Live retrieval / Workers AI / Same model both runs
The proof, run live.
Pick a question. Both runs hit the same Workers AI model with the same corpus. One stuffs all 12 cards into the prompt. The other retrieves the top 3 cards by cosine similarity. We capture token counts, latency, and cost in real time. No mock numbers.
Step 1 / pick a question
Or run with your own question
First run pays ~2-4 seconds. Subsequent loads hit KV cache.
Workers AI error
| Metric | Traditional RAG | Card Network | Delta |
|---|---|---|---|
| Input tokens | -- | -- | -- |
| Output tokens | -- | -- | -- |
| Total latency | -- | -- | -- |
| Cost (est) | -- | -- | -- |
| Cards in context | -- | -- | -- |
Traditional RAG answer
Model:
Card Network answer
Model:
Both answers come from the same Workers AI model, the same article corpus, the same question. Card Network retrieval used --% fewer input tokens. That savings compounds across millions of agent queries. It is the mechanical heart of the support-burn scenario, the AI-bill-spike scenario, and every CNA Knowledge Sprint we deliver.
Methodology
- ▮ Corpus: 12 cards, ~3,000 tokens, source: why-ai-agents-need-pre-chunking.json.
- ▮ Embeddings:
@cf/baai/bge-base-en-v1.5. Cosine similarity. Cards embedded once + cached in KV. - ▮ Text generation:
@cf/google/gemma-4-26b-a4b-it. Falls back to@cf/meta/llama-3.1-8b-instruct-fastif unavailable. Same model on both runs. - ▮ Cost band: $0.30/M input + $0.60/M output tokens (Workers AI Paid plan, public estimate).
- ▮ Caching: results cached 7 days per question in Workers KV. Add
?refresh=1to the API to force a fresh run.
Commercial intake
Saw the savings? Want them on your stack?
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