Relay.app Review 2026

a plain-English automation platform for creating reliable workflows across 200+ apps with AI credits and team collaboration.

Relay.app is a plain-English automation platform for creating reliable workflows across 200+ apps with AI credits and team collaboration. The curl-fetched vendor pages position it in the ai automation category and emphasize practical capabilities rather than a vague concept: plain-English automation builder, 200+ connectors, AI builder credits, shared workflows, custom MCP servers. For builders, the main value is shortening the distance between an idea and an operational workflow. A product team can use it for sales ops automation, marketing handoffs, internal workflow automation, AI agent academy training, while a business team can evaluate it around repeatability, governance, and time saved. The pricing evidence captured during this run showed: Free ($0: 1 user, 500 free AI credits/month, 200 steps/month); Professional ($19/month billed annually: 1 user, 2,000 AI credits/month, 750 steps/month); Team ($59/month billed annually: 10 users, 2,000 AI credits/month, 1,500 steps/month); Enterprise (Custom: Custom usage, integrations, support, workshops, SOC2/GDPR). A good first pilot is a narrow, measurable workflow: define the input, expected output, human review point, and success metric, then compare the result with the current manual process. Buyers should also confirm data handling, retention, export options, admin controls, and whether generated work can be inspected or versioned. Compared with older automation or content tools, Relay.app is most interesting where AI is part of the actual execution loop rather than only a chat interface. It should fit teams that want speed, but still need enough structure to avoid brittle one-off experiments. MCP note: Relay.app has verified Model Context Protocol support as a both; Pricing comparison mentions Custom MCP Servers, suggesting Relay can expose custom MCP endpoints for AI workflows while also using model providers. This matters for teams standardizing agent integrations because it lets compatible tools expose or consume context without bespoke glue code.