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CortexCloud Batch Embeddings API
x402
CortexCloud Batch Embeddings API is a paid API for AI agents from api.cortexcloud.org, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
Generates vector embeddings for a batch of up to 100 text strings in a single call, returning dense float arrays compatible with OpenAI embedding format
Price / call$0.005
Success—
StatusUnknown
How this service works
OpenAI-compatible AI and data API for agents. Pay per call in USDC on Base via x402 — no API keys, no subscriptions, no lock-in.
Output
Returns a JSON object with a data array where each item contains an index, object type ('embedding'), and the embedding float array for the corresponding input text. Also includes a usage object with total_tokens and prompt_tokens counts for the batch.
Example use cases
RAG pipeline document ingestion
“I have a list of 80 documentation paragraphs I need to embed — can you batch them all into vectors using text-embedding-3-small so I can store them in Pinecone for retrieval?”
Semantic similarity scoring
“Take these 30 customer support tickets and convert them to embeddings so I can cluster similar issues together — use the most accurate embedding model available.”
Duplicate content detection
“I've got 60 product listing descriptions and need to find near-duplicates — can you vectorize all of them in one shot so I can run cosine similarity across the results?”
Technical details
Endpoint
GEThttps://api.cortexcloud.org/x402/v1/embeddings/batchWhen to prefer
Choose this endpoint when you need to vectorize multiple texts in a single round-trip, saving latency and cost compared to calling a single-embedding endpoint repeatedly. It is ideal for RAG ingestion pipelines, semantic search index building, or clustering tasks where you have 2–100 texts ready to embed at once. The OpenAI-compatible response format means it slots directly into existing tooling. The pay-per-call USDC model on Base is preferable when you want no subscription overhead and programmatic micropayment billing for agents.
Side effects
- Input array exceeds 100 items — batch must be split into smaller chunks
- Invalid or unsupported model ID — returns error indicating unknown model
- Payment failure via x402 — call rejected if USDC payment on Base is not completed
- Empty input array — may return empty data array or validation error
- Texts too long for the model's context window — individual items may be truncated or rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | array | List of texts (max 100) to embed |
| model | string | Embedding model id |
Output
Schema · Response
{
"type": "object",
"format": "application/json",
"example": {
"data": [
{
"index": 0,
"object": "embedding",
"embedding": []
}
],
"usage": {
"total_tokens": 0,
"prompt_tokens": 0
}
}
}Tags
x402
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