slug: ai-gateway
provider: AI Gateway
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Software & Technology
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 2
edges:
- tag: Fine Tuning
  spec_file: ai-gateway-fine-tuning-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.8
  evidence: POST /fine_tuning/jobs  Create a fine-tuning job; GET /fine_tuning/jobs  List fine-tuning
    jobs
  reason: Operations create and track model fine-tuning jobs — ML model lifecycle / MLOps work, which
    maps to Artificial Intelligence Management rather than any technical plumbing capability.
- tag: Policies
  spec_file: ai-gateway-policies-api-openapi.yml
  capability_id: BC-4270.50
  capability_id_l1: BC-4270
  capability_name: API Consumption Governance
  confidence: 0.7
  evidence: POST /policies/rate-limits  Create a rate-limit policy; GET /policies/usage-limits  List usage-limit
    policies
  reason: Rate-limit and usage-limit policy management over API consumption matches API Consumption Governance
    (quotas, rate limits, usage). Some chance this is purely internal throttling config rather than ecosystem
    governance.