# TuneTrain.ai

**Canonical:** https://apis.io/providers/tunetrain/  
**Website:** https://tunetrain.ai/  
**APIs profiled:** 0

TuneTrain.ai provides a SaaS platform that enables businesses to fine‑tune small language models using their own data. Users can upload datasets in CSV or JSONL, augment them with AI‑generated variations, and apply LLM‑based distillation to improve quality. The service supports popular models such as Llama 3, Mistral, and Phi‑4, offering real‑time training monitoring, model hosting, and secure deployment. Built with enterprise‑grade security, it complies with SOC 2, EU AI Act, and provides extensive documentation and support for AI model customization.

## Kin Score — 8.7 / 100 (minimal)

Scored 2026-10-23 under rubric 0.25.0.

| Facet | Score |
|---|---|
| Discoverability | 48.2 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 0.0 |
| Access Clarity | 21.1 |

Regulatory layer — **Horizontal (data, software, accessibility, platform)**: 12.7 (matched via fallback).

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | no |
| Protected Resource Metadata | no |
| Dynamic Client Registration | no |
| Agentic Commerce | no |
| Pagination Ratio | na |
| Operation Distinctiveness | na |

## Security (1)

- **Tunetrain Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, Software-as-a-Service, Machine Learning, Fine-Tuning

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/tunetrain/). Scores are computed from the provider's own public artifacts under a published rubric.