Azion

With LoRA for efficient adaptation + serverless GPU architectures on Azion for distributed inference, teams can: • Cut latency • Improve resilience • Ship real-time AI experiences globally ...without inheriting a platform engineering nightmare. Learn more: azion.com/en/products/ai…

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.

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 👇]

Takeaway for CTOs: pushing delivery + compute closer to users + applying granular caching & media optimization can reduce origin dependency and make performance predictable while you scale. Want the full architecture + results breakdown? Read the full success story: azion.com/en/success-cas…

Efficiency win: 4.3 TB of bandwidth saved per day through automated image processing + optimization. Business impact followed: - 96% online sales growth (Q3 2021 vs. 2019) - Mobile app = 70%+ of digital sales - App conversion rate 50% higher than web - 86% user satisfaction rate

CTOs: when your mobile app drives 70%+ of digital sales, “good enough” performance becomes existential. Here’s how @voudemarisa (Brazil’s largest women’s fashion retail chain) revamped its architecture and delivered 85% of traffic via distributed infrastructure. 🧵