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Inference402 Text Embeddings
x402
Inference402 Text Embeddings is a paid API for AI agents from inference402.worlddata402.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-19).
Generates dense vector embeddings for up to 32 text strings using BGE-M3 (1024 dimensions), paid per-call with USDC on Base via x402.
Price / call$0.002
Success—
StatusUnknown
How this service works
Pay-per-call AI inference for agents: chat (Llama 3.3 70B / 3.1 8B), structured JSON, summarize, translate (incl. Egyptian Arabic), extract, classify, sentiment, keywords, rewrite, embeddings, moderation, vision and speech-to-text. USDC on Base mainnet over x402. No API key, no account.
Output
Returns a JSON object with the embedding count, model name ('cf/baai/bge-m3'), number of dimensions (1024), and an array of float arrays — one 1024-dimensional embedding vector per input text string.
Example use cases
RAG pipeline document ingestion
“Embed these 5 product descriptions so I can store them in my Pinecone index for semantic search: 'Wireless noise-cancelling headphones with 30hr battery', 'Ergonomic standing desk with height memory', 'Mechanical keyboard with RGB backlight', 'Ultrawide curved monitor 34 inch', 'USB-C hub with 7 ports'.”
Semantic similarity scoring
“I need vector embeddings for both of these sentences so I can compute their cosine similarity: 'How do I reset my password?' and 'Steps to recover account access' — convert them to BGE-M3 vectors.”
Customer feedback clustering
“Turn these three customer reviews into embeddings so I can cluster them by topic: 'Shipping was incredibly fast', 'The product broke after one week', 'Customer support resolved my issue quickly'.”
Technical details
Endpoint
GEThttps://inference402.worlddata402.workers.dev/v1/embed?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need keywordless semantic embeddings (BGE-M3, 1024-dim) without API keys or account setup — ideal for agents that need pay-as-you-go vector generation via x402/USDC on Base. Prefer over OpenAI or Cohere embeddings when you want accountless micropayment access, multilingual coverage (BGE-M3 supports 100+ languages), or batch embedding up to 32 texts per call.
Side effects
- Payment failure: 402 returned if USDC payment on Base is missing or insufficient
- Input too large: array exceeds 32 items or individual text exceeds 2000 characters
- Empty input: minItems:1 violated returns validation error
- Network/worker timeout: Cloudflare Worker cold-start or inference latency spike
- Invalid JSON body: malformed texts array causes 400 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| texts | array |
Output
Schema · Response
{
"type": "json",
"example": {
"count": 2,
"model": "@cf/baai/bge-m3",
"dimensions": 1024,
"embeddings": [
[
0.012,
-0.034,
"..."
],
[
0.011,
-0.03,
"..."
]
]
}
}Tags
x402
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