Cloud infrastructure for AI agents

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Existing cloud providers were not designed for AI agents: sandboxing options are either inadequate or excessive, and pricing models charge for always-on compute even when agents run intermittently in bursts.

Solution concept

Purpose-built cloud infrastructure with full VM isolation per agent run and usage-based pricing aligned to bursty agentic workloads, so customers pay only for compute time actually used while maintaining strong security boundaries.

Who has it

Teams and companies running AI agents in production who need isolated, cost-efficient compute.

How they handle it today

Currently using general-purpose cloud providers (VMs, serverless platforms, etc.) that either lack proper sandboxing for agents or charge for idle time, forcing acceptance of either security or cost inefficiency.

Why now

AI agents are moving into production use cases across companies; existing cloud infrastructure creates friction on both cost and security fronts. The maturity of agentic frameworks and benchmarks (e.g., OSworld test) validates the production readiness of agents as a workload class.

How it could charge

Usage-based pricing per agent execution, billed by compute time consumed. Likely tiered for volume or feature lock-in on isolation guarantees and execution limits.