An observability layer for agentic and AI-augmented automation workflows across n8n, Make, and Zapier that tracks token spend, flags silent failures, and surfaces cost-per-business-outcome so teams ca
AI API costs inside automation workflows are invisible until the bill arrives and nobody has built the Datadog for this specific layer
Built for N8n power users managing multiple AI/LLM workflows who need cost visibility and reliability monitoring..
Framing monitoring as ROI accountability rather than just uptime alerts, which is the question every ops manager actually asks about their automation stack
“Built a free tool to track AI/API cost per n8n workflow + silent failure alerts — looking for beta feedback…”
The receipts — real demand
“Built a free tool to track AI/API cost per n8n workflow + silent failure alerts — looking for beta feedback”
Full dossier
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Every corroborating quote, the source receipts, and the community echo. One email, no payment.
demand score 6.2 — the receipts are below
Why this is a gap
Surfaced from a high-intensity complaint with clear willingness to pay and a specific, reachable audience.
The market
N8n power users managing multiple AI/LLM workflows need cost and failure visibility. No search volume; niche audience of n8n experts. Founder already built a free prototype, signaling real pain.
Competition & the opening
No direct competitors (3/10 crowded). N8n itself lacks native cost tracking per workflow and silent failure detection. The opening is a purpose-built monitoring layer.
What's hard to build
Feasibility unknown, but likely moderate. Must hook into n8n's execution logs via API or webhooks, aggregate LLM provider costs (OpenAI, Anthropic, etc. all different billing APIs), and detect silent failures (non-error executions with empty results). Data pipeline complexity is the main friction.
Why now
n8n users face runaway AI/API bills and silent failures as workflows scale, with no native observability.
How you'd monetize
$29/mo SaaS or usage-based per-workflow