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x402.shizu.me OpenAI-Compatible Embeddings
x402
x402.shizu.me OpenAI-Compatible Embeddings is a paid API for AI agents from x402.shizu.me, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-20).
Generates text embeddings via a drop-in OpenAI-compatible endpoint, paid per call in USDC with no account or API key required.
Price / call$0.002
Success—
StatusUnknown
How this service works
Drop-in OpenAI embeddings endpoint — same base_url as /v1/chat/completions, pay per call in USDC, no account or key. POST {input: string|array (max 16 items, 8KB total), model?, dimensions?}. Returns the standard {data:[{embedding:[...]}], usage} shape. Flat price per call. The bulk-loop primitive for RAG ingestion, semantic search, dedup, clustering — point your OpenAI SDK here and embed.
Output
Returns a standard OpenAI-shaped JSON response with a `data` array of embedding objects (each containing an `embedding` float array and an `index`), plus a `usage` object with token counts. The response is structurally identical to OpenAI's /v1/embeddings output, making it a drop-in replacement.
Example use cases
RAG document ingestion pipeline
“I need to embed these 12 text chunks from my knowledge base so I can store them in Pinecone for retrieval-augmented generation — can you vectorize them all in one call?”
Semantic deduplication of user content
“Take these 8 user-submitted product reviews and generate embeddings for each so I can cluster them and remove near-duplicates before storing them.”
Semantic search index building
“Convert these 10 FAQ answers into embedding vectors using the text-embedding-3-small model so I can build a semantic search index for my support chatbot.”
Technical details
Endpoint
POSThttps://x402.shizu.me/v1/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need OpenAI-compatible embeddings without wanting to manage API keys or accounts — ideal for agent pipelines that want pay-per-call USDC billing, or when you want to drop in a compatible embeddings endpoint alongside the same provider's chat completions. Best for batches up to 16 items; for larger bulk jobs, loop calls here rather than using a managed embedding service.
Side effects
- Input exceeds 16 items or 8KB total — request rejected with an error
- Payment fails or USDC balance insufficient — call returns payment error
- Unsupported model name passed — returns model-not-found error
- Malformed input (non-string, null) — returns validation error
- Network timeout if input is near the size limit and processing is slow
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | — | |
| model | — |
Tags
x402
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