Governance and FinOps for AI models (tokenomics & LLM)
Sovereign deployment of Mistral models on Kubernetes with tight GPU cost steering. Full budget control during experimentation through a tokenomics approach.
01 Challenges and context
Context and strategic stakes
The group aimed to accelerate its innovation capabilities by integrating Mistral generative AI models (LLMs) for its developers. However, native GCP managed services (Vertex AI) prioritized proprietary models (Gemini). The client therefore opted for a sovereign deployment of Mistral models on its own Google Kubernetes Engine (GKE) clusters. The objective of this 3-month Proof of Concept (POC) was twofold: to demonstrate the technical feasibility of autonomous inference and to prove the economic viability of GPU consumption, while preventing any budget overrun (Tokenomics).
02 Work carried out
Approach and methodology
Before any deployment, we developed three exhaustive AI hosting business cases to objectively compare the unit inference costs (GPU Compute, context storage, licenses) between a GCP scenario (Vertex AI + GKE), an AWS scenario (EKS + SageMaker), and an On-Premise scenario. This modeling included strict criteria for sovereignty and legal responsibility, and took into account the risk of capacity shortages for GPU hardware. We collaborated closely with the Procurement teams and Mistral's technical teams to validate budgetary assumptions.
03 Outcomes achieved
Results and indicators
37 %
GPU cost reduction
0
Budget overrun
3 months
POC duration
FinOps Performance: 37 % reduction in GPU compute costs achieved through optimization methods on GKE.
Absolute Control: 0 budget overrun authorized or observed during the 3-month POC experimentation.
Data Governance: 100 % of AI workloads are now tagged, traced, and financially monitored in real time.