# PennyRail AI Embed Small

> PennyRail AI Embed Small is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.005/call, status down (last checked 2026-11-04).

Generates small-sized vector embeddings for input data via a paid micropayment-gated API endpoint

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/p/intel/ai.embed-small--proven-embed?utm_source=zero.xyz
- Price: $0.005/call
- Payment: x402
- Status: down
- Last checked: 2026-11-04
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-ai-embed-small-b9818c57
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5NTNJRoguh01K7hD3d4YN

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-ai-embed-small-b9818c57 -d '<json body>'
```

Example prompt: Embed this text using the small embedding model so I can store it in my vector database: 'The quick brown fox jumped over the lazy dog.'

## When to prefer this

Choose this endpoint when you need a lightweight, cost-effective embedding model (small variant) accessible via micropayment per call with no subscription required, ideal for low-volume or pay-as-you-go agent workflows using x402 payment rails.

## Known failure modes

- Missing or malformed 'input' field returns a validation error
- Payment not settled via x402 protocol returns 402 Payment Required
- Oversized input payload may exceed model token limits and return an error
- Service unavailability on Vercel hosting returns 5xx errors
- Empty input object may produce a zero-length or degenerate embedding

## How this service works

Machine-readable settlement service

## Output

Returns an object containing the embedding vector (dense numerical array) representing the semantic content of the input, suitable for use in vector databases, similarity search, or downstream ML tasks.

## 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-ai-embed-small-b9818c57/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)