{"uid":"cap_tGXH_bb95nsMnfw_2pVgT","slug":"animica-train-quote-80eff0e9","name":"Animica Train Quote","description":"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.","url":"https://animica.dev/x402/train/quote","method":"POST","headers":{},"bodySchema":{"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}"}}},"responseSchema":{"type":"json"},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001794","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.001794/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001794","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001794","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_bFPfMJ2CXXlBy86kOUja5","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001794","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Returns a cost and feasibility quote for a distributed machine learning training job before committing budget","exampleAgentPrompt":"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.","exampleUseCases":[{"title":"Budget check before federated training","prompt":"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?"},{"title":"Inline dataset classification cost estimate","prompt":"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?"},{"title":"Privacy-sensitive training feasibility check","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:42:11.214Z","isFirstParty":false}