Canny vs Hatz AI
Side-by-side trajectory, velocity, and editorial themes.
Canny is betting on Ideas and Autopilot — AI-triaged feedback wired to revenue.
Canny's recent work centers on Ideas, its AI-triaged feedback hub, and the Autopilot engine behind it: on-demand auto-grouping of ungrouped ideas, the Ideas beta opening to all Core-plan teams, Slack DMs that close the loop with account owners when ideas complete or get assigned, CSV exports, and manual company creation for teams without a data sync. Autopilot now also surfaces conversations where no feedback was found.
Canny is evolving from a feedback board into an AI-driven feedback-to-revenue prioritization system. The emphasis is on Autopilot triaging incoming feedback into the right product groups and tying it to customer and ARR context, then routing the loop back to sales and CS through Slack.
Expect Ideas to keep moving from beta toward general availability with deeper revenue and account context, and more Autopilot automation around triage and grouping.
Hatz is building the governed multi-tenant control plane for MSPs running AI.
Hatz AI is shipping fast on two axes: a multi-tenant MSP control plane (per-tenant integration and custom-MCP enable/disable, provisioning templates that fix a tenant's models, apps, and permissions at creation, usage dashboards, download restrictions) and a broadening model and integration layer (Opus 4.8, an LLM Gateway extended to Anthropic, Gemini 3.5 Flash, Auto model-selection modes, and a steady stream of official MCP integrations). Recent releases emphasize admin control over which capabilities each tenant gets.
Hatz is positioning as the governance and provisioning layer for MSPs delivering AI to many client tenants — not just another chat product, but the control plane that decides which models, tools, and integrations each tenant can touch. Model and integration breadth is table stakes; the differentiation is per-tenant control.
Expect more tenant-governance depth — finer permission and policy controls, more provisioning automation — alongside the continuing cadence of new model and MCP-integration additions.
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