The Core Trade-off
When building AI-powered features, one of the first decisions you'll face is whether to use a hosted LLM API (like OpenAI, Anthropic, or Google) or self-host an open-source model (like Llama 3, Mistral, or Qwen).
There's no universal right answer — but there is a right answer for your specific situation.
When Hosted APIs Win
Speed of Integration
Hosted APIs let you go from zero to production in hours. No infrastructure to manage, no model weights to download, no GPU provisioning.
Cost at Low Volume
If you're processing fewer than 1M tokens/day, hosted APIs are almost always cheaper. You pay per token with zero fixed costs.
State-of-the-Art Quality
GPT-4o and Claude 3.5 Sonnet still outperform most open-source models on complex reasoning tasks. If quality is paramount and budget allows, hosted wins.
When Self-Hosting Wins
Data Privacy
If your data can't leave your infrastructure (healthcare, finance, legal), self-hosting is non-negotiable.
Cost at High Volume
Once you cross ~10-50M tokens/day, self-hosting becomes dramatically cheaper. A single H100 can serve millions of tokens per day.
Latency & Control
No network round-trip to an external API. You control the model, the version, the timeouts, the rate limits.
The Decision Framework
| Factor | Hosted API | Self-Hosted |
|---|---|---|
| Time to deploy | Hours | Days-weeks |
| Low volume cost | ✅ Cheaper | ❌ More expensive |
| High volume cost | ❌ Expensive | ✅ Much cheaper |
| Data privacy | ⚠️ Shared | ✅ Full control |
| Model quality | ✅ SOTA | ⚠️ Good but not SOTA |
| Customization | ❌ Limited | ✅ Full fine-tuning |
My Recommendation
Start with hosted APIs. Ship fast, validate your use case, understand your actual token volumes. Once you hit scale or have a hard privacy requirement, migrate to self-hosted.
"Make it work, make it right, make it fast" — and in AI, hosted APIs help you make it work fastest.