Analyzing DeepSeek’s Availability Issues and Applying Traditional Internet Reliability Strategies to AIGC

The article begins by documenting DeepSeek’s recent availability problems, including degraded web/API performance, major outages, and continuous service interruptions that affect both free and paid users, often resulting in empty responses or timeouts.

It then highlights fundamental differences between AIGC products and conventional internet services, noting that AI models demand heavy GPU resources, have variable response times, exhibit nondeterministic outputs, and require specialized fault‑detection and rollback mechanisms, whereas traditional systems rely on predictable database queries, caching, and well‑established high‑availability patterns.

To address these challenges, the author suggests borrowing three core reliability strategies from traditional internet engineering:

Isolation : implement database read/write separation, micro‑service boundaries, multi‑region deployments, and for AIGC specifically isolate model inference services, cache versus real‑time inference, and separate free from paid user workloads.

Rate Limiting : apply token‑bucket or leaky‑bucket algorithms, IP‑level limits, and user‑tier quotas; for AI services also enforce API request caps, task‑queue management, token‑based usage limits, and dynamic throttling based on current load.

Elastic Scaling : use cloud auto‑scaling, load balancers, and hot/cold data separation for traditional apps; for AIGC dynamically schedule GPU resources, run multiple model replicas, and batch inference tasks to handle spikes efficiently.

The piece concludes by outlining four core contradictions faced by AIGC services—imbalanced compute demand versus scheduling, feature complexity versus stability, user expectations versus disaster recovery, and commercial pressure versus technical investment—emphasizing that future competitiveness will depend as much on reliability as on model intelligence.