Optimizing AI model routing for cost, speed, and quality.
AI Model Routing Strategist
LLM providers, cost-quality tradeoffs
Best for
- ▸Design cost-optimized routing across OpenAI, Anthropic, and open-source models for customer support chatbots
- ▸Implement cascading strategies that try Claude Haiku first, escalate to GPT-4 for complex reasoning tasks
- ▸Build confidence-based routing that automatically retries failed requests through backup providers
- ▸Create prompt caching architectures that reduce token costs by 50%+ for repetitive enterprise workflows
What you'll get
- ▸Complete routing decision tree with specific confidence thresholds, cost calculations per request type, and Python implementation using async queues
- ▸Multi-tier cascading strategy document with escalation triggers, provider-specific circuit breakers, and monitoring dashboards setup
- ▸Cost optimization framework showing before/after spend projections, quality benchmarks, and latency impact analysis across different routing strategies
Detailed workload characteristics including request types, volume estimates, quality requirements, latency constraints, and current cost baseline across multiple LLM providers.
Complete routing architecture with specific model selection logic, cascading rules, cost projections, fallback chains, and implementation code for production deployment.
What's inside
“You are an AI Model Routing Strategist. You help teams choose the right AI model for each task by evaluating cost, latency, quality, and context window tradeoffs, then designing routing logic that sends each request to the optimal model. - Reject "one model for everything" architectures. A classific...”
Covers
Not designed for ↓
- ×Training or fine-tuning custom models from scratch
- ×Building the actual LLM inference infrastructure or serving endpoints
- ×General API integration without cost/quality optimization strategy
- ×Single-model optimization or prompt engineering for one provider
SupaScore
90▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 7 sources (3 paper, 3 official docs, 1 web)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
v5 rewrite: behavior-focused, 90% shorter, no filler
Pipeline v4: rebuilt with 3 helper skills
Initial release
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
LLM Cost Optimization Pipeline
Design routing strategy, implement quality benchmarks, then deploy monitoring for continuous optimization of multi-model systems
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