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PennyRail Embedding API (ai.v1-embeddings)
x402
PennyRail Embedding API (ai.v1-embeddings) is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.002/call, status unknown (last checked 2026-11-01).
Generates vector embeddings for input data via a paid micropayment-gated API endpoint on PennyRail
Price / call$0.002
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns an object containing the computed embedding vector (numeric float array) representing the semantic content of the input, usable directly in vector databases, similarity computations, or ML pipelines. The response schema is open-ended, so additional metadata fields may be included alongside the embedding.
Example use cases
Semantic search index population
“I need to embed a product description — 'Wireless noise-cancelling headphones with 30-hour battery life' — into a vector so I can store it in my semantic search index.”
RAG pipeline document encoding
“Take this paragraph from my knowledge base and turn it into an embedding vector so my retrieval-augmented generation system can do similarity lookups against it.”
Duplicate content detection
“Can you generate embeddings for these two support ticket summaries so I can measure their cosine similarity and flag them as duplicates if they're close enough?”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--embedding-api?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a micropayment-gated, x402-compatible embedding API that integrates natively with Coinbase's payment infrastructure. It is well-suited for AI agents that already operate within the x402 pay-per-call ecosystem and need to generate embeddings on demand without a pre-provisioned API key or subscription. Prefer alternatives like OpenAI or Cohere embeddings if you need guaranteed SLAs, well-documented schemas, or high-volume batch processing.
Side effects
- Payment not provided or insufficient USDC (402 Payment Required)
- Malformed or missing 'input' field in request body (400 Bad Request)
- Input object type not supported by the underlying embedding model
- Service unavailable or cold-start latency on Vercel serverless (503/504)
- Response schema is fully open-ended, so embedding field name must be inferred at runtime
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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