The most cost-effective way is usually to avoid building your own full comparison engine from scratch and instead build a thin workflow layer on top of a shopping search API or existing shopping feed/search provider.

Recommended approach

1. Use an external shopping search API as the product discovery layer

Instead of crawling retail sites yourself, use a provider that already aggregates product results, pricing, availability, and merchant metadata.

This is typically the cheapest because it reduces:

• crawling infrastructure
• anti-bot maintenance
• site-specific parsers
• data quality cleanup
• legal/compliance overhead

Look for APIs that provide:

• product title, price, merchant
• image and URL
• shipping/availability when possible
• stable identifiers or dedupable attributes
• pagination and filters
• bulk or discounted pricing at volume

2. Normalize only the fields you need

For comparison workflows, don’t ingest every available attribute. Store only:

• product name
• brand
• model / MPN / GTIN if available
• price
• shipping cost
• seller
• condition
• timestamp
• product URL
• image URL

This cuts storage, transformation, and matching costs.

3. Create a lightweight matching layer

The core value is usually deduplicating the same product across merchants.

Use a matching strategy like:

• exact matches on GTIN/UPC/EAN/MPN
• brand + model rules
• fuzzy text similarity for fallback
• image similarity only if needed, because it’s expensive

A cost-effective pattern is:

• exact match first
• heuristic match second
• ML/embedding only for uncertain cases

4. Cache aggressively

Search and product data change frequently, but not every query needs a live call.

Use:

• query-result caching
• product-level caching
• TTL based on category volatility
• incremental refresh for popular products

This can dramatically reduce API costs.

5. Precompute comparison sets

If users repeatedly compare the same categories or popular SKUs:

• precompute “top alternatives”
• precompute price-ranked merchant lists
• precompute normalized product clusters

This avoids recomputing similarity on every request.

6. Use a queue-based async pipeline

At scale, don’t make the whole workflow synchronous.

Typical pipeline:

1. Query search API
2. Normalize results
3. Deduplicate/match
4. Enrich or score
5. Store in comparison index
6. Serve from indexed data

Use background jobs for enrichment, and only do live search when needed.

7. Keep ranking simple

If you need a “best deal” or comparison ranking, start with rules:

• lowest total landed price
• trusted seller score
• availability
• shipping time
• return policy

Avoid expensive custom ranking models until you have enough traffic/data.

8. Build around a search index

Store normalized product records in a search/index layer like:

• Elasticsearch / OpenSearch
• PostgreSQL full-text + trigram for smaller scale
• vector DB only if semantic matching is truly needed

This helps with:

• fast lookups
• comparison views
• filtering
• duplicate detection

---

Cheapest viable architecture

Shopping search API → normalization service → matching/dedup service → indexed product store → comparison UI/API

This is usually the best cost/performance balance.

---

When to crawl yourself

Only consider crawling if:

• you need niche merchants not covered by APIs
• you need exclusive or highly custom data
• your query volume is so high that API costs exceed crawl costs
• you have strong compliance/legal support

Even then, start with a hybrid model:

• use API for broad coverage
• crawl only high-value missing merchants

---

Biggest cost drivers to watch

1. API query volume
2. Duplicate product matching
3. Frequent refreshes
4. Image/ML-based matching
5. Over-fetching irrelevant attributes
6. Poor caching

---

Practical cost-saving tips

• batch queries wherever possible
• cache by category and canonical product ID
• refresh popular items more often than long-tail items
• use exact identifiers first
• store raw results separately from normalized records
• monitor cost per matched product, not just per API call

---

If you want, I can also outline:

1. a low-cost MVP architecture,
2. a recommended tech stack, or
3. a build-vs-buy cost model for this workflow.