How to Calculate Model Hosting Cost

What is Model Hosting Cost?

The Self-Hosted Model Cost calculator estimates the total expense of running an open-source LLM (Llama 3, Mistral, Mixtral, Phi-3) on your own infrastructure, including GPU servers, bandwidth, storage, and operational overhead. It compares self-hosting against equivalent API costs to find the breakeven point.

Formula

Monthly Hosting Cost = GPU Server Cost + Storage + Bandwidth + Ops Overhead

C_gpu: GPU Server Cost ($/month) — Monthly GPU rental or amortized hardware cost
C_ops: Operations Overhead ($/month) — Engineering time, monitoring, and maintenance costs
Q: Monthly Queries (queries/month) — Total inference requests served per month
BW: Bandwidth Cost ($/month) — Data transfer costs for serving responses

Step-by-Step Guide

1Select the model you want to host and its hardware requirements
2Choose between cloud GPU rental (Lambda, RunPod, AWS) or on-premise hardware
3Enter your expected query volume and concurrent user load
4View monthly cost, cost per query, and breakeven vs. API alternatives

Worked Examples

Input

Llama 3 70B on 2× A100 80GB (Lambda Labs), 200K queries/month

Result

GPU: 2 × $1.10/hr × 730 hrs = $1,606/month. Storage: $50. Bandwidth: $100. Ops: $200. Total: $1,956/month. Per-query: $0.0098. Breakeven vs. GPT-4o at $0.005/query: self-hosting is more expensive at this volume.

Input

Mistral 7B on 1× A10G (AWS), 1M queries/month

Result

GPU: $0.76/hr × 730 = $555/month. Per-query: $0.00055. Breakeven vs. GPT-4o-mini at $0.0003/query: need ~3M queries/month to break even. At 1M queries, API is 2x cheaper.

Common Mistakes to Avoid

✕Forgetting to include engineering time for model serving setup, monitoring, and maintenance (10-40 hours/month)
✕Not accounting for GPU memory requirements — a 70B model needs at minimum 140GB GPU RAM (2× A100 80GB or quantized)
✕Comparing self-hosted open-source model costs against API costs without normalizing for output quality differences

Frequently Asked Questions

When does self-hosting an LLM become cheaper than API services?

For frontier-equivalent quality (70B+ models), self-hosting typically breaks even at 500K-2M queries per month compared to GPT-4o pricing. For smaller models competing with GPT-4o-mini, the breakeven is much higher (3M+ queries/month) because mini model API pricing is already very low. Data privacy and customization needs may justify self-hosting even at lower volumes.

Can I run a 70B model on a single GPU?

Not at full precision. A 70B parameter model requires ~140GB GPU RAM at FP16. Using 4-bit quantization (GPTQ or AWQ), it fits on a single 80GB A100 or H100 with acceptable quality loss. For production serving with good throughput, 2× A100 80GB or 1× H100 is recommended.

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