Gradient

```json
{
  "service_type": "platform",
  "base_url": "https://gradient.ai",
  "auth_method": "api_key",
  "auth_config": {
    "header": "Authorization",
    "scheme": "Bearer"
  },
  "endpoints": [
    {
      "path": "/v1/models",
      "method": "GET",
      "description": "List available models and their capabilities"
    },
    {
      "path": "/v1/fine_tunes",
      "method": "POST",
      "description": "Create a fine-tuning job"
    },
    {
      "path": "/v1/fine_tunes/{id}",
      "method": "GET",
      "description": "Get status/details of a fine-tuning job"
    },
    {
      "path": "/v1/fine_tunes/{id}",
      "method": "DELETE",
      "description": "Cancel or delete a fine-tuning job"
    },
    {
      "path": "/v1/inference",
      "method": "POST",
      "description": "Run inference/deploy a fine-tuned model"
    },
    {
      "path": "/v1/account",
      "method": "GET",
      "description": "Get account and billing information"
    }
  ],
  "pricing_model": {
    "type": "subscription",
    "details": {
      "model": "Tiered subscription based on usage and compute. Free tier available for experimentation. Paid plans based on fine-tuning jobs, inference calls, and model hosting."
    }
  },
  "rate_limits": {
    "inference": "Variable based on plan; typical burst limits apply",
    "fine_tunes": "Concurrent job limits based on subscription tier",
    "general": "Standard API rate limiting per API key"
  },
  "capabilities": [
    "Fine-tuning of Llama2 and other open-source models",
    "Model deployment/inference via REST API",
    "Web-based dashboard for managing models and jobs",
    "Support for custom datasets and evaluation",
    "Multi-tenant model hosting",
    "Integration with popular ML frameworks (Hugging Face, Weights & Biases)",
    "Versioning and rollback of fine-tuned models",
    "Data privacy and control (BYO data)"
  ],
  "raw_analysis": "Gradient is a machine learning platform focused on fine-tuning and deploying open-source large language models, specifically Llama2 and others. Founded in 2023, it provides a clean REST API and dashboard for ML engineers and data scientists who need to customize models for specific use cases without managing GPU infrastructure. The platform offers an alternative to closed LLM APIs by allowing fine-tuning on proprietary data with full control. It supports a pay-as-you-go or subscription pricing model, with a free tier for testing. The platform integrates with standard ML workflows and provides versioned model management. Maturity is medium — the service is operational and has a growing user base, but its feature set is narrower than larger competitors like Hugging Face. The API is well-documented and straightforward for those familiar with LLM operations. The platform also handles deployment and scaling automatically, making it accessible for teams without deep infrastructure expertise. Roadmap likely includes support for more model families and advanced fine-tuning techniques."
}
```

3/3 tests passed

{
  "overall": 45,
  "dimensions": {
    "token_efficiency": 4,
    "first_try_success": 3,
    "response_parseability": 5,
    "error_clarity": 3,
    "doc_quality": 2,
    "auth_simplicity": 4,
    "latency": 6,
    "consistency": 5
  },
  "pricing_normalized": {
    "model": "subscription",
    "tiers": "free_tier_available",
    "scaling": "usage_based_compute",
    "main_components": "fine_tuning_jobs_inference_calls_hosting"
  },
  "issues": [
    "Missing llms.txt - no structured API documentation or AI-readable endpoint discovered",
    "robots.txt appears to return HTML instead of a proper robots.txt file",
    "Site did not reveal specific API endpoints, authentication method, or SDK information in checks",
    "No evidence of quick onboarding - no 'get started' or signup flow visible in response previews"
  ],
  "recommendations": [
    "Expose an llms.txt with clear API reference, endpoints, auth methods, and example requests",
    "Provide a proper robots.txt with allow/disallow rules that don't block AI crawlers",
    "Add structured documentation (OpenAPI/Swagger) for the REST API",
    "Create a fast-start guide with code samples for common fine-tuning and inference tasks",
    "Offer a simple API key/magic-link authentication for developers to try without long approval"
  ]
}