AdTest.AI vs Spiral

AdTest.AI evaluates advertising creative before launch with AI scoring and optional audience research. Its engine scores ads across 13 dimensions and returns an overall effectiveness score, strengths, weaknesses and recommendations. It accepts images, video, carousels and messaging copy; AI Ad Studio can generate platform-ready variants from a brand URL and send them for scoring. Optional audience studies combine human ratings with AI scores in a Mixed Score, with audience reads taking 24–48 hours. Reports can be exported as branded multi-page PDFs, and audience results can be cross-tabulated and exported as CSV. Team workspaces support project sharing, role-based access, invitations and per-workspace billing. The seven-day trial requires no credit card and includes three AI test credits, but not API or MCP access. Paid plans include Creator at $30 a month and Starter at $50 a month; one-time audience studies start at 299.00 USD. Android and iPhone apps let existing users run tests and review reports, while purchases are made on the web. The service also offers a REST API and an official Claude MCP server for AI-only scoring.

Spiral combines ad research, creative generation, campaign launch, and performance analysis for mobile app growth teams. Its research covers Meta and TikTok, with competitor tracking and feeds for winning, trending, and high-EU-reach ads. Teams can generate static and video ads, adapt competitor concepts, translate videos, and swap AI avatars. Connecting a Meta Ads account is optional; connected accounts can be used to analyze performance and launch campaigns from Spiral. API and MCP access are available, and their data calls count against plan credits. The service runs on web and through an API, and its site says it works better on desktop. Explore costs $175 a month, Launch costs $350 a month, and Grow costs $750 a month; Scale pricing is custom. The 7-day trial offers access to the paid service, and there is no free plan. Spiral says it uses encryption, access controls, logging and monitoring, backups, and a secure development lifecycle. It may use customer data and outputs in aggregated or de-identified form to improve its own models and analytics.