¿What Is Microsoft Azure? Azure OpenAI vs Azure ML Studio Comparison

Introduction:

Case Study: Azure OpenAI vs Azure Machine Learning Studio – Differences, Real Cases, and Invisible Costs

“Many organizations have Azure credits or plans enabled with AI services, but they don't activate a single one. Worse yet: they confuse OpenAI with Machine Learning Studio and waste their potential.”

Context

A technology company with an Azure Enterprise Agreement (EA) subscription wanted to develop artificial intelligence-based solutions for legal text analysis and risk prediction. The technical team did not know whether to use Azure OpenAI Service or Azure Machine Learning Studio, so they tried to use both... doubling consumption and getting no clear results for two months.

Furthermore, the finance team noticed an unexpected increase in costs due to model inferences without monitoring or configured limits.

Implemented Real-World Use Cases

Azure OpenAI Service

• Legal document classifier with 4 lines of code and a fine-tuned prompt.
• Summarization of lengthy contracts in seconds using GPT-4.
• Automatic generation of responses for claims with integrated moderation.

Azure Machine Learning Studio

• Prediction of customer delinquency using historical data in CSV format.
• Customer classification by risk using decision trees.
• Anomaly detection in utility consumption.

💸 Invisible Costs Detected

• Usage of OpenAI endpoints without token control (long prompt + high temperature).

• Unnecessary ML Studio training runs left active for days.

• Non-utilization of reserved instances or proper sizing (dimensioning).

• Lack of awareness regarding free credits available through licensing or CSP programs.

✅ Results (After Corrections)

• 65% reduction in monthly costs by defining scaling and monitoring rules.

• AI security and governance implemented under Azure AD policies and sensitivity labels.

• Acceleration of Proofs of Concept (PoCs) to 1 week using OpenAI (without training models from scratch).

• Increased understanding by stakeholders of the differences between generative vs. predictive AI.

💡 Lessons Learned: 

• OpenAI and ML Studio do not compete: they complement each other depending on the problem you want to solve.

• Without cost control for tokens or CPU, AI can become unsustainable.

• Measuring, labeling, and automating AI governance is part of technical and financial success.

• Taking advantage of Azure credits requires activation, not just subscription.