OnDemand presents itself as an operating system for agentic AI rather than a single-purpose tool. From one workspace, teams can prototype agents, attach models and tools, coordinate multiple agents across visual workflows, and then ship those agents into web apps, internal processes or customer-facing experiences. The pitch is consolidation: instead of gluing a vector store, an orchestration framework, a model gateway and a deployment pipeline together, OnDemand ties intelligence, agents, tools, data and infrastructure into one layer.
The platform is deliberately model-neutral and hardware-agnostic. You can use leading hosted LLMs, register proprietary models, connect private inference endpoints and bring your own API keys, then run workloads across GPU, CPU or NPU architectures in the cloud, at the edge or on private infrastructure. A no-code Playground with execution tracing sits alongside an Agent Flow Builder for multi-agent orchestration, so both prompt tinkerers and workflow designers can work in the same environment.
Security and governance are positioned for enterprise buyers, with SOC 2 Type I, ISO/IEC 27001, data-sovereignty options and privacy-first connectors. OnDemand also ships Android and iOS apps so agents and sessions follow users between devices. Pricing is not published on the public site; interested teams are routed to a sales conversation, which fits its enterprise orientation.
