ManageEngine ServiceDesk Plus Cloud vs Hatz AI
Side-by-side trajectory, velocity, and editorial themes.
ServiceDesk Plus Cloud adds change-scheduling visibility while its Zia and Teams rollout continues by region.
This is the Cloud edition of ServiceDesk Plus (a near-duplicate of the separately tracked ServiceDesk Plus product, with overlapping release notes). Its recent cadence mixes small change-management improvements — a new option to view overlapping changes in the scheduler — with a steady stream of request-, template-, and sandbox-related bug fixes and the ongoing regional expansion of Zia, its Zoho-hosted LLM.
The direction mirrors the broader ServiceDesk Plus line: incremental ITSM refinements (change scheduling, request handling) layered over data-center-by-data-center availability of the Zia AI features. Most visible activity is maintenance and regional rollout rather than new capability, consistent with a mature cloud ITSM product hardening its edges.
Expect continued small change-management and request-workflow enhancements alongside further Zia availability across data centers.
Hatz turns its MSP AI platform into an agent-composition and phone-automation system.
Hatz AI is an MSP-oriented AI workspace: a governed model selector plus agents, workflows, integrations, and AI phone agents, sold through managed-service-provider tenancy. Recent releases push hard on two fronts: making phone agents a real front-line call system (routing, warm transfer, caller memory, business hours, post-call workflows) and making agents composable inside workflows. Model breadth keeps expanding, with Sonnet 5 and seven new LLMs added to the selector.
The direction is from a chat-with-models tool toward an automation platform where saved agents are reusable building blocks and phone agents replace human triage. Governance is a throughline: role-based model, integration, and tool controls, tenant templates, and usage budgets all deepen the MSP multi-tenant control plane. Model selection is increasingly abstracted behind Auto-LLM.
Expect further phone-agent autonomy and more agent-as-step composition across workflows, with continued MSP governance controls and ongoing additions to the model roster.
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