# PennyRail Batch Text Embeddings

> PennyRail Batch Text Embeddings is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).

Generates vector embeddings for a batch of text inputs, payable per-call via x402 micropayment protocol

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--batch-embeddings
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-batch-text-embeddings-0c70027c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_E0ts9El7nD8IClUFKee9v

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability pennyrail-batch-text-embeddings-0c70027c -d '<json body>'
```

Example prompt: Generate batch embeddings for these five product descriptions so I can index them for semantic search: 'wireless headphones', 'noise-cancelling earbuds', 'Bluetooth speaker', 'USB-C audio adapter', 'studio monitor speakers'.

## When to prefer this

Choose this endpoint when you need to embed multiple texts in a single call using a pay-per-use micropayment model (x402/USDC), especially in agent pipelines where spinning up your own embedding infrastructure is overkill and you want to pay only for what you use. Prefer over self-hosted alternatives when low operational overhead and per-call billing matter more than throughput volume.

## Known failure modes

- Missing or malformed 'input' object returns a 400-level validation error
- Payment not attached or insufficient USDC results in 402 Payment Required response
- Oversized batch may exceed compute limits and return a timeout or payload-too-large error
- Invalid x402 payment header causes settlement failure and request rejection
- Empty input object may return empty embeddings or an error depending on server handling

## How this service works

Machine-readable settlement service

## Output

Returns an object containing vector embedding arrays corresponding to each input text, suitable for downstream tasks such as semantic similarity, clustering, classification, or retrieval.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "object",
 "additionalProperties": true
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/pennyrail-batch-text-embeddings-0c70027c/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from pennyrail.vercel.app](https://www.zero.xyz/host/pennyrail.vercel.app/llms.txt)