Conclusion
- Choose hosted APIs first when traffic is unpredictable or below sustained GPU utilization.
- Choose local when privacy, offline control, or high utilization matters more than setup time.
- The honest metric is cost per successful job, including retries, ops time, electricity, and idle GPU hours.
- Use DeepSeek/Qwen/SiliconFlow as the low-cost API baseline before buying or renting GPUs.
What to do next
- Estimate monthly input/output tokens and peak concurrency from real logs or a one-week pilot.
- Calculate hosted cost with DeepSeek, Qwen, SiliconFlow, Groq, or OpenRouter pricing plus expected retries.
- Calculate local cost: GPU rental or depreciation, electricity, storage, monitoring, upgrades, and engineer time.
- Run the same 20-task benchmark on a hosted API and a local model; compare accepted outputs, latency, and failure rate.
- Start hosted, then move only stable high-volume background workloads to local if utilization justifies it.
Recommended paths
| Provider | Free / credits | Best for |
|---|---|---|
| DeepSeek | $5 signup / current console credit | Hosted low-cost baseline for text and coding |
| Qwen | 70M signup tokens | China-friendly hosted coding and long context |
| SiliconFlow | Free models + ¥14 credit | China-hosted open models without GPU ops |
| Groq | Free developer limits vary | Fast open-model API before local latency work |
| OpenLLMAPI | Signup credit varies | One endpoint to compare hosted routes before localizing |
Global developer checklist
- Confirm whether signup, billing, and API keys work from your country before writing production code.
- Prefer OpenAI-compatible endpoints when you may need to switch models, regions, or providers later.
- Test free credits with a real smoke prompt and record latency, error shape, streaming behavior, and quota burn.
- Keep at least one fallback route for provider outages, model deprecations, and regional access changes.
Production handoff
Want API cost logs before deciding local?
Route experiments through one OpenAI-compatible key, compare DeepSeek, Qwen, GPT, Claude, and Gemini, then localize only workloads that prove cheaper.
FAQ
When does local LLM hosting become cheaper?
Usually when you can keep GPUs busy for many hours per day, run batch jobs predictably, or already own suitable hardware. Idle GPUs destroy the cost advantage.
What costs do people forget in local LLM math?
Ops time, model serving bugs, monitoring, storage, upgrades, quantization testing, electricity, and the cost of lower model quality or retries.
Should privacy-sensitive apps use local models?
Often yes, but also consider private cloud, region-specific providers, redaction, and data retention policies. Cost is not the only constraint.
What is the safest migration path?
Start with an OpenAI-compatible hosted API, log real demand, then move only proven high-volume workloads to local or dedicated inference.