AI Neural Network Background

Gen AI Strategy Simulator

Created by CK Cheruvettolil

Visualize the critical tradeoffs between Cost and Sovereignty when architecting your Generative AI solution.

tune Configuration

Fine-Tune
Fine-Tune Existing Weights
Build from Scratch Pre-training

Adapting an existing open-weights model to your data. Lower cost, faster time-to-market.

Public Cloud
Public Cloud Opex Model
On-Premises Capex Model

Leveraging hyperscaler infrastructure. Flexible scaling, no hardware maintenance, but potential data residency concerns.

Impact Analysis

Total Investment & Complexity Low (Opex Focus)

Pay-as-you-go tokens/compute. Low barrier to entry.

Sovereignty & Control Low (Trust-based)

Relies on CSP security and base model license.

LOW
COST & COMPLEXITY
HIGH
LOW
SOVEREIGNTY
HIGH
Secure Enterprise
Sovereign Fortress
Agile Adopter
Cloud Native Giant
The Agile Adopter
Scenario Visualization

The Agile Adopter

Fastest ROI Flexible

You prioritize speed and flexibility. By fine-tuning existing models on public cloud infrastructure, you minimize upfront CapEx. However, your data resides on shared infrastructure, and your model's IP is partially dependent on the base model.

trending_up Pros

  • Minimal upfront cost
  • Elastic scalability
  • Fastest time-to-market

warning Cons

  • Data residency concerns
  • Recurring Opex accumulation
  • Vendor lock-in

Verdict: Ideal for B2B SaaS, internal productivity tools, and rapid POCs.