An inference platform is the software and hardware infrastructure that runs an AI model in production and answers user requests quickly, reliably and at a controlled cost.
Training a model is not enough: it then has to be loaded onto GPUs, handle thousands of concurrent requests, batch requests to make better use of hardware, monitor latency and absorb load spikes. An inference platform combines for this an execution engine (such as vLLM), orchestration (often Kubernetes), an API and observability.
It can be built in-house or bought from a specialized provider that bills by usage. The choice depends on volume, confidentiality constraints and the team's ability to operate the whole stack.
Why it matters when hiring
Building and running an inference platform requires a rare mix: solid ML culture, strong distributed systems skills and a taste for optimization. These profiles are often found on the MLOps, infra or SRE side rather than among data scientists. In interviews, get the candidate to talk about latency, throughput and cost per request: a real practitioner gives orders of magnitude and explains their trade-offs.