The question shows up in almost every AI adoption conversation: is buying your own hardware actually cheaper than paying per token, or is it an upfront investment that takes years to recover? The answer depends entirely on your usage volume. This article runs the numbers across three years.
What Cloud LLM APIs Actually Cost
The major providers bill by token. For calibration: 1 million tokens is roughly 750,000 English words — around 1,000 pages of standard office text.
Current API prices per official provider pricing pages, spring 2026:
| Model | Input / million tokens | Output / million tokens |
|---|---|---|
| GPT-4o | $2.50 | $10.00 |
| GPT-4o mini | $0.15 | $0.60 |
| Claude Sonnet 4.6 | $3.00 | $15.00 |
| Claude Haiku 4.5 | $1.00 | $5.00 |
| Claude Opus 4.7 | $5.00 | $25.00 |
For classification and short-answer tasks, Haiku or GPT-4o mini cut costs substantially. For complex document analysis, multilingual tasks, or structured data extraction, most SMBs end up using GPT-4o or Claude Sonnet in practice.
Reference Calculation: 10-User SMB
Assumption: 10 employees, 100 AI requests each per working day (250 days/year), averaging 1,000 input tokens and 500 output tokens per request.
Annual volume:
- Input: 10 × 100 × 250 × 1,000 = 250 million tokens
- Output: 10 × 100 × 250 × 500 = 125 million tokens
Annual cloud cost — GPT-4o: (250 × $2.50) + (125 × $10.00) = $625 + $1,250 = ~$1,875/year
Annual cloud cost — Claude Sonnet 4.6: (250 × $3.00) + (125 × $15.00) = $750 + $1,875 = ~$2,625/year
Over 3 years (assuming flat pricing):
- GPT-4o: ~$5,600 ≈ €5,200
- Claude Sonnet: ~$7,900 ≈ €7,300
These are estimates. Actual costs shift with usage peaks, volume discounts, and price changes — all of which are at the provider's discretion.
What a Local Stack Costs
The most widely discussed entry-level AI server for SMBs in 2026 is the Mac Studio M4 Max. According to Apple's official store, the base M4 Max configuration starts at $1,999; configurations with 64–128 GB of unified memory — required for 30B+ parameter models at usable quality — are priced higher, with reported retail figures in the $2,199–$2,799 range for the higher memory options.
With Ollama and MLX (Apple's own machine learning framework) installed, this hardware runs models such as Llama 3.3 70B, Qwen 2.5-72B, and Mistral Small 4. Community practitioners report inference speeds of 20–40 tokens per second for 70B-class models on M4 Max hardware — fast enough for interactive use and automated pipelines.
Electricity
Based on community-reported measurements, the Mac Studio under active LLM inference typically draws 150–250 W. At 8 hours of active use per working day:
- Active inference: ~200 W × 8h × 250 days = 400 kWh/year
- Standby (remaining time): ~20 W × ~2,920 h = ~58 kWh/year
- Total: ~458 kWh/year
At a pan-EU average of roughly €0.25–0.30/kWh: €115–137/year in electricity — roughly €345–410 over three years.
Other Costs
- Extended warranty (AppleCare+ or equivalent): ~€300–400 for 3 years
- Management overhead: model updates via Ollama (
ollama pull llama3.3) take minutes and are self-contained. Realistic IT time: 1–2 hours per month.
3-Year Local TCO
| Cost item | Amount |
|---|---|
| Hardware (64–128 GB Mac Studio M4 Max) | €2,050–€2,600 |
| Electricity (3 years) | €345–€410 |
| Extended warranty (optional) | €300–€400 |
| Total | €2,700–€3,400 |
Break-Even by Usage Scenario
| Scenario | Cloud GPT-4o (3yr) | Cloud Sonnet (3yr) | Local stack (3yr) |
|---|---|---|---|
| 5 users, 50 req/day | ~€1,300 | ~€1,825 | €2,700–€3,400 |
| 10 users, 100 req/day | ~€5,200 | ~€7,300 | €2,700–€3,400 |
| 15 users, 150 req/day | ~€11,700 | ~€16,400 | €2,700–€3,400 |
For a 10-person team using AI daily, local hardware pays itself off in approximately 10–16 months relative to a cloud alternative. After that, operating costs are electricity only.
What the Numbers Don't Capture
GDPR and Data Sovereignty
Every token sent to OpenAI or Anthropic crosses your network boundary. Once prompts contain customer records, employee data, or contract details, you have an active transfer of personal data to a non-EU entity. That is not a theoretical risk — it is an ongoing compliance obligation that must be documented, legally grounded, and monitored.
A local stack processes everything on your own hardware: no third-country transfer, no dependency on data protection frameworks that can be challenged in court, no reliance on provider-side data retention policies that you do not control. For businesses operating under the GDPR, this often resolves the compliance question structurally, independent of cost. See our data sovereignty overview for what local processing means in legal practice.
No Rate Limits, No Cloud Incidents
Cloud APIs throttle concurrent requests. With ten people querying simultaneously, the provider caps throughput. Local models have no such ceiling — capacity is whatever your hardware provides, available continuously.
Model Stability
Providers modify, reprice, and retire models on their own schedule. A local model — Llama 3.3, Qwen 2.5-72B, Mistral Small 4 — stays exactly where you leave it. You control the upgrade timeline, which matters in regulated industries and for long-lived automations where prompt-model alignment is critical.
Total Cost of Cloud Subscriptions
The calculations above cover API usage only. If your team also pays for chat interfaces — ChatGPT Team, Claude for Work — those subscription fees layer on top. A 10-person team on Claude for Work at current pricing would add thousands of euros to the 3-year cloud total before a single API call is made.
When Cloud APIs Still Make Sense
Not every usage profile justifies a local stack:
- Very low volume: Under 30–40 requests per user per day, cloud is cheaper and requires no IT overhead.
- No technical staff: Someone needs to manage Ollama, pull model updates, and notice if the service has failed.
- Highly variable load: If AI use is project-based — four intensive months per year and minimal use otherwise — hardware rarely pencils out.
- Need for frontier models: For tasks where the latest GPT-4 or Claude Opus capability is the deciding factor, local open-weight models may not reach the same ceiling. This gap is narrowing every quarter, but it still exists for the most demanding reasoning tasks.
The Practical Starting Point
For a European SMB with 10–15 employees using AI daily — document processing, customer query handling, internal knowledge retrieval — the local stack comes out at roughly one-third to one-half the 3-year cost of cloud APIs, with GDPR compliance built in at the infrastructure level.
The fastest way to know your own break-even: run one month on metered cloud APIs, record your actual token volume, then model the local equivalent. We run exactly this exercise in our pilot projects. If you want to talk through the numbers for your specific workload first, get in touch.