The infrastructure layer that runs large language model inference for production AI workloads.
LLM hosting is the infrastructure that runs large language model inference for production workloads. Responsibilities include serving model weights efficiently, scaling against workload volume, deployment-posture choice (cloud, VPC, on-premise, air-gapped), latency management, and ensuring the deployment satisfies the security and compliance frameworks the customer's review requires.
LLM hosting is one of the most consequential decisions in enterprise AI adoption because model traffic carries the customer's data. Where the model is hosted determines where that data goes — vendor-managed APIs vs single-tenant deployments vs air-gapped on-premise inference. The model size, the latency requirements, and the deployment posture combine into the LLM hosting choice.
Sensitive enterprise workloads (litigation, M&A pre-announcement, clinical, regulatory) often require LLM hosting that doesn't send model traffic to external providers. Customers' security policies increasingly mandate on-premise or VPC LLM hosting for these workloads.
Cloud-hosted LLM serving in a region matching the customer's data-residency policy
Single-tenant LLM serving in the customer's VPC with customer-managed encryption keys
On-premise LLM serving in the customer's data center for air-gapped or sovereign workloads
Bring-your-own-model deployments where the customer specifies the model lineage and provider
Beth's LLM hosting follows the customer's chosen deployment posture. For cloud-hosted Beth, models are hosted regionally; for VPC, models run inside the customer's cloud account; for on-premise and air-gapped, models run on customer infrastructure. Customer data is never used for model training under any deployment posture.
Customers can specify preferences (model lineage, model provider, model size) during pilot scoping. Huper handles model selection per workflow with the customer's preferences as inputs.
Data flow depends on deployment posture. Cloud-hosted: data flows through Huper-managed regional infrastructure. VPC/self-hosted: data stays inside the customer's cloud account. On-premise/air-gapped: data never leaves the customer's environment. Customer data is never used for model training under any posture.
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