# PennyRail AI Embeddings (proven-v1)

> PennyRail AI Embeddings (proven-v1) is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.002/call, status down (last checked 2026-10-22).

Generates vector embeddings for input data using a proven v1 embeddings model, accessible via micropayment-gated API

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--proven-v1-embeddings?utm_source=zero.xyz
- Price: $0.002/call
- Payment: x402
- Status: down
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-ai-embeddings-proven-v1-04b4a16e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HT4xZplRQ6gkMecsqvyNa

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-embeddings-proven-v1-04b4a16e -d '<json body>'
```

Example prompt: Generate vector embeddings for this text using PennyRail's proven-v1 embeddings model so I can use them for semantic similarity search: 'The quick brown fox jumps over the lazy dog.'

## When to prefer this

Choose this endpoint when you need pay-per-call vector embeddings with no subscription commitment, priced at $0.002 USDC per call via x402 micropayments. Ideal for agents running in crypto-native or x402-compatible environments that want to avoid API key management and prefer on-demand billing. Not ideal if you need guaranteed SLA, bulk discounts, or a well-documented schema.

## Known failure modes

- Missing required 'input' field returns validation error
- Payment not provided or insufficient USDC causes 402 Payment Required
- Malformed input object may return 400 Bad Request
- Service unavailability on Vercel deployment returns 503
- Empty or null input may produce degenerate embeddings or an error

## How this service works

Machine-readable settlement service

## Output

Returns an object containing the vector embedding (numerical array) representing the input, suitable for downstream similarity, search, clustering, or ML tasks. The response schema is open-ended (additionalProperties: true), so exact field names may vary but will include embedding data.

## 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-embeddings-proven-v1-04b4a16e/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)