Overview
Running AI locally has an upfront hardware cost but near-zero marginal cost per generation. Cloud APIs charge per request, per token, or per minute of compute. This page compares the two approaches across common workloads to help you decide which makes sense for your usage pattern.
All local cost estimates assume you already own a GPU-capable machine and pay roughly $0.15/kWh for electricity. Cloud prices are based on published API rates as of early 2026 and may change.
Image Generation Costs
Image generation is one of the most cost-effective workloads to run locally. A single 1024×1024 SDXL image takes about 15–30 seconds on a modern GPU and uses roughly $0.0005 of electricity.
| Provider | Cost per 1,000 Images | Notes |
|---|---|---|
| Local (Thumper-Run) | ~$0.50 | Electricity only; assumes existing GPU |
| Stability AI API | ~$40 | $0.04/image at 1024×1024 |
| DALL-E 3 (OpenAI) | $40–$80 | $0.04–$0.08/image depending on quality |
| Midjourney | ~$30/mo | Subscription; unlimited on higher tiers |
| Replicate (SDXL) | ~$10 | ~$0.01/image; cheapest cloud option |
LLM Inference Costs
Local LLM inference typically uses smaller models (7B–13B parameters) compared to cloud frontier models (100B+ parameters). The trade-off is cost vs capability.
| Provider | Cost per 1M Tokens | Model Size | Notes |
|---|---|---|---|
| Local (Thumper-Run) | ~$0.05 | 7B–13B | Electricity only; Ollama or llama.cpp |
| OpenAI GPT-4o | $5–$15 | Frontier | $5 input / $15 output per 1M tokens |
| Claude Sonnet | $3–$15 | Frontier | $3 input / $15 output per 1M tokens |
| Groq (Llama 3) | ~$0.27 | 70B | Fast inference; limited free tier |
| Together AI | ~$0.20 | 7B–70B | Open-source models via cloud |
Storage Costs
AI models are large files. Running locally means storing them on your disk. Here’s what to expect for a typical setup.
| Component | Typical Size | Notes |
|---|---|---|
| SDXL (image generation) | 6–7 GB | Base + VAE + refiner |
| LLM 7B (Q4 quantized) | 4–5 GB | Llama 3, Mistral, Qwen |
| LLM 13B (Q4 quantized) | 8–10 GB | Higher quality, more VRAM needed |
| Music generation (ACE-Step) | 2–3 GB | DiT model + vocoder |
| Video generation (LTX-Video) | 5–8 GB | Base model + VAE |
| Typical setup (3–4 apps) | 50–100 GB | Apps + shared model cache |
By comparison, cloud APIs require zero local storage — models live on the provider’s servers. However, you pay for every request and have no offline access.
When Cloud Wins
Cloud APIs are the better choice in several scenarios:
- Infrequent use — fewer than 100 images per month or light LLM usage
- Frontier models — you need GPT-4, Claude, or other models too large to run locally
- No GPU — your machine lacks a discrete GPU and CPU inference is too slow
- Instant scale — you need to process thousands of requests in parallel
- Team collaboration — you need shared API access with usage tracking
When Local Wins
Local AI becomes the clear winner for heavy or privacy-sensitive workloads:
- Heavy use — more than 500 images per month or frequent LLM conversations
- Privacy requirements — data must never leave your device
- Offline access — you need AI capabilities without internet
- No recurring costs — after hardware investment, marginal cost is near zero
- Full control — choose any model, fine-tune it, modify workflows
Break-Even Analysis
| Usage Level | Monthly Cloud Cost | Monthly Local Cost | Break-Even |
|---|---|---|---|
| Light (100 images) | $4–$8 | ~$0.05 | ~12 months |
| Medium (500 images) | $20–$40 | ~$0.25 | ~3 months |
| Heavy (2,000 images) | $80–$160 | ~$1.00 | ~1 month |
Break-even assumes a $500–$1,000 GPU investment amortized over time. If you already own a gaming PC with a modern GPU, the break-even is immediate — every generation is essentially free.
Key Takeaways
- Local AI has near-zero marginal cost after hardware investment
- Heavy users (500+ images/month) break even in roughly 3 months
- Cloud wins for infrequent use, frontier models, or no-GPU setups
- Local and cloud are complementary — use both for different workloads