Task-based model routing for AI speed
Route lightweight jobs to smaller fast models and reserve larger models for harder reasoning so the product feels quick without giving up depth where it matters.
Why this can grow a startup
Users often judge AI quality through speed before they have the evidence to judge reasoning. If simple tasks feel slow, the whole feature starts to look expensive and theatrical. Task-based routing protects the fast path. It lets the product answer lightweight questions quickly, keeps heavier reasoning available when the job really needs it, and gives the team a cleaner way to manage cost, latency, and trust together instead of pretending one model should handle every task equally well.
Company example
PostHog routes work across Claude Sonnet 4, GPT-4.1 mini, and GPT-4.1 so faster models handle simpler requests while heavier models are saved for harder jobs.
Source and metric
Source: PostHog Newsletter · Browse PostHog Newsletter tactics
PostHog explicitly mixes fast and slow models by task instead of running one default model for every request.
Source discovered: May 26, 2026
When to use it
Use this when Product, Activation, Retention is relevant to ai products, latency, model ops and you can run a bounded test with a medium budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
Founder checklist
- Read PostHog Newsletter and identify what is directly supported.
- Choose one channel context: Product, Activation, Retention.
- Define the test around PostHog explicitly mixes fast and slow models by task instead of running one default model for every request..
- Set an owner, evidence window, and stop condition before launch.
Explore the context
Apply this with an operator
Connect activation, customer value, retention, and referral into one measurable loop.