When to Self-Host an LLM

Self-host when: (1) API costs exceed $3,000/month, (2) You need <50ms latency, (3) Data can't leave your network, (4) You need full model control. Otherwise, APIs are cheaper and simpler.

The 4 Reasons to Self-Host

1. Cost Threshold: $3,000+/month in API spend

Below $3K/month, the overhead of self-hosting (GPU rental, engineering time, monitoring, updates) exceeds what you'd save. Above $3K/month with consistent throughput, self-hosting a 70B model can reduce costs by 40-60%. The break-even depends on utilization — GPUs you're not using still cost money.

2. Latency: Sub-50ms first-token requirements

API calls include network latency (50-200ms) plus queue time. If your application needs sub-50ms time-to-first-token (real-time voice, gaming, high-frequency trading), self-hosting on local GPUs eliminates network overhead.

3. Data Privacy: Data cannot leave your network

Regulated industries (healthcare, finance, defense) may require that data never leaves your infrastructure. Self-hosting ensures prompts, completions, and fine-tuning data stay on your hardware. Note: some API providers offer on-premises deployments, but at significant cost.

4. Model Control: Fine-tuning, custom inference, research

If you need to modify model weights, run custom inference pipelines, or experiment with architectures, self-hosting gives you full control. APIs only expose what the provider chooses to expose.

Self-Hosting Cost Breakdown

SetupMonthly CostGood For
1x A100 (cloud)~$1,100-1,5007B-13B models
2x A100 (cloud)~$2,200-3,00034B-70B models
4x A100 (cloud)~$4,400-6,00070B+ models, high throughput
RunPod spot (A100)~$800-1,200Non-critical workloads
Mac Studio M2 Ultra (owned)~$30 electricity7B-13B hobby/dev

When to Stick with APIs

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