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Cloud Cost Optimization Solutions for Media & Entertainment
Executive Summary
AI workloads are the new frontier of cloud waste. ZOLIX AI provides the only Sovereign AI FinOps engine for the LLM era, analyzing token-level attribution and GPU cluster rightsizing.
Economic Impact
30% increase in inference throughput per dollar by eliminating VRAM hoarding and optimizing KV cache hits.
Strategic Objectives
01
Token Attribution
Mapping every LLM inference call to a specific user, business unit, or project ID.
02
GPU Density Tuning
Maximizing VRAM utilization to prevent unnecessary cluster scaling during idle periods.
03
Model TCO Analysis
Comparing managed API costs (OpenAI) vs. self-hosted open-source models (Llama 3).
Technical Architecture
The ZOLIX Advantage
Token-level attribution and H100/A100 cluster rightsizing with Sovereign AI data privacy.
30% Throughput
Targeted Efficiency Gain
Implementation Roadmap
Zero-agent discovery & CUR ingestion
AI-driven anomaly detection baseline
Automated remediation policy rollout
Continuous governance & reporting