🚏 Routing saves 30–60% on AI cost when done well

PM LLM Routing
(2026 Edition)

5 routing strategies and 4 pitfalls.

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5 Strategies

1.

Tier by complexity — small/medium/large model per task

2.

Route by latency budget — fast models for inline, slow for async

3.

Route by safety — sensitive tasks to vetted models

4.

Use multiple vendors for resilience

5.

Cache aggressively before routing

4 Pitfalls

Routing based on cost alone — quality regressions show up later

No A/B testing of routing decisions

Routing logic that's a black box no one understands

Vendor lock-in through tightly-coupled tooling

FAQ

Is model routing worth the engineering complexity?

For products with material AI cost, yes. Routing typically saves 30–60% on model spend without quality regression. The engineering investment pays back fast at scale. For small or experimental products, default to one good model and optimise later.

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