# 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 unknown (last checked 2026-09-15).

Generates small-scale vector embeddings for text or structured input via a paid x402 micropayment-gated API endpoint

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/p/intel/ai.embed-small--embed
- Price: $0.005/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-ai-embed-small-6f64bc06
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gdjorJuSS2LSUApaRyuLx

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-6f64bc06 -d '<json body>'
```

Example prompt: Embed this sentence using the small embedding model: 'The quick brown fox jumps over the lazy dog' — I need the vector output for a semantic similarity lookup.

## When to prefer this

Choose this endpoint when you need lightweight, cost-efficient vector embeddings via a micropayment x402 model and do not require a large embedding dimension. It is ideal for agents operating in pay-per-call pipelines where cost control per embedding call matters ($0.005 USDC), and when integrating with Coinbase-compatible x402 payment flows.

## Known failure modes

- Missing or malformed 'input' field returns a validation error
- Payment not provided or insufficient USDC (x402 payment required before processing)
- Input object too large or unsupported type may return a 400 error
- Service unavailability on Vercel deployment returns 5xx
- Ambiguous or empty input may return a zero-vector or error

## How this service works

Machine-readable settlement service

## Output

Returns a JSON object containing the vector embedding (numerical array) representing the semantic content of the input, suitable for downstream tasks like similarity search, clustering, or retrieval-augmented generation.

## 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-6f64bc06/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)