Stop optimizing cloud costs by shopping for better pricing.
We measured TCO across cloud vs serverless edge architectures.
The biggest cost differences came from architecture, not location:
→ 65% lower infra costs
→ 87% fewer maintenance hours
→ 68% less deployment effort
Full framework: https://t.co/4Cy5vmj2vk
#CloudComputing #DevOps
Full breakdown of the migration:
• Monolith vs individual packages comparison • CI/CD workflow before/after • Migration phases and timeline • How it affects your workflow
Read here: https://t.co/ayT1zVsuaS
Clear changelogs: Each package maintains its own CHANGELOG.md. No noise from unrelated modules.
Better tree-shaking: Import from @aziontech/storage → bundlers eliminate unused code more effectively.
Update control: Update only what's necessary. Keep stable versions in critical modules.
If you're still running GenAI inference from a single cloud region and calling it "production-ready," your users are paying the latency tax:
• Jittery responses
• Inconsistent UX
• Brittle failover
Here's why centralized architectures fail AI — and what works instead:
[thread continues 👇]
The bottleneck for AI inference adoption in 2026 isn't hardware.
It's operational maturity.
Serverless platforms that standardize infrastructure + simplify model consumption = faster Time-to-Market + resilient AI apps.