Machine Learning Model - Technology / Platform choice

Question

Machine Learning Model - Technology / Platform choice

Kaushik Dutta 165

Hello Team,

We are building Custom Machine Learning Model and train those models. This Model should predict some business forecasting results and data is exposed via APIs. The Models will be trained based on the OLTP historical data.

What will be my decision tree to choose the technology stack between Azure Databricks vs. Azure ML Studio?

The answer should be given from Cost, performance, scalability, data volume, resiliency, operational prospective.

Regards,

Kaushik

SRILAKSHMI C 14,655 Reputation points Microsoft External Staff Moderator

2026-01-29T07:31:53.8766667+00:00

Hi Kaushik Dutta,

Did you get any chance to review the above response. Do let me know if you have any further queries.

Thank you!
Kaushik Dutta 165 Reputation points

2026-01-29T14:31:26.1566667+00:00

Hello, in our use case, the dataset size is around 100GB, we need to build bespoked Models and surfaced with APIs so that we can get the required business output when that API is invoked.

The Model will be trained based on our OLTP database. Although the actual database is more than 750GB, we are looking for a subset of its data, which can grow upto 100GB.

We will be using on-premises system and a well-defined Azure Integration layer to talk to the Model API. The system load is unpredictable and trigged based on business user's input.

Expecting lots of parallel request to the Model.

I need to understad, how performant the Azure ML Studio would be, also the Cost, operations and maintainability, scalability, CI-CD features, and any limitations in both the Technologies.

If you can refine your recommendation based on the above points, it would be great.

Regards,

Kaushik

Answer accepted by question author

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SRILAKSHMI C 14,655 Reputation points Microsoft External Staff Moderator

2026-01-29T07:31:53.8766667+00:00

Hi Kaushik Dutta,

Did you get any chance to review the above response. Do let me know if you have any further queries.

Thank you!
Kaushik Dutta 165 Reputation points

2026-01-29T14:31:26.1566667+00:00

Hello, in our use case, the dataset size is around 100GB, we need to build bespoked Models and surfaced with APIs so that we can get the required business output when that API is invoked.

The Model will be trained based on our OLTP database. Although the actual database is more than 750GB, we are looking for a subset of its data, which can grow upto 100GB.

We will be using on-premises system and a well-defined Azure Integration layer to talk to the Model API. The system load is unpredictable and trigged based on business user's input.

Expecting lots of parallel request to the Model.

I need to understad, how performant the Azure ML Studio would be, also the Cost, operations and maintainability, scalability, CI-CD features, and any limitations in both the Technologies.

If you can refine your recommendation based on the above points, it would be great.

Regards,

Kaushik

Answer 1

Hello Kaushik Dutta,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that you are building Machine Learning Model and in need of Technology / Platform choice.

Regarding your scenario, explanations and putting your data at rest into consideration:

As a solution architect, my advice on best practice is to combine both platforms; if heavy ETL is required but rely on Azure ML Studio as the primary platform for model training, lifecycle management, and API deployment. If OLTP data requires Spark-scale ETL > Use Databricks for data prep. Also, if training/deployment/APIs are your core requirement > Use Azure ML Studio.

This is the only solution aligned with:

Microsoft AI Decision Framework - https://microsoft.github.io/Microsoft-AI-Decision-Framework/docs/decision-framework.html
Azure Architecture Center AI/ML guidance - https://learn.microsoft.com/en-us/azure/architecture/ai-ml/

In summary use the table below:

Summary
Requirement	Best Tool	Reason
Heavy OLTP ETL	Databricks	Spark-scale performance
Model training	Azure ML	Pipelines, AutoML, MLOps features
Deployment via API	Azure ML	Managed endpoints
Governance/resiliency	Azure ML	Built-in monitoring & drift detection
Cost optimization	Azure ML (auto-shutdown)	More predictable compute lifecycle

I hope this is helpful! Do not hesitate to let me know if you have any other questions or clarifications.

Please don't forget to close up the thread here by upvoting and accept it as an answer if it is helpful.

Answer 2

Hello Kaushik Dutta,

Welcome to Microsoft Q&A and Thank you for reaching out.

Choosing Between Azure Databricks and Azure ML Studio for Custom ML Models

When building custom machine learning models for business forecasting, trained on OLTP historical data and exposed via APIs, both Azure Databricks and Azure ML Studio play important but different roles. The right choice depends on where the complexity lies in your ML lifecycle.

1. Cost

Azure Databricks

Pricing is based on VM compute + Databricks Units (DBUs).

Very cost-effective for large-scale distributed data processing.

Can become expensive if clusters are left running or used for small workloads.

Azure ML Studio

Pay-as-you-go pricing based on training and inference compute usage.

More cost-efficient for model training, experimentation, and API hosting.

Supports auto-shutdown and managed endpoints, reducing idle costs.

Databricks is more economical for big data processing, while Azure ML is more cost-efficient for model training and serving.

2. Performance

Azure Databricks

Excellent performance for large datasets using Apache Spark.

Ideal for heavy feature engineering, aggregations, and distributed ML.

Azure ML Studio

Optimized for ML experimentation and training workflows.

Performance is strong for small to medium datasets and production inference.

Not designed to replace Spark for massive data transformations.

Use Databricks for data-heavy workloads, Azure ML for model-centric workloads.

3. Scalability

Azure Databricks

Horizontally scalable by design.

Handles TB–PB scale data easily.

Azure ML Studio

Scales well for training jobs and inference endpoints.

Designed for production ML workloads, not raw data lakes.

Databricks scales best for data, Azure ML scales best for models and APIs.

4. Data Volume

Azure Databricks

Best suited for very large OLTP historical datasets.

Ideal for joins, windowing, time-series feature engineering, and transformations.

Azure ML Studio

Works best once data is curated and feature-ready.

Can handle large datasets but requires more careful tuning.

Large, raw, historical data → Databricks

Cleaned training datasets → Azure ML

5. Resiliency

Azure Databricks

Built-in fault tolerance via Spark (task retries, checkpointing).

Strong for long-running data pipelines.

Azure ML Studio

Job retries, pipeline recovery, and endpoint resiliency.

Better suited for production ML lifecycle reliability.

Databricks is resilient for data processing, Azure ML for model operations.

6. Operational & MLOps Perspective

Azure Databricks

Strong collaborative environment for data engineers and scientists.

Basic MLflow-based experiment tracking and model registry.

Not optimized for secure, scalable API hosting.

Azure ML Studio

Purpose-built for end-to-end MLOps:

Experiment tracking

  Model versioning & registry
  
     CI/CD integration
     
        Managed real-time & batch endpoints
        
           Monitoring and retraining

If your models are exposed via APIs and used by business systems, Azure ML Studio is the better operational platform.

Choose Azure Databricks if:

You are processing very large OLTP datasets
Feature engineering is the most complex part
Distributed data processing is your main challenge

Choose Azure ML Studio if:

Your dataset is manageable
You need fast model development, deployment, and monitoring
API exposure and governance matter

Please refer this

I Hope this helps. Do let me know if you have any further queries.

Thank you!

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