The pitch on dataweave.com is direct: "AI-powered e-commerce analytics for digital commerce." Pricing, assortment, product matching, share of search — across retailer catalogs, served to brand customers as real-time intelligence. Three workloads stacked on top of each other, each with its own infrastructure problem.
Three primitives that map directly to how DataWeave actually runs:
Workers + Browser Rendering — managed headless Chromium for JS-heavy retailer sites. Replaces Lambda + EC2 crawler fleets and the scaling/cost overhead that comes with them.
R2 — zero-egress object storage for crawled HTML, images, and product feeds. The S3 egress-per-customer-query bill is the silent margin tax on every analytics product; R2 erases it.
AI Gateway — in front of Claude (already in production). Semantic cache on repeated SKU attribute extractions, per-tenant cost attribution across your brand customer base, fallback when Anthropic rate-limits.
Is the bigger near-term pain on the crawling side — managing the EC2 fleet that pulls down JS-heavy retailer pages — or on the inference economics side — keeping per-brand AI cost predictable as customer count grows? 20 minutes to find the right starting point.
The detailed primitive-by-primitive mapping — including the request-flow diagram for a brand-customer query on Cloudflare, the Workers + Browser Rendering crawler architecture, the egress-tax math, and the 90-day rollout — runs about 45 KB of dense technical content.
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