Self-Hosted LLM vs API
Almost nobody should self-host. The few that should, know it.
The "we'll save money by self-hosting" pitch almost never plays out. Here's the honest math.
| Self-Hosted (Llama, Mistral, etc) | API (OpenAI, Anthropic, Gemini) | |
|---|---|---|
| Quality | Llama 3.1 405B ≈ GPT-4 (mostly). 70B is great. Below that, drop-off is real. | Frontier-grade. |
| Setup | GPUs, vLLM/TGI, autoscaling, monitoring, fallback. | API key. |
| Cost (low volume) | Way more expensive. Idle GPUs burn money. | Pay per token. |
| Cost (high volume, 10M+ requests/mo) | Can be cheaper, sometimes dramatically. | Scales linearly. |
| Latency | Lower in the same VPC. | Network hop, but providers are fast. |
| Compliance | Full control. HIPAA / SOC2 in your environment. | Depends on provider. Most have BAAs and SOC2. |
| Time to first ship | Weeks. | Hours. |
Pick Self-Hosted (Llama, Mistral, etc) when
Self-host when: regulatory requirements force it, you have steady high-volume inference (10M+ calls/mo), or you have a real engineering team to operate it.
Pick API (OpenAI, Anthropic, Gemini) when
API when: anything else.
Bottom line
For 95% of companies, the API is the right answer. For the other 5%, you already know who you are and you have a GPU budget.
Need help picking — or stitching them together?
We do this for clients every week. Bring us the workflow, we'll bring the architecture.
Talk to usGlossary
- Llama (Meta)Meta's open-source LLM family — the leading choice for self-hosted and fine-tuned deployments.
- LLMOpsThe operational practice of running LLM-based systems in production — monitoring, versioning, and iteration.
- InferenceRunning a trained model to generate output. The expensive part of AI in production.
- QuantizationStoring model weights at lower precision (e.g., 4-bit) to save memory and run faster.