---
type: concept
title: "Self-Hosting Cost Crossover"
description: "The napkin-math point where monthly per-token spend exceeds a fixed cloud-GPU bill, which is exactly when self-hosting an open model starts paying for itself."
tags: [cost-optimization, napkin-math, scaling]
videos: [the-hard-truth-about-hosting-your-own-llms, local-vs-cloud-llmsrag-lets-finally-end-this-debate]
created: 2026-07-21
updated: 2026-07-21
---

# Self-Hosting Cost Crossover

The self-hosting crossover is the volume at which a fixed monthly GPU bill becomes cheaper than paying per token for the same model, and it is not a matter of taste: "there is an exact calculable time when it makes sense to make the switch and I will cover that as well" [0:02:53]. The strategy that falls out of it is to start on a cheap hosted inference provider running the exact open-weight model you eventually intend to host yourself, so the migration later is an endpoint swap rather than a model change.

The math is deliberately napkin-grade. Take the hourly rate of the GPU you would rent and annualize it to a month: "I'm just going to take 0.39 because it is 39 cents an hour multiply it by 24 because there's 24 hours in a day and then multiply by 30 CU there's roughly 30 days in a month and that gives us a grand total of $280 a month" [0:10:18]. Then convert the hosted provider's price into prompts: tokens per dollar divided by your average tokens per prompt (Cole assumes 5,000) gives prompts per dollar, and multiplying by the fixed bill gives the break-even volume. For a RunPod A40 against Llama 3.1 70B on Groq that lands near 94,000 prompts a month: "if you get to like 3,000 promps per day with your application you would want to self-host your llm instead" [0:13:03].

Two adjustments matter. The fixed side is fixed only if the GPU runs 24/7, so bursty traffic pushes the crossover much higher. And cost is only one axis of the decision - data sensitivity or a hard requirement for a frontier model can override the arithmetic entirely, in either direction.

## Prerequisites

- [Cloud GPU Hosting](./cloud-gpu-hosting.md) - the fixed-cost side of the comparison, and why Cole prefers renting over a home rig.
- [Local & Self-Hosted AI](./local-ai.md) - what you are crossing over into.

## Part of

- [Local vs. Cloud Decision Framework](./local-vs-cloud-decision-framework.md) - scale is the last question in the decision tree; this concept supplies the number.

## Contrasts with

- [Usage-Based Pricing Trap](./usage-based-pricing-trap.md) - the failure mode of never running the math and letting per-token spend compound.
- [LLM Data Privacy Tiers](./llm-data-privacy-tiers.md) - the non-cost reason to self-host early, regardless of volume.

## Tools

- [RunPod](../entities/tools/runpod.md) - the cloud GPU whose hourly rate anchors the example.
- [Groq](../entities/organizations/groq.md) - the fast, cheap per-token provider you start on.
- [Llama](../entities/tools/llama.md) - the open model that is the same on both sides of the crossover.
- [DigitalOcean](../entities/organizations/digital-ocean.md) - a pricier GPU alternative.
- [Ollama](../entities/tools/ollama.md), [Nvidia](../entities/organizations/nvidia.md) - what actually serves the model once you switch.
- [Together AI](../entities/organizations/together-ai.md) - Pay-per-token inference provider serving open models like Llama 3.1; cheaper on its lite 70B variant but pricier than Groq once you want turbo-speed inference.

## Related

- [LLM Cost Optimization](./llm-cost-optimization.md) - the broader toolkit, including caching and smaller models.
- [Local LLM Hardware Requirements](./local-llm-hardware-requirements.md), [Model Quantization](./model-quantization.md) - levers that move the fixed side of the equation down.
- [Provider Independence](./provider-independence.md) - staying on one model across the switch is what makes the migration safe.
- [Model Selection](./model-selection.md), [Local LLMs as Agents](./local-llms-as-agents.md), [Agent Deployment](./agent-deployment.md), [Prompt Caching](./prompt-caching.md).

## Sources

- [The HARD Truth About Hosting Your Own LLMs](../sources/the-hard-truth-about-hosting-your-own-llms.md) - "[0:02:53] there is an exact calculable time when it makes sense to make the switch and I will cover that as well"
- [The HARD Truth About Hosting Your Own LLMs](../sources/the-hard-truth-about-hosting-your-own-llms.md) - "[0:10:18] I'm just going to take 0.39 because it is 39 cents an hour multiply it by 24 because there's 24 hours in a day and then multiply by 30 CU there's roughly 30 days in a month and that gives us a grand total of $280 a month"
- [The HARD Truth About Hosting Your Own LLMs](../sources/the-hard-truth-about-hosting-your-own-llms.md) - "[0:13:03] if you get to like 3,000 promps per day with your application you would want to self-host your llm instead"
- [Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate](../sources/local-vs-cloud-llmsrag-lets-finally-end-this-debate.md)
