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Animica Train Quote
x402
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
Price / call$0.001794
Success—
StatusUnknown
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.
Example use cases
Budget check before federated training
“Before I commit any money, can you get me a quote to train a LoRA head on my stored dataset using federated_rounds strategy with PRIVATE_VERIFIED_WORKERS privacy, targeting 90% accuracy within 2 hours and a max budget of 5.00 USDC?”
Inline dataset classification cost estimate
“I have a small labeled text dataset I want to train a linear classifier on — can you quote me the cost using all-MiniLM-L6-v2 with public distributed mode and a 1.00 USDC budget, aiming for at least 88% accuracy?”
Privacy-sensitive training feasibility check
“Can you check if it's feasible to train a linear_head model on my dataset in LOCAL_ONLY privacy mode using population_search, with a target accuracy of 95% and a 3 USDC budget cap, and tell me what it would cost?”
Technical details
Endpoint
POSThttps://animica.dev/x402/train/quoteWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| task | string | training method: linear_head | lora_head (default linear_head) |
| budget | object | {"amount":"1.00","currency":"USDC"} |
| dataset | object | {"texts":[...],"labels":[...]} inline, or {"dataset_id":"ds_..."} |
| strategy | string | population_search | federated_rounds |
| base_model | string | frozen feature encoder (only all-MiniLM-L6-v2) |
| privacy_mode | string | PUBLIC_DISTRIBUTED | PRIVATE_VERIFIED_WORKERS | LOCAL_ONLY |
| requirements | object | {"target_metric":"accuracy","target_score":0.92,"max_duration_seconds":3600} |
Output
Schema · Response
{
"type": "json"
}Tags
x402
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