# 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.

- Canonical HTML: https://growth.iangoh.com/growth-ideas/task-based-model-routing-for-ai-speed/
- Source: [newsletter.posthog.com](https://newsletter.posthog.com/p/what-weve-learned-about-building)
- GrowthDex source hub: [PostHog Newsletter](/sources/posthog-newsletter-newsletter-posthog-com-2/)
- Last checked: 2026-05-26
- Rarity: rare
- Budget: medium
- Channels: Product, Activation, Retention
- Stages: ai products, latency, model ops, ux


## Why this can grow

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.

## Ian's take

From scaling consumer platforms across MENA and Southeast Asia, my default is to distrust growth work that only looks good in a slide. My bias is to treat this as a small market test first. Make the audience narrow, make the promise concrete, and let the first real response decide whether it deserves more work. I would run it small enough to learn quickly, then only scale the parts that real users repeat, save, reply to, or buy from. For this tactic, I would watch one clear growth signal before putting more time or budget behind it.

## Action plan

1. Define one narrow startup segment where task-based model routing for ai speed can create a measurable lift.
2. Turn the tactic into one offer, page, campaign, or workflow for the Product and Activation channel.
3. Use the evidence from newsletter.posthog.com to set the first version of the message, format, and audience.
4. Launch a small test for 7 to 14 days with one success metric: one measurable growth signal.
5. Review the result, keep the winning message, remove weak variants, and turn the learning into a repeatable growth playbook.

## 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.

## Adjacent tactics in the same lane

- [Async AI workflows with cached retries](/growth-ideas/async-ai-workflows-with-cached-retries/) - same source, 2 shared channels, 2 shared stages
- [Workflow-first AI demand validation](/growth-ideas/workflow-first-ai-demand-validation/) - same source, 3 shared channels, 1 shared stage
- [Uncertainty, source, and progress cues in AI UI](/growth-ideas/uncertainty-source-and-progress-cues-in-ai-ui/) - same source, 2 shared channels, 2 shared stages
- [Suggested prompts in the empty AI state](/growth-ideas/suggested-prompts-in-empty-ai-state/) - same source, 2 shared channels, 2 shared stages

## Read GrowthDex essays

Browse the plain-English essay index at [GrowthDex Blog](/blog/).

## Related GrowthDex essays

- [The AI feature only feels smart after the first useful minute](/blog/the-ai-feature-only-feels-smart-after-the-first-useful-minute/) - ai products, activation, product UX

## Advisory

If you want help turning this into a working growth system, Ian Goh offers advisory at https://iangoh.com/advisory.