What does self-hosting an LLM cost compared to ChatGPT and Claude?
Hosted AI is billed per user and per token, so the bill grows with every person and every integration. A GPU server costs the same whether it handles a hundred requests or a million. Enter your own numbers to see where the break-even point is for your company.
Estimate your monthly cost
Defaults are examples, replace them with your own quotes. A server runs 730 hours a month. For owned hardware, spread the purchase price over its lifetime and add power and hosting. Staff time and Loadcore platform fees are not included.
How the comparison works
Hosted AI costs are the sum of two parts:
- Subscriptions: the number of people on a paid AI plan times the price per seat.
- API usage: the tokens your applications and integrations send each month times the blended price per million tokens. Input and output are often priced differently, so use an average that matches your mix.
Self-hosted cost is the number of GPU servers times their hourly cost times 730 hours a month. For rented servers, use the provider's hourly rate. For owned hardware, divide the purchase price by the number of months you expect to use it, add power and hosting, and convert to an hourly rate.
Why the curve favors self-hosting at scale
Hosted pricing grows linearly: twice the users, twice the bill. Self-hosted cost grows in steps: one more server when throughput runs out. Once a server is busy for a good part of the day, every additional request is effectively free.
That is why the break-even point is mostly a question of utilization. A server that sits idle is expensive, a server that serves a whole department is cheap.
Keep utilization high
- Share one model across teams. Give several departments access to the same instance instead of running a model per team.
- Size the model to the task. A Qwen3.8 27B on one 24 to 48 GB GPU answers most everyday questions. Keep large models like gpt-oss 120B for the teams that need them.
- Use mixture-of-experts models. Models such as Gemma 4 26B A4B activate only a few billion parameters per token and serve far more tokens per second than a dense model of the same size.
- Reuse the GPUs you already own. Distributed instances combine the memory of up to eight servers on your local network to fit a larger model.
Next steps
See how a self-hosted ChatGPT alternative is set up for a whole company, or read the documentation to register your first server.
Frequently asked questions
How many GPU servers do we need?
It depends on the model and on how many people use it at the same time. A single GPU server running a mid-size model can serve a department, and you add servers running the same model when you need more throughput. Start with one server, measure usage in the console, and scale from there.
Should we rent or buy GPUs?
Renting by the hour is the fastest way to start and keeps the cost flexible. Buying makes sense once usage is steady, because the hardware usually pays for itself well within its lifetime. Loadcore works the same way on both, and you can mix them.
What is not included in this calculator?
Staff time, power and hosting for owned hardware, and Loadcore platform fees. Hosted plans also differ in features and in model quality, so compare what you actually need, not only the price.
Where do the default numbers come from?
They are examples. The subscription price stands for a typical business AI seat, the token price for a 50/50 blend of input and output list prices of GPT-6.1 Sol and Claude Sonnet 5.5 as of November 2026, and the server price for a rented H200 by the hour. Replace them with your own quotes.
Give your team a private ChatGPT alternative on your own GPU servers. Open models, an OpenAI-compatible API, per-department access and no prompts leaving your network.
Run open models with strong reasoning and coding skills on your own GPU servers as a private alternative to Claude. OpenAI-compatible API, per-team access, no per-token fees.
Run your first model today.
Register a GPU server, pull an open model and hand your team an API key.