40-article series on cost-effective AI implementation in enterprise
The rapid commercialization of artificial intelligence capabilities has created a burgeoning market for reusable AI models that can be deployed across multiple tenants in shared cloud environments. However, the economic viability of such multi‑tenant AI ecosystems remains uncertain, as licensing structures must balance revenue generation with affordability and adoption incentives. This article ...
Edge AI deployments face competing objectives of latency, energy consumption, and operational expense. This article investigates placement strategies for AI inference at the edge, balancing these dimensions through a multi-objective optimization framework. We evaluate a range of deployment options across heterogeneous edge environments and present empirical results that quantify tradeoffs betwe...
Large language model (LLM) APIs consume a disproportionate share of operating budgets for AI‑driven products. This article investigates practical techniques for reducing token consumption while preserving output fidelity. We present a systematic analysis of prompt compression, caching strategies, dynamic model routing, and context‑window management, supported by benchmark experiments on publicl...
Internal AI platform teams face significant challenges in transparently charging back infrastructure costs to business units. Current metering approaches often lack fairness considerations and fail to provide clear adoption incentives. This article resolves critical gaps in cost attribution frameworks by analyzing state-of-the-art models and proposing a novel integrated approach. We address thr...
Enterprise AI workloads increasingly rely on batch inference to amortize GPU costs and improve throughput. However, the proliferation of batching strategies—continuous batching, static batching, and priority-aware scheduling—introduces complexity in selecting an optimal approach for production environments. This article addresses three critical research questions: (1) How do continuous and stat...
Quantized model formats have become central to cost-effective deployment of large AI models in enterprise environments. This article compares three prominent quantization formats—GGUF, ONNX Runtime with QDQ, and AWQ—focusing on accuracy–cost trade‑offs, integration complexity, and scalability in on‑premise settings. We formulate three research questions (RQ1, RQ2, RQ3) that guide the analysis: ...
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Model distillation comprises techniques for transferring knowledge from large, high-capacity neural networks—often referred to as teacher models—into compact, resource-efficient student models. While classic distillation paradigms have demonstrated modest accuracy gains, recent empirical investigations reveal that under carefully scoped enterprise conditions, distilled models can not only match...
Edge AI is reshaping real-time analytics across IoT, mobile, and on-premise environments, yet practitioners lack a unified cost model that captures the full spectrum of trade-offs. This article quantifies the economic implications of on-device inference versus cloud round‑trip inference at scale, integrating connectivity costs, latency requirements, and data‑sovereignty constraints. We introduc...
The rapid adoption of large language models (LLMs) for commercial applications has shifted focus from isolated inference to shared, multi‑tenant serving environments. While existing studies address scaling and latency optimization, they often neglect the equitable allocation of compute resources across distinct business units, leading to SLA violations and cost imbalance. This article investiga...