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ForgeMesh Embeddings API
x402
ForgeMesh Embeddings API is a paid API for AI agents from x402.forgemesh.io, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).
Generates 768-dimension embedding vectors for 1–64 texts per call, computed locally, paid per call with USDC via x402.
Price / call$0.002
Success—
StatusUnknown
How this service works
Embeddings API: 768-dimension embedding vectors for up to 64 texts per call, computed locally on our hardware — no OpenAI key, no account, pay per call with USDC. Use for semantic search, similarity, clustering, deduplication, and RAG indexes. Inputs are never stored.
Output
An array of 768-dimension floating-point embedding vectors, one per input text, suitable for semantic search, clustering, deduplication, or indexing into a vector database.
Example use cases
Example prompt
“Generate 768-dim embeddings for these three sentences so I can index them for semantic search: 'The cat sat on the mat.', 'A feline rested on a rug.', 'Dogs love playing fetch.' — pay per call with USDC, no OpenAI key needed.”
Technical details
Endpoint
POSThttps://x402.forgemesh.io/embeddingsWhen to prefer
Choose this endpoint when you need text embeddings without an OpenAI account or API key, want to pay per call in USDC with no subscription, require privacy (inputs are never stored), and need up to 64 texts vectorized in a single call at a low flat rate of $0.002 USDC.
Side effects
- More than 64 texts submitted in one call — returns validation error
- Empty texts array or missing both 'text' and 'texts' fields — returns 400 bad request
- Payment not provided or insufficient USDC — returns 402 Payment Required
- Individual text too long for model context window — may return truncation error or 400
- Network timeout if batch is large and hardware is under load
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | single text alternative to texts[] |
| texts | array | 1-64 texts to embed |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "nomic-embed-text",
"dimensions": 768,
"embeddings": [
[
0.011,
-0.02
],
[
0.03,
0.004
]
]
}
}Tags
x402
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