OpenAI Embedding Cost Product Catalog Calculator
Estimate openai embedding cost to index a product catalog — titles, descriptions, metafields — for search, RAG, and recommendations. Model full re-embeds, incremental updates, and optional query-time embeddings.
1 Catalog size
2 Tokens per product
3 Re-index cadence
monthly_index_cost = full_cost × fulls_per_month × (1 + incremental_pct/100).
Example: 4 fulls + 15% incremental → multiplier 4 × 1.15.
4 Query-time embeddings (optional)
5 Cost estimate
| Model | $ / 1M tokens | Notes |
|---|---|---|
| text-embedding-3-small | $0.02 | Default — strong price/quality for catalogs |
| text-embedding-3-large | $0.13 | Higher dimensions / quality |
| text-embedding-ada-002 | $0.10 | Legacy; prefer 3-small for new indexes |
What an openai embedding cost product catalog calculator is for
Before you wire Shopify titles and descriptions into a vector search or RAG pipeline, you need a credible openai embedding cost product catalog budget. This tool multiplies products × tokens per product, applies your embedding model rate, then folds in full re-index cadence and incremental updates so monthly spend is not a surprise after launch week.
- Catalog embed — one full pass over every product document (title + description + extras).
- Re-index — fulls per month plus an incremental % of the catalog between those fulls.
- Query embeds — optional cost to embed each search query at query time.
- Per 1k products — scale the full-embed cost to compare catalog sizes quickly.
FAQ
How is monthly index cost calculated?
We use monthly_index_cost = full_cost × fulls_per_month × (1 + incremental_pct/100). Defaults: full cost $0.0232 × 4 × 1.15 ≈ $0.11/mo for indexing alone.
Should I use text-embedding-3-small or 3-large?
For most Shopify catalog search and RAG, 3-small is the default: ~6.5× cheaper than 3-large at list rates. Upgrade to 3-large when evals show retrieval quality gaps that matter to revenue.
Do query embeddings use the same model?
Yes in this calculator — query tokens are billed at the selected embedding model’s $/MTok. Retrieval + generation LLM costs are separate (see the RAG cost estimator).
Are Sep 2026 rates guaranteed?
No. OpenAI updates list prices. Always confirm on the official pricing page before budgeting. This tool is illustrative, not billing advice.
Supporting guide (openai embedding cost for product catalog) is coming next. Need a catalog search / RAG build? Work with Faraz or WhatsApp +92 334 3454747.