need predictable costs, and when the language tokenization bias affects you economically.

The crossover point

For most operations processing Spanish-language documents at medium volumes, the crossover point — where local inference becomes more economical than APIs — is reached surprisingly fast, generally between 3 and 6 months of operation, depending on the chosen hardware.

Complete methodology and detailed cost analysis available at theprivatestack.com.