Lesson 1067 of 2116
On-Device AI vs Cloud AI: When Each Wins
On-device AI (local inference) and cloud AI have distinct trade-offs. Both have growing roles in production.
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What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2on-device AI
- 3cloud AI
- 4edge inference
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Terms to connect while reading
Section 1
The premise
On-device and cloud AI serve different needs; many production systems use both.
What AI does well here
- Use on-device for: latency-sensitive (no network round-trip), privacy-sensitive (data stays local), offline-capable use cases
- Use cloud for: compute-heavy (large models), centrally-updated, internet-dependent use cases
- Build hybrid architectures where appropriate
- Monitor user experience across both paths
What AI cannot do
- Eliminate the trade-offs (different deployments, different complexity)
- Substitute on-device hype for actual capability assessment
- Make hybrid free (it's more complex than either alone)
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