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PennyRail Large Embedding Service
x402
PennyRail Large Embedding Service is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).
Generates large-scale dense vector embeddings from an input object via a paid x402 micro-settlement endpoint
Price / call$0.01
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns a JSON object containing the dense vector embedding produced by the large embedding model for the provided input, suitable for downstream semantic search, clustering, or similarity computation tasks.
Example use cases
RAG pipeline document ingestion
“I need to embed this paragraph from our internal knowledge base so I can index it in Pinecone for retrieval-augmented generation — here's the text: 'Our refund policy allows returns within 30 days of purchase with original receipt.'”
Semantic similarity search setup
“Take this customer support query and convert it into a large embedding vector so I can find the most semantically similar tickets in our database: 'My order arrived damaged and I need a replacement.'”
Content clustering for recommendation engine
“Generate a large embedding for this article headline so I can group it with similar content in our recommendation system: 'How central banks manage inflation through interest rate policy.'”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/standard/ai.embed-large--embed-largeWhen to prefer
Choose this endpoint when you need large-model-quality embeddings with per-call micro-payment pricing via x402/USDC, especially in agentic pipelines where you want pay-as-you-go embedding without API key management. Prefer it over smaller embedding endpoints when semantic fidelity and vector dimensionality matter for downstream retrieval quality.
Side effects
- Missing or malformed 'input' field returns a validation error
- Payment not settled via x402 protocol results in 402 Payment Required response
- Input object structure not understood by embedding model may return an empty or error response
- Network timeout on Vercel serverless cold start may cause latency spikes
- Overly large input may exceed model token limits
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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