When should I use Azure OpenAI vs Azure Machine LearninG

Hi there

not sure, about yoiur usecase but Use Azure OpenAI if you just need to analyze or generate text — it’s quick, powerful, and doesn’t require training. Use Azure Machine Learning if you need full control, custom models, or structured data processing. Many projects combine both: Azure ML for data prep or prediction, and Azure OpenAI for natural language tasks like summarization or insights.

Use Azure OpenAI/Azure AI Foundry when your project involves natural language understanding, reasoning, or generation tasks that can be handled by pretrained foundation models (like GPT-5 or embedding models) without requiring custom model training. This is particularly suitable for:

• Text analysis and summarization (e.g., extracting insights from unstructured text)
• Conversational interfaces (chatbots, assistants)
• Content generation (reports, documentation, emails, etc.)
• Semantic search or document retrieval using embeddings
• Code generation or explanation

You get state-of-the-art performance out of the box and can fine-tune or use prompt engineering for customization. The tradeoff is less control over the model internals but much faster development.

Use Azure Machine Learning when you need to train, customize, or operationalize your own models — including classical ML, deep learning, or even fine-tuned language models that aren’t available in Azure OpenAI. This is best suited for:

• Custom domain data requiring model training (e.g., sentiment detection tailored to financial, medical, or industrial text)
• Predictive analytics or structured data tasks (regression, classification, forecasting)
• Model lifecycle management — training, tuning, versioning, deployment, monitoring
• Integration of non-LLM AI (computer vision, tabular models, reinforcement learning)

In short, Azure ML gives you control and flexibility for end-to-end machine learning workflows.

Combining them is often the best approach for enterprise-grade AI solutions. A common hybrid pattern looks like this:

1. Azure ML handles data preprocessing and model operations:
   • Clean and structure text data
   • Detect and remove personally identifiable information (PII)
   • Classify or cluster documents
   • Generate embeddings for semantic analysis
   • Manage datasets and pipelines
2. Azure OpenAI/Azure AI Foundry handles advanced language reasoning and generation:
   • Summarize insights from the processed data
   • Generate explanations or reports
   • Conduct natural language queries on ML outputs

For example, you could use Azure ML to train a classifier that tags customer feedback by sentiment and topic, then use Azure OpenAI or other generative AI models to generate executive summaries or recommend strategic actions based on those tags.

In general, you might want to consider the following approach:

• Start with Azure OpenAI/Azure AI Foundry if your text analysis needs align with what large language models already do well.
• Move to or add Azure ML if your domain has unique vocabulary, regulatory constraints, or performance needs that require custom model training.
• Use Azure OpenAI/Azure AI Foundry to orchestrate workflows that connect OpenAI models, ML models, and external data pipelines seamlessly.

If the above response helps answer your question, remember to "Accept Answer" so that others in the community facing similar issues can easily find the solution. Your contribution is highly appreciated.

hth

Marcin