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PennyRail AI Small Text Embeddings
x402
PennyRail AI Small Text Embeddings is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Generates dense vector embeddings for text input using a small embedding model, accessible via pay-per-call settlement.
Price / call$0.005
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns a JSON object containing the dense vector embedding (float array) representing the semantic content of the input text, generated by the small embedding model. The response schema is open-ended and may include the embedding array, model metadata, and token usage.
Example use cases
RAG pipeline document indexing
“Turn this paragraph into a vector embedding so I can store it in my vector database for retrieval-augmented generation: 'The Federal Reserve raised interest rates by 25 basis points in its latest meeting.'”
Semantic similarity search
“Embed this user query so I can find the most semantically similar documents in my knowledge base: 'What are the side effects of ibuprofen?'”
Text clustering for topic modeling
“Convert each of these customer feedback snippets into embeddings so I can cluster them by topic — start with: 'Your checkout process is way too complicated and takes forever.'”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/intel/ai.embed-small--text-embeddingsWhen to prefer
Choose this endpoint when you need cost-effective, low-latency text embeddings at $0.005 USDC per call and can tolerate a smaller model's representational capacity. Ideal for high-volume embedding pipelines (RAG, semantic search, clustering) where per-call cost matters and a 'small' model quality tier is sufficient. Prefer over larger embedding models when budget or throughput is the primary constraint.
Side effects
- Missing or malformed 'input' object returns a 400 validation error
- Payment not fulfilled via x402 protocol results in a 402 Payment Required response
- Input text too long for the small model context window may cause truncation or error
- Upstream model unavailability on Vercel infrastructure causes 503 errors
- Empty input object may return a zero-vector or error depending on implementation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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