AI Tool Comparison
Comparing as AI Product Recommendations & PersonalizationLily AI vs Nosto
Lily AI
VS
Nosto
Verdict by Category
Detailed Comparison
Feature
Lily AI
Nosto
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.
CustomNosto does not publish fixed public pricing; it is quote-based and tailored per merchant via a demo request. Historically, Nosto started with a pay-on-performance model (charging only on conversions from recommendations), then moved to a fixed monthly license fee scaled to merchant revenue, and later introduced modular 'Build Your Own' plans letting merchants select and pay for specific products (recommendations, search, merchandising, A/B testing, etc.) rather than a single bundled tier. Enterprise features, professional services, and Huginn AI agent access are typically part of custom enterprise contracts. Merchants must contact Nosto's sales team or book a demo 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
AI-powered ecommerce personalization, search, and merchandising for growing brands
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
Nosto Pros & Cons
Pros
- Full-stack Commerce Experience Platform covering recommendations, search, merchandising, testing, and UGC in one system
- Modular 'Build Your Own' pricing lets merchants pay only for the products they actually use
- Huginn AI agent network adds genuinely new agentic capabilities beyond typical rules-based personalization
- Strong enterprise track record: 1,500+ brands across 100+ countries with well-documented case study results
- Native integrations with all major commerce platforms (Shopify Plus, Salesforce Commerce Cloud, Adobe Commerce, BigCommerce, Shopware)
- Designed to have no negative impact on site speed, addressing a common concern with personalization tools
Cons
- No public pricing — requires a demo and sales conversation to get a quote, which slows evaluation for smaller merchants
- Pricing is based on merchant revenue, meaning costs scale up automatically as a store grows, which can surprise fast-growing brands
- Enterprise-grade platform aimed at mid-market and larger retailers; likely overkill and cost-prohibitive for small independent stores
- Full value requires clean, well-instrumented first-party data, so stores with messy product or customer data need setup work before seeing strong results
- Deep feature set (personalization, search, merchandising, A/B testing, agentic AI) means a real learning curve for teams new to CXP-style tools