Self-hosted LLMs, when the API bill keeps growing

Paying per token is the right start and often the wrong steady state. Estela looks at what you actually send to AI APIs, moves the repeatable work to models you own, and leaves the rest where it is.

Where the money goes

• Repetitive tasksClassification, extraction and summaries that a smaller open model does well.
• Long documentsSending the same context again and again.
• The wrong modelA frontier model doing a job a small one handles.

What we do

• 1. MeasureWe read your usage and say what can move and what shouldn’t.
• 2. RouteSmall tasks go to a local model; hard reasoning stays on a frontier API.
• 3. HostA GPU server on your premises or a dedicated machine, with an API your tools already speak.
• 4. OperateUpdates, monitoring and backups.

Honest limits

• Light useLow or bursty use is cheaper on an API. We say so.
• The best modelsThe strongest frontier models are not open; some tasks stay in the cloud.

Questions

Is a self-hosted LLM cheaper than the OpenAI API?

Only with steady, heavy use; for occasional use the API wins. Estela measures your usage first and tells you which side you are on.

Which tasks can move to a local model?

Classification, extraction, search and summaries usually can. Open-ended reasoning on hard problems usually stays on a frontier model.

Who runs the server?

We do: installation, monitoring, updates and backups. Your team just calls the API.