Lesson 1418 of 2116
AI LLM Routing Platforms: Martian, Not Diamond, OpenRouter
Compare model routing platforms that pick a model per request based on cost and quality.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2model routing
- 3cost optimization
- 4quality routing
Concept cluster
Terms to connect while reading
Section 1
The premise
Routing platforms cut spend by sending easy queries to cheap models — but hidden costs (latency, vendor risk) need care.
What AI does well here
- Route by predicted task complexity to cost-appropriate models.
- Provide unified API across providers.
- Track cost savings vs. always-frontier baseline.
What AI cannot do
- Predict quality without observing your specific workload.
- Replace your own eval suite.
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI LLM Routing Platforms: Martian, Not Diamond, OpenRouter”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 9 min
Vercel AI Gateway: When Model Routing Beats Direct Provider Integration
Direct integration with one model provider is fast to build; multi-model routing through a gateway becomes essential as use cases mature. The Vercel AI Gateway is one option — here's when it fits.
Creators · 11 min
AI Batch Inference Platforms for Bulk Workloads
When to send work through batch APIs (OpenAI Batch, Anthropic Message Batches, Bedrock Batch) versus realtime.
Creators · 24 min
Anthropic Batch API: Half-Price Claude for Async Workloads
Anthropic's Batch API runs Claude requests asynchronously at 50% off; the discipline is identifying which workflows can wait 24 hours.
