7 de septiembre, 20258 minutesArchitecture

Local (WASM) vs Image CDN: 2025 architectural comparison

Latency, privacy, cost and control: when to optimize images in-browser vs delegating to an image CDN/service.

Summary

Choosing between a local (browser WASM) pipeline and an image CDN/service impacts total cost of ownership, scalability, privacy compliance, and iteration speed.

1. Models compared

  • Local (client): Decode/encode on device; zero upload of originals.
  • Dynamic CDN: On-demand variants (resize, WebP/AVIF) cached at edge.
  • Pre-processing build/CI: Generate responsive variants ahead of deploy.

2. Comparison table

Factor Local (WASM) Dynamic CDN Pre-processed
Initial latency Low (no round trip) PoP dependent Very low (static)
Variable infra cost 0 direct infra Pay per request/GB Build time cost
Privacy High (no raw upload) Medium (upload required) Depends host
Format rollout Instant (client) Instant (edge) Rebuild cycle
User control Max Medium High (not interactive)

3. Local approach advantages

  • Eliminates raw upload trip for heavy sources.
  • Improves perception (immediate optimization feedback).
  • Minimizes sensitive asset exposure surface.
  • Enables rapid param experimentation (tight loop).

4. CDN / server strengths

  • Globally cacheable hero assets & shared variants.
  • Offloads CPU from weak devices for heavy encodes (e.g., high-detail AVIF).
  • Advanced transformations (smart crop, face/subject detection) easier server-side.

5. Total cost of ownership (TCO)

(Insert formula: image volume * (GB saved * CDN egress/unit) vs engineering + maintenance of local pipeline.)

6. Decision criteria

Scenario Recommendation
Consumer editing tool Local + fallback CDN for heavy tasks
Large catalog e-commerce Dynamic CDN
Static marketing site Pre-processed + static CDN
Strict privacy / regulated media Prefer Local

7. Suggested hybrid model

  • Pre-generate baseline responsive sizes.
  • Client-side for interactive adjustments (extra compression/crop).
  • CDN to serve cached variants to end users.

8. Risks & mitigation

Risk Mitigation
High CPU on low-end mobiles Adaptive quality by hardware heuristics
Result inconsistency Centralized parameter schema
CDN cost growth Monitor transformed byte volume + cache hit ratio

9. Hybrid flow example

(User drags → local compress → uploads already reduced → CDN serves responsive set.)

10. Metrics to monitor

  • LCP p75 by asset role.
  • INP p75 on interactive routes.
  • % bytes saved vs originals.
  • Cache hit ratio (if CDN).
  • Time to first optimized preview (UX metric).

Conclusion

Not binary: layering strategies reduces cost while maximizing UX + privacy. Continuous measurement justifies each pipeline stage.

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