vLLM specialists for your dedicated team.

Where vLLM fits in a long-term product team.

Self-hosting is chosen for a reason, not by default. Data that cannot leave your environment, cost at high sustained volume, or latency that an API round trip cannot meet. Absent one of those, a hosted API is cheaper and considerably less work.

Where it is justified, throughput per GPU decides the economics entirely. The difference between an efficient serving setup and a naive one is several times the hardware bill, which is why the serving layer deserves the same attention as the model.

What your dedicated team does with vLLM.

Self-hosted inference is an operational commitment. GPUs need capacity planning, model versions need rollout discipline, and throughput tuning continues as usage changes, which is ongoing work.

It is also frequently adopted for a reason that stops applying. When hosted pricing moves or volume falls, revisiting the decision is part of the job, and companies hire AI developers for it when the feature itself is also in scope.

Common vLLM use cases.

• Inference inside your own boundary Running models where regulation or contract prevents sending data to a third party.
• Cost at sustained volume Self-hosting where per-token pricing has passed the point of being the cheaper option.
• Getting more from existing GPUs Serving configuration tuned so the hardware already bought carries more load.

Adding vLLM skills to your team.

Serving infrastructure is assigned to a dedicated MLOps team, working on your own hardware or in your own cloud account.

vLLM specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.