2.2 Limitations of Traditional Cloud and Centralized APIs

Mainstream centralized cloud and API providers (e.g., AWS, GCP) perform well during the training phase but reveal significant issues in the execution era:

• Rigid and unpredictable costs:Frequent inference requests — especially those involving long contexts or multi-turn interactions — make billing unpredictable. According to Clarifai’s analysis, training costs are periodic expenditures, while inference costs accumulate continuously and often exceed total training expenses. PYMNTS reports that companies are facing “unexpected AI usage bills,” with cloud invoices growing over 40% month-on-month (CloudOptimo data). NVIDIA’s blog points out that despite overall declines in inference costs, poor optimization still leads to high energy and computational overhead.

• Insufficient context reuse:Each model call often requires resending large context segments, resulting in extensive redundant computation. This redundancy amplifies costs in high-frequency scenarios due to a lack of efficient caching mechanisms.

• Single-point trust and compliance risks:Some industries require auditable inference chains and privacy guarantees, yet centralized services often lack adequate data sovereignty and auditability. The Stanford AI Index Report shows that the number of AI-related legislative mentions across 75 countries has increased ninefold since 2016, with an additional 21.3% increase from 2023 to 2024, highlighting growing privacy and compliance pressure.

• Lack of edge and hybrid deployment:Many use cases demand low-latency inference close to data sources, but centralized clouds are costly and complex to extend to the edge. According to Equinix, centralized processing creates a “latency tax,” where data transmission times cause network congestion and bottlenecks. WEKA reports that slow data access lowers GPU utilization, driving up costs and harming user experience. Deloitte further suggests that privacy, latency, and workflow demands are pushing a transition from centralized cloud toward edge or hybrid deployments.