The consumption model where AI agent infrastructure is delivered as a fully managed service — vendor operates, customer configures.
AI Infrastructure as a Service (AI-IaaS) is the consumption model where AI agent infrastructure is delivered as a fully managed service. The vendor operates the underlying AI engineering (model serving, vector storage, orchestration, governance, security posture); the customer's role is workflow configuration and integration with the existing stack. Distinct from cloud IaaS (which delivers raw compute/storage/network) and from packaged AI features (which deliver narrow AI capability inside another product).
AI-IaaS emerged as the alternative to building AI engineering capability in-house. It compresses time-to-production-deployment from quarters or years (in-house build) to weeks (managed deployment). The trade-off is operational ownership — the customer doesn't operate the underlying AI engineering — which is typically a feature for most enterprise buyers.
Most enterprise buyers don't have internal AI engineering capacity to operate production AI safely, but do have operations and integration capacity to apply AI to their workflows. AI-IaaS lets these buyers adopt enterprise AI without first building a five-to-ten-person AI engineering team.
Beth — managed AI agents for high-stakes enterprise operations with deployment posture choice and audit-grade governance
AI-IaaS for document processing where the vendor operates the AI engineering and the customer's ops team owns workflow configuration
AI-IaaS for compliance monitoring where the vendor handles model selection and the customer configures the policy framework
Beth is Huper Technology's AI-IaaS product for high-stakes enterprise operations. Beth runs the AI engineering layer (model selection, deployment, security posture, audit trails, guardrails); customer ops and IT teams configure workflows and integrations. Beth supports cloud SaaS, dedicated VPC, on-premise, self-hosted, and air-gapped deployment postures, chosen against the customer's security review.
Cloud IaaS (AWS, Azure, GCP) delivers raw compute, storage, and networking; the customer's team builds the AI layer on top. AI-IaaS delivers the full AI agent infrastructure as a managed service; the customer's team configures workflows on top. AI-IaaS is to cloud IaaS roughly what SaaS is to PaaS.
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