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PennyRail AI Embed Small
x402
PennyRail AI Embed Small is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
Generates small-scale vector embeddings for text or structured input via a paid x402 micropayment-gated API endpoint
Price / call$0.005
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns a JSON object containing the vector embedding (numerical array) representing the semantic content of the input, suitable for downstream tasks like similarity search, clustering, or retrieval-augmented generation.
Example use cases
Semantic search index builder
“Take this list of product descriptions and embed each one using the small embedding model so I can index them for semantic search.”
Document similarity comparison
“Embed these two paragraphs with the small model and give me their vector representations so I can compute how similar they are.”
Clustering user feedback
“I have a batch of short customer reviews — embed each one using the small embedding model so I can cluster them by topic.”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/intel/ai.embed-small--embedWhen to prefer
Choose this endpoint when you need lightweight, cost-efficient vector embeddings via a micropayment x402 model and do not require a large embedding dimension. It is ideal for agents operating in pay-per-call pipelines where cost control per embedding call matters ($0.005 USDC), and when integrating with Coinbase-compatible x402 payment flows.
Side effects
- Missing or malformed 'input' field returns a validation error
- Payment not provided or insufficient USDC (x402 payment required before processing)
- Input object too large or unsupported type may return a 400 error
- Service unavailability on Vercel deployment returns 5xx
- Ambiguous or empty input may return a zero-vector or error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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