Cross-Listing Tool Data Layer Architecture

An r/reselling post asked for cross-listing tool options. Most cross-listing SaaS (Vendoo, ListingMirror, Crosslist) focus on the orchestration UI; the data layer underneath is the hard part. This is the architecture that holds up when the tool grows past 1,000 users.

The four data needs

• Per-SKU pricing across marketplaces (Amazon, Walmart, Google Shopping, eBay).
• Per-SKU competition (how many sellers list the same product on each marketplace).
• Demand signal (Reddit threads, search volume trends).
• Pricing-history (for repricer logic).

What Scavio covers

Three of the four. Amazon search, Walmart search, Google Shopping search return per-SKU pricing and competition data. Reddit search returns the demand signal. eBay is not covered by Scavio; cross-listing tools that center eBay use eBay Browse API as the primary source and Scavio for everything else.

import os, requests
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

def per_sku(sku):
    return {
        'amazon': requests.post('https://api.scavio.dev/api/v1/amazon/search',
            headers=H, json={'query': sku}).json(),
        'walmart': requests.post('https://api.scavio.dev/api/v1/walmart/search',
            headers=H, json={'query': sku}).json(),
        'shopping': requests.post('https://api.scavio.dev/api/v1/search',
            headers=H, json={'query': sku, 'search_type': 'shopping'}).json(),
        'reddit': requests.post('https://api.scavio.dev/api/v1/reddit/search',
            headers=H, json={'query': sku}).json(),
    }

The cost math

Per SKU per day across 3 Scavio marketplaces = 3 credits = ~$0.013/day. A cross-listing tool tracking 1,000 SKUs daily across 3 marketplaces uses 3,000 credits/day = ~$0.39/day = ~$12/mo. That is the data layer cost; the SaaS sells at $29-99/user/mo.

The pricing-history piece

Scavio returns current snapshots, not historical prices. Pricing history requires storing snapshots over time. The right architecture: Scavio pulls daily, you store in your own database (SQLite or Postgres), reprice logic queries history from your DB. Scavio handles the live data; you handle the history.

Where Keepa fits

Keepa specializes in Amazon historical pricing. For cross-listing tools that emphasize Amazon-first repricing (most do), Keepa adds value Scavio does not. The right stack at that scale: Keepa for Amazon history, Scavio for cross-marketplace live data, eBay Browse API for eBay.

The Reddit signal layer

r/Flipping and r/reselling discuss what is moving in the past 7 days. A reddit_search query for the SKU surfaces those threads. Most cross-listing tools ignore this signal and miss the early-adopter advantage; the ones that surface it (via Scavio or similar) sell into the power-user segment as a differentiator.

The MCP piece

Cross-listing tools that ship a Claude Code skill (for the power-user segment) attach mcp.scavio.dev/mcp. The skill becomes "price this SKU across Amazon and Walmart" and returns structured comparable listings. Sellers who also use Claude Code stack the cross-listing tool's UI with Claude-driven analysis.

Honest constraints

eBay coverage is the gap. Scavio does not return eBay data in 2026. Cross-listing tools that center eBay (most do) need eBay Browse API regardless. The cleanest architecture: eBay Browse for eBay, Scavio for Amazon + Walmart + Google Shopping, Keepa for Amazon historical. Three vendors instead of one, but each is purpose-fit.

What ships in MVP

Per-SKU live pricing across 3 marketplaces (Scavio). Manual eBay entry (most cross-listing SaaS does this anyway in early MVP). Daily cron + SQLite pricing history. UI that shows the cheapest current price across marketplaces. That is enough to validate the SaaS; eBay Browse API and Keepa get added in v2.