“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Enterprise AI Model Selection & Evaluation
The LLM landscape is fragmented, with GPT-4o, Claude, Gemini, Mistral, Phi, and Azure-hosted options each carrying different strengths, costs, compliance postures, and performance characteristics. This workshop gives teams a structured evaluation process based on benchmarking, not vendor claims.
VNode ITeSIntermediate
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration2 days
FormatVirtual or On-site
LevelIntermediate
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Enterprise AI Model Selection & Evaluation as an MCT-led VNode ITeS program for Technical architects, AI leads, and engineering managers evaluating LLMs for enterprise deployment. Typical duration is 2 days (Virtual or On-site). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Technical architects, AI leads, and engineering managers evaluating LLMs for enterprise deployment
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
An enterprise evaluation framework for choosing LLMs, benchmarking GPT-4o, Claude, Gemini, Mistral, Phi-3, and Azure-hosted models against real business requirements.
Prerequisites
- Familiarity with LLMs or prior exposure to Azure OpenAI
- Understanding of enterprise requirements such as compliance, cost, and latency
Program Outcomes
Capabilities your teams will gain
- Apply a structured model evaluation framework tailored to a specific enterprise use case
- Run hands-on benchmarks across at least two major LLM providers
- Evaluate cost, latency, accuracy, and compliance trade-offs with evidence
- Produce a documented decision matrix ready to present to business stakeholders
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
- LLM landscape and provider comparison
- Structured evaluation frameworks and benchmarking
- Cost, latency, and compliance trade-offs
- Enterprise decision matrix and documentation
1Module 1
LLM Landscape and Provider Comparison
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Module 1
LLM Landscape and Provider Comparison
Map GPT-4o, Claude, Gemini, Mistral, Phi-3, and Azure-hosted model options against enterprise deployment requirements including data residency and compliance.
- LLM landscape and provider comparison
2Module 2
Evaluation Frameworks and Benchmarking
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Module 2
Evaluation Frameworks and Benchmarking
Run structured benchmarks across LLM providers using defined task scenarios, quality scoring criteria, and reproducible evaluation methodology.
- Run structured benchmarks across LLM providers using defined task scenarios
- quality scoring criteria
- and reproducible evaluation methodology
3Module 3
Cost, Latency, and Compliance Trade-offs
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Module 3
Cost, Latency, and Compliance Trade-offs
Evaluate cost per token, response latency, data residency requirements, and regulatory compliance posture for each model option.
- Evaluate cost per token
- response latency
- data residency requirements
- and regulatory compliance posture for each model option
4Module 4
Enterprise Decision Matrix
+
Module 4
Enterprise Decision Matrix
Produce a documented model selection decision matrix with evidence-based business rationale ready for stakeholder review and sign-off.
- Enterprise decision matrix and documentation
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Intermediate practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Prevents costly model selection mistakes by giving teams a structured, evidence-based framework to evaluate and choose the right LLM before committing budget to production.
- Focus evaluation on team's specific use case and data type
- Include Azure AI Foundry deployment path and cost optimization
- Add compliance focus for regulated industries (banking, healthcare, government)
Resources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
