Show me a cost breakdown of running local embeddings versus using commercial APIs for a document search tool built on a tight budget
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Show me a cost breakdown of running local embeddings versus using commercial APIs for a document search tool built on a tight budget
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 1 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
2026-10 local embedding models sentence-transformers BGE small CPU inference requirements cost comparison embedding API pricing Google ↗
2026 embedding API pricing OpenAI text-embedding-3-small Cohere embed Voyage AI pricing per million tokens Google ↗
- text-embedding-3-small Model · developers.openai.com
- Pricing · docs.voyageai.com
- Speeding up Inference · sbert.net
- Local Embedding Models in 2026: Which to Run, What Hardware They Need, and What They Cost · maximem.ai
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2026-10 local embedding models sentence-transformers BGE small CPU inference requirements cost comparison embedding API pricing OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
2026 embedding API pricing OpenAI text-embedding-3-small Cohere embed Voyage AI pricing per million tokens OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |