AI Tool Comparison
Comparing as AI Product Recommendations & PersonalizationLily AI vs Dynamic Yield
Lily AI
VS
Dynamic Yield
Verdict by Category
Detailed Comparison
Feature
Lily AI
Dynamic Yield
Pricing
CustomLily AI uses a fully custom pricing model — there is no published rate card. Pricing is scoped around three variables: catalog size (number of SKUs), selected use cases, and number of markets or regions. Use cases that can be individually selected and priced include Google Shopping attribute generation, Meta catalog ads copy, SEO/AEO metadata, conversational commerce attributes, item setup and classification, and consumer product copy.
Cost per SKU generally improves as volume grows, since fixed setup costs spread across more items. More complex outputs (conversational descriptions) cost more per SKU than structured attribute sets. Localizing into additional markets adds processing cost on top of the base catalog price.
All plans include catalog ingestion and scoring, agentic enrichment of the highest-impact gaps, matched-spend A/B testing, and reporting with confidence intervals. Higher tiers add more channels, optimization cadence, support, and governance features. To get an exact quote, teams can use the pricing estimator on lily.ai/pricing or book a demo. A free 30-day pilot on 500 products is available — it includes catalog gap scoring and measured lift against a control before any broader commitment.
CustomDynamic Yield does not publish public pricing. It operates on a custom, quote-based enterprise pricing model, with third-party industry estimates placing entry-level annual contracts around $35,000+ per year, scaling based on traffic volume, number of channels (web, app, email, offline), and which platform modules (Segmentation, Targeting, Recommendations, Journey Orchestration, Optimization, Search, AI Agents) are included. Prospective customers must book a demo directly with the Mastercard Dynamic Yield sales team to receive a specific quote.
Categories
AI E-commerce ToolsAI Marketing ToolsAI Developer APIs & Platforms
AI E-commerce Tools
Summary
Agentic product intelligence engine — make every product AI-ready everywhere it sells
Mastercard's enterprise AI personalization and experimentation platform, Experience OS
Lily AI Pros & Cons
Pros
- Tested, not promised — every result is measured against a matched-spend control with confidence intervals, so lift claims hold up to CFO scrutiny
- Broad surface coverage — one enrichment layer improves Google Ads, Meta Ads, AI discovery (ChatGPT, Gemini, AI Overviews), and onsite search simultaneously
- Proven enterprise results — +28% Google Shopping revenue lift, +21.4% Meta ROAS lift, +28.3% onsite revenue lift from real customer A/B tests
- 4.9/5 on G2 — the highest-rated product in its category with reviews from performance marketing and e-commerce teams
- No replatforming required — Lily Max improves data quality inside existing feed managers and commerce stacks without replacing anything
- Female-led, founder-run company — Purva Gupta named EY Entrepreneur of the Year 2024 Bay Area finalist; $63.9M raised from Canaan, NEA, Conductive Ventures
Cons
- Custom pricing only — no public rate card; teams must go through a sales/demo process to get a quote, which adds procurement friction
- Primarily a B2B enterprise SaaS platform — not suited for small DTC brands or Shopify merchants without a significant SKU catalog
- Focused on product data enrichment rather than end-to-end campaign management — teams still need existing feed managers and ad platforms
- Results depend heavily on starting catalog quality — brands with very thin or poorly structured product data may need significant data preparation before seeing lift
- No self-serve free tier — the free offering is a scoped 30-day pilot on 500 products, requiring a demo call first
- Smaller team (~34-94 employees) than enterprise martech competitors like Salesforce or Adobe — limited regional support capacity
Dynamic Yield Pros & Cons
Pros
- Named a Gartner Magic Quadrant Leader for Personalization Engines for eight consecutive recognition periods (2019-2025)
- Unified Experience OS architecture connects testing, personalization, and recommendations in one workflow instead of siloed tools
- Backed by Mastercard's infrastructure, security certifications (SOC II, ISO 27701/27017/27018), and enterprise-grade compliance (GDPR, CCPA)
- Shopping Muse and Experience OS Agents bring genuine AI-native conversational commerce and workflow automation to the platform
- Proven at massive scale: 400+ brands, tens of millions of daily transactions, and well-documented case studies (G Adventures +50% conversion, Ocado +55% add-to-cart)
- Open, agnostic architecture integrates with existing DMPs, web analytics, and tag managers rather than forcing a full stack replacement
Cons
- No public pricing — entry cost is reported around $35k+/year, positioning it firmly as an enterprise-only tool out of reach for small and mid-sized merchants
- Requires a demo and sales conversation to get any pricing information, slowing down evaluation for buyers who want quick comparisons
- Ownership under Mastercard (following the McDonald's-to-Mastercard acquisition history) may raise questions for some brands about long-term product roadmap independence
- Feature breadth (segmentation, targeting, recommendations, journey orchestration, optimization, search, AI agents) means a genuine learning curve and likely need for a dedicated CRO or personalization team
- Best suited to organizations with existing experimentation and personalization maturity; smaller teams may find the full platform more than they need