Describe how the state is shared
How many components need it, whether it crosses routes, and how much of it comes from the server are the strongest signals for which approach fits.
STATE STRATEGY PICKER
State Management Selector
Recommend an appropriate state-management approach — local state, context, a global store, or a server-state library — from your application's behaviour and complexity.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
Score each candidate strategy
Local state, Context, a global store, and a server-state library each earn points from the signals that favour them, so the winner is explainable, not guessed.
Get the highest-scoring approach with reasoning
The result names one primary strategy with the specific inputs that drove it, plus next steps for implementing it cleanly.
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Frequently asked questions
How is this different from a general tech-stack picker?
This tool only recommends a state-management approach — local state vs Context vs a global store vs a server-state library — not a full technology stack.
Can I need more than one strategy at once?
Yes, and that's common — server-synced data usually belongs in a server-state library while client-only UI state stays local or in Context. Run the tool once per distinct kind of state if your app mixes both.
Why does server-synced data score so differently?
Fetched data needs caching, revalidation, and loading/error handling that a hand-rolled store or local state doesn't provide out of the box, so it strongly favours a dedicated server-state library.
Does team size actually matter for this decision?
It's a secondary signal: larger teams benefit from the inspectability and conventions a shared store enforces, even when the state itself wouldn't strictly require one.
What if my result is close between two strategies?
A close score usually means either would work reasonably — pick the one with less new tooling for your team, since maintainability often matters more than the theoretical best fit.
How We Compare
| Feature | MVPHub | Redux Toolkit docs | TanStack Query docs |
|---|---|---|---|
| Weighs your specific app's sharing pattern | Included | Not included | Not included |
| Distinguishes client state from server-synced state | Included | Limited | Included |
| Library-agnostic recommendation | Included | Not included | Not included |
| Transparent scoring you can re-run as the app changes | Included | Not included | Not included |
Redux Toolkit's docs explain when Redux fits well but naturally advocate for Redux itself. TanStack Query's docs are excellent on server-state caching but don't address client-only state decisions. MVPHub scores your specific inputs across all four common approaches before you commit to a library.
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