Cloud Computing for Data Scientists – AWS, GCP and Azure Compared

The shift from on-premises computing to cloud infrastructure is complete for the vast majority of data science teams. Virtually every major company that builds ML systems today does so on AWS, GCP, or Azure — or a combination of the three. For data scientists, this means that cloud literacy is no longer optional. You need to understand how to spin up compute, access object storage, run distributed training jobs, deploy model endpoints, monitor resource usage, and control costs. The choice between AWS, GCP, and Azure affects not just tooling but career opportunities — companies hire with a preference for cloud-specific experience. This guide gives you a complete, honest comparison of the three major clouds for data science workloads, covering compute, storage, managed ML platforms, databases, and cost structures.

Core Infrastructure — Compute, Storage and Networking

All three clouds offer similar fundamental building blocks, but the naming conventions, default configurations, and pricing models differ enough to cause confusion. Understanding the equivalent services across clouds is essential for reading job descriptions and working with multi-cloud teams.

Compute: AWS EC2, GCP Compute Engine, and Azure Virtual Machines all offer GPU-accelerated instances for ML training. The most commonly used GPU instance types for training large models are the AWS p3 and p4 families (V100 and A100 GPUs), GCP a2 instances (A100), and Azure NC-series (V100/A100). For inference, smaller GPU instances or CPU instances with optimised kernels (AWS c7g with Graviton, GCP T2A with Ampere Altra) offer better cost efficiency. Spot/preemptible instances are essential for training workloads — most training jobs can be checkpointed and resumed on interruption, making the 60–90% cost saving easily justified.

Object Storage: AWS S3, GCP Cloud Storage, and Azure Blob Storage are functionally equivalent for data science purposes. All three offer multi-tier storage (standard, infrequent access, archive), strong consistency, and fine-grained IAM access control. The practical differences are pricing (GCP Cloud Storage egress is slightly cheaper for large volumes) and native integration with other services in the same cloud (SageMaker training jobs read S3 natively; Vertex AI reads GCS natively). Multi-cloud object storage strategies should account for egress charges, which at $0.08–0.09/GB can become significant at petabyte scale.

Managed ML Platforms — SageMaker vs Vertex AI vs Azure ML

Each cloud provider offers a managed end-to-end ML platform designed to handle the full lifecycle from data preparation to model deployment and monitoring. These platforms reduce operational overhead significantly — you do not need to manage the underlying Kubernetes cluster, set up experiment tracking infrastructure, or build your own model registry. The trade-off is that you become tightly coupled to the cloud provider’s abstractions and pricing.

AWS SageMaker is the most mature and most used managed ML platform. It covers the full lifecycle: data labelling (Ground Truth), feature store, experiment tracking, distributed training (with built-in support for PyTorch and TensorFlow), hyperparameter tuning, model registry, real-time and batch endpoints, and model monitoring. SageMaker Pipelines provides Airflow-like DAG orchestration for ML workflows. The main criticism is API complexity — SageMaker’s abstraction layer has a steep learning curve, and debugging jobs that fail in the managed environment is harder than debugging locally. Costs can also be significant if pipelines are not carefully monitored.

Google Vertex AI is Google’s unified ML platform, launched in 2021 by consolidating several previously separate products (AI Platform, AutoML, etc.). Its key strengths are BigQuery ML integration (run ML on data without moving it out of the warehouse), AutoML for structured data, and tight integration with Google’s pre-trained APIs (Vision, Natural Language, Translation). Vertex AI’s managed pipelines use Kubeflow Pipelines under the hood, making it easier to port workloads to self-managed Kubeflow. For teams already on GCP and using BigQuery, Vertex AI is the natural choice.

Azure Machine Learning is the strongest choice for organisations that are Microsoft-heavy (Azure AD, Office 365, Dynamics) and need tight enterprise governance integration. Azure ML supports MLflow natively for experiment tracking, has strong integration with Azure DevOps for CI/CD pipelines, and offers the most comprehensive role-based access control of the three platforms. Azure’s AI services (Azure OpenAI, Azure AI Search, Azure Cognitive Services) are particularly well-integrated for teams building applications on top of Microsoft’s AI infrastructure, including enterprise GPT-4 deployments.

Cost Management — The Skill No One Teaches

Cloud cost management is one of the most important and least-taught skills for data scientists. It is also one of the most career-relevant: engineering managers remember the data scientist who ran a $40,000 training job without checking the estimated cost, and they remember it for a long time. The following principles apply across all three clouds.

Right-sizing compute: The most common source of waste is over-provisioning — using a 16-GPU instance for a job that would complete adequately on 4 GPUs in 20% longer training time. Before submitting a large training job, profile it on a single small GPU instance to identify whether it is compute-bound, memory-bound, or I/O-bound. This determines which instance type optimises cost. A job that is I/O-bound (data loading is the bottleneck) benefits from faster storage, not more GPU compute.

Spot/preemptible instances: All training jobs should run on spot instances by default, with checkpointing enabled every 15–30 minutes. The interruption rate for spot instances is low (typically 5–15% depending on instance type and region), and the cost saving of 60–90% almost always outweighs the occasional need to restart from the last checkpoint. For SageMaker, managed spot training is a single parameter change. For GCP, preemptible VMs are selected with a checkbox. The payback is immediate.

Storage costs: Large datasets accumulate quietly in object storage. Implement lifecycle policies that automatically move data older than 90 days to infrequent-access tiers and archive data older than 365 days. Delete failed experiment artefacts. Use data versioning systems (DVC, Delta Lake) that store only incremental changes rather than full dataset copies for each experiment.

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The big data technologies that run on cloud infrastructure — Spark, Kafka, and data lakehouses — are covered in our Big Data Technologies guide. MLOps practices including CI/CD pipelines, model monitoring, and cloud deployment patterns are in our MLOps Interview Q&A. Deploying models as interactive web applications on cloud infrastructure is covered in our Streamlit Deployment guide. Feature engineering workflows that integrate with cloud feature stores are in our Feature Engineering guide.