{"id":2608,"date":"2026-07-24T11:00:00","date_gmt":"2026-07-24T08:00:00","guid":{"rendered":"https:\/\/neurounit.ai\/blog\/?p=2608"},"modified":"2026-07-05T14:18:58","modified_gmt":"2026-07-05T11:18:58","slug":"open-source-llms-when-to-self-host","status":"publish","type":"post","link":"https:\/\/neurounit.ai\/blog\/en\/open-source-llms-when-to-self-host\/","title":{"rendered":"Open-Source LLMs: When to Self-Host"},"content":{"rendered":"

Every team that hits a large API bill starts asking the same question: what if we just run the model ourselves?<\/p>\n

It sounds simple. Open weights are free to download. GPUs are available to rent. The math looks obvious on a spreadsheet. Then reality shows up. Self-hosting an open-source LLM is a real engineering commitment, not a cost hack. It pays off in specific situations and quietly drains your team in others.<\/p>\n

This guide is about telling those situations apart. No hype, no fear. Just the tradeoffs and a way to decide.<\/p>\n

What «self-hosting» actually means<\/h2>\n

Downloading open weights is the easy part. Running them in production is the work.<\/p>\n

To serve an open-source model you need an inference stack, GPU hardware or rented GPU instances, a way to batch and queue requests, monitoring, and someone on call when it breaks at 3am. You are no longer a consumer of an API. You are now an infrastructure team running a latency-sensitive service.<\/p>\n

There is a middle path too. Some providers host open-weight models for you and expose them through a standard API. That is not self-hosting. That is renting someone else’s inference. It gives you model choice without the operational burden, and for many teams it is the smarter first step before committing to your own hardware.<\/p>\n

So the real spectrum looks like this:<\/p>\n