From Laptop to Production: Self-Host LLMs with Ollama and vLLM, Then Distribute Them Like a Pro with Odock
You can prototype on Ollama in an afternoon and serve real production traffic on vLLM at 6x the throughput. The part most guides skip is what happens next: turning that self-hosted model into something you can safely hand out to teams, customers, or a pro tier with proper access control and billing.
Youcef Kaddour
Founder at Odock and AI infrastructure engineer
What you should take away
- 1
Ollama and vLLM solve different problems: Ollama is the fastest way to prototype with a local model, vLLM is what production traffic actually needs, with roughly 6x the throughput of Ollama under concurrent load, growing to over 16x on newer GPUs at scale.
- 2
A single GPU running vLLM can be materially cheaper than calling a hosted frontier API once you clear a few hundred requests per hour, but only if you also solve access control, multi-tenancy, and billing, which raw vLLM does not provide out of the box.
- 3
Odock treats a self-hosted vLLM (or Ollama) deployment as just another provider behind the gateway, which means your self-hosted model gets the same virtual keys, budgets, pricing, and usage records as any hosted provider, and can be distributed to internal teams or paying customers as a governed product.