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.
Source-backed 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-reported evidence
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.
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
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.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to Product and Activation, using evidence from PostHog Newsletter as context—not as a promised result.
- Record a baseline for: Target users who complete the tactic’s intended next action. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
Compare a stack for your situation
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