Do we need Kubernetes for LLM deployment?

No. Kubernetes is useful when shared infrastructure, multi-model orchestration, scaling, and your team's existing operating model justify it. A managed endpoint or dedicated GPU server can be appropriate when it meets the same security, capacity, release, and recovery requirements.

Is a private LLM deployment always more secure?

No. Private hosting gives you more direct control, but security still depends on identity, patching, document permissions, network policy, telemetry, secrets, and recovery. A poorly operated private environment can be less secure than a well-governed managed service.

What is the difference between LLM deployment and LLM serving?

Serving is the runtime layer that loads a model and answers inference requests. LLM deployment includes serving plus access control, routing, release automation, monitoring, evaluation, cost management, and recovery.

What should an LLM deployment architecture include?

A production architecture should include authenticated application access, authorized retrieval where needed, an inference gateway, one or more approved inference targets, observability, cost controls, and a tested recovery path.

Can we use vLLM for production LLM serving?

Yes, for compatible models and infrastructure. vLLM provides OpenAI-compatible serving interfaces and production-oriented deployment options, but authentication, document permissions, infrastructure security, high availability, and application-level evaluation still need to be designed around it.

Does a private endpoint guarantee data residency?

No. Private connectivity controls the network path. You must separately verify where inference is processed, where data is stored, what telemetry is retained, and whether any fallback route can move requests to another location.

What should a small team build first?

Start with one workload, one approved model path, a representative evaluation set, and a named operator. Add access controls, monitoring, cost visibility, and tested rollback before adding multiple models or complex hybrid routing.