# Animica Train Quote

> Animica Train Quote is a paid API for AI agents from animica.dev, paid per call via x402, $0.001794/call, status unknown (last checked 2026-09-14).

Returns a cost and feasibility quote for a distributed machine learning training job before committing budget

## Facts

- Endpoint: POST https://animica.dev/x402/train/quote
- Price: $0.001794/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/animica-train-quote-80eff0e9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_tGXH_bb95nsMnfw_2pVgT

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 animica-train-quote-80eff0e9 -d '<json body>'
```

Example prompt: Give me a quote for training a linear_head classifier on my inline text dataset with labels using all-MiniLM-L6-v2, public distributed strategy, targeting 92% accuracy within an hour, and a budget of 1.00 USDC.

## When to prefer this

Use this endpoint when you want to estimate costs and feasibility of a distributed or federated ML training job on Animica before committing a USDC budget. It is ideal for AI agents that need to validate budget constraints, compare training strategies, or plan jobs programmatically using the x402 pay-per-call model. Prefer this over generic ML training APIs when you need privacy mode control, federated learning strategies, or on-chain micropayment billing.

## Known failure modes

- Invalid or unsupported base_model returns an error (only all-MiniLM-L6-v2 is supported)
- Missing required dataset field causes a validation error
- Budget amount too low to fulfill training requirements returns infeasibility response
- Invalid task type (not linear_head or lora_head) causes a bad request error
- Malformed dataset schema (missing texts or labels) causes a parsing error
- Incompatible privacy_mode and strategy combination may return an error or infeasibility

## How this service works

Animica Python Cloud: deploy a Python function to animica.dev, get a public endpoint, and earn ANM every time someone runs it. Free AI (OpenAI-compatible, no key), free scheduled Workers, and an 80/20 developer split.

## Output

A JSON object containing a cost quote and feasibility assessment for the requested training job, including estimated price, expected duration, whether the job is achievable within the given budget and requirements, and resource allocation details.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "task": {
   "type": "string",
   "description": "training method: linear_head | lora_head (default linear_head)"
  },
  "budget": {
   "type": "object",
   "description": "{\"amount\":\"1.00\",\"currency\":\"USDC\"}"
  },
  "dataset": {
   "type": "object",
   "description": "{\"texts\":[...],\"labels\":[...]} inline, or {\"dataset_id\":\"ds_...\"}"
  },
  "strategy": {
   "type": "string",
   "description": "population_search | federated_rounds"
  },
  "base_model": {
   "type": "string",
   "description": "frozen feature encoder (only all-MiniLM-L6-v2)"
  },
  "privacy_mode": {
   "type": "string",
   "description": "PUBLIC_DISTRIBUTED | PRIVATE_VERIFIED_WORKERS | LOCAL_ONLY"
  },
  "requirements": {
   "type": "object",
   "description": "{\"target_metric\":\"accuracy\",\"target_score\":0.92,\"max_duration_seconds\":3600}"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json"
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/animica-train-quote-80eff0e9/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from animica.dev](https://www.zero.xyz/host/animica.dev/llms.txt)