Cloud LLM vs Local AI-Agent: Cost Comparison with Real Example
A dozen invoices land in your shared mailbox. An n8n workflow picks up the PDFs and images, a local small language model (SLM) reads each invoice and the workflow files every document by year, month and vendor in your ERP. The invoices never leave your network. What would the same workflow cost with a cloud model? When does your own hardware pay off? This post does the calculation. As we explained in our post on Small Language Models (SLMs): Agentic AI for Your Workflow Automation, focused tasks like invoice sorting do not need the largest models. Our guide to GDPR & AI: Safe Local Hosting for LLMs explains why many companies want to keep invoices in-house.
What a cloud model costs per invoice
Cloud models are billed per token. For one invoice we assume:
- about 1 500 tokens of text after OCR for a one-page invoice
- about 500 tokens of instructions for the model
- about 300 tokens of answer: the extracted fields as JSON
GPT-4 Turbo costs $10 per million input tokens and $30 per million output tokens (OpenAI). One invoice therefore costs about 3 cents. Newer models such as GPT-4o cost less per token.
What changes the bill is the workflow. An agent that reads an invoice, checks the result, classifies it and writes a note calls the model several times per document:
| Workflow | Invoices a month | GPT-4 Turbo a month |
|---|---|---|
| One model call per invoice | 1 200 | about $35 |
| An agent with five calls per invoice | 1 200 | about $175 |
| The same agent | 10 000 | about $1 450 |
Long documents such as contracts or RFPs raise the tokens per document in the same way.
A local AI agent for invoices
S&S Technologies combines the open-source n8n automation platform with an on-prem SLM tuned for document extraction. The result is a governed, end-to-end workflow that:
- Captures invoices from email, SFTP, your file system, NAS or scan devices.
- Extracts text and key-value pairs with a 13-B parameter model running on a single RTX 3060 (12 GB VRAM).
- Classifies each file by year, month and vendor.
- Stores the PDFs in your ERP or DMS and posts an audit trail to your ITSM system.
- Secures every step with RBAC, versioned assets and full logging - no internet needed.
All this runs on a €1 800 workstation (Intel® Core™ i7, 32 GB RAM, Nvidia RTX GPU, 2 TB NVMe). After that, a run costs a few cents of electricity. No additional licences, no per-seat charges, no fee per token.
How the agent processes an invoice
- Ingestion: n8n watches an "Incoming-Invoices" mailbox, file system changes or any other ingress pipeline you use.
- Extraction: The local SLM receives the OCR text and returns structured JSON for each invoice.
- Classification: Using vendor name and invoice date, the workflow builds the path
/Invoices/2025/07/VENDOR_NAME/and writes the original PDF plus metadata.
When local pays off
- With one model call per invoice and 1 200 invoices a month, the cloud costs about $35 a month. The workstation would need years to pay for itself. Here the reason to run locally is the data, not the price.
- With an agent that calls the model five times per invoice, the cloud costs about $175 a month and the workstation pays for itself in about a year.
- At 10 000 invoices a month, the same agent costs about $1 450 a month in the cloud. The workstation pays for itself within two months.
Besides the price:
- Data stays in your company, which helps with the GDPR and internal audits.
- No rate limits or price changes of a provider when the finance team closes a period.
- Built-in governance: versioned workflows, RBAC, tamper-proof logs.
Is a local LLM right for you?
Start by checking whether your workflow...
- Processes many or long documents every day (e.g., invoices, contracts, RFPs)
- Calls the model several times per document, as agents do
- Contains personal or financial data that must stay on-prem
- Suffers from rate-limit spikes or unpredictable API spend
If you tick two or more boxes, local AI is worth a closer look.
Offer: S&S Technologies will run a 2-week pilot: we measure the token volume and cost of your current workflow, deploy a local agent and hand over a cost model plus roadmap.
Automate. Optimize. Scale.
Contact our team to reserve a slot this quarter.