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PennyRail text-embedding-3-large (Large Text Embedding)
x402
PennyRail text-embedding-3-large (Large Text Embedding) 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 high-dimensional vector embeddings for input text using OpenAI's text-embedding-3-large model, accessible via micropayment-gated API
Price / call$0.01
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns a high-dimensional float vector (embedding) representing the semantic content of the input text, produced by OpenAI's text-embedding-3-large model. The response is a JSON object containing the embedding array suitable for downstream use in vector databases, similarity calculations, or ML pipelines.
Example use cases
RAG pipeline document ingestion
“I'm building a retrieval-augmented generation system — can you embed this paragraph using text-embedding-3-large so I can store it in my Pinecone index? The text is: 'Our return policy allows customers to return any item within 30 days of purchase for a full refund.'”
Semantic similarity scoring
“I need to compare how semantically similar two pieces of text are. First embed this sentence for me using the large embedding model: 'The patient presented with acute chest pain and shortness of breath.'”
Query vectorization for search
“I'm running a vector search over my knowledge base — can you embed this user query with text-embedding-3-large so I can find the closest matching documents? Query: 'How do I reset my two-factor authentication?'”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/standard/ai.embed-large--text-embedding-3-largeWhen to prefer
Choose this endpoint when you need large, high-quality text embeddings (text-embedding-3-large dimensions) on a pay-per-call basis without managing your own OpenAI API keys or subscription. Ideal for agents that need occasional embedding calls via the x402 micropayment protocol without provisioning API credentials. Prefer this over self-hosted solutions when you want zero-infrastructure access to state-of-the-art embeddings at $0.01 USDC per call.
Side effects
- Missing or malformed 'input' object returns a 400 validation error
- Payment not included or insufficient USDC results in 402 Payment Required before processing
- Empty or null text input may return a zero-vector or error depending on model behavior
- Oversized input exceeding token limits (8191 tokens for text-embedding-3-large) may result in truncation or error
- Service unavailability on Vercel infrastructure returns 503
- Upstream OpenAI API rate limits or outages may cause intermittent failures
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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