In-Depth Comparison: AWS SageMaker, Azure ML, and GCP Vertex AI in 2024

Price Comparison

For starters we dive into a detailed cost comparison of AWS SageMaker, Azure ML, and GCP Vertex AI. In order to have a more realistic comparison, we came up with the following 3 example use cases:

• Use Case 1 - Model Training: - Training a machine learning model using a standard dataset on a GPU-enabled instance for 10 hours.
• Use Case 2 - Model Deployment for Prediction: - Deploying a trained model for real-time predictions on a standard instance with moderate traffic, running 24/7 for a month.
• Use Case 3 - Batch Processing: - Running a batch job for inference on a large dataset using a standard CPU instance for 5 hours.

Price Comparison Table

Assumptions and Notes:

• AWS SageMaker:
  • Use Case 1: Instance cost for p3.2xlarge ($3.06 per hour).
  • Use Case 2: Instance cost for ml.m5.large ($0.1345 per hour).
  • Use Case 3: Same as Use Case 2, pro-rated for 5 hours.
• Azure ML:
  • Use Case 1: NC6 GPU instance ($0.90 per hour).
  • Use Case 2: Standard_DS3_v2 instance ($0.145 per hour).
  • Use Case 3: Same as Use Case 2, pro-rated for 5 hours.
• GCP Vertex AI:
  • Use Case 1: N1 Standard-8 + Tesla T4 GPU ($4.82 per hour).
  • Use Case 2: E2-standard-4 instance ($0.1851 per hour in US regions).
  • Use Case 3: N1 Standard-4 instance ($0.21 per hour, pro-rated for 5 hours).

Note: Actual costs may vary due to specific configurations, regions, and pricing updates. These estimates exclude potential additional costs like networking or storage. For precise calculations, refer to each cloud provider’s pricing calculator.

Feature Comparison Table

Moving onto feature comparison, to give a quick overview, we’ve put all the data we have into a single table. Below the table you’ll find more details regarding each supported features.

Speech and Text Processing Capabilities

Common Features Across Platforms

All three platforms offer robust capabilities in speech recognition, text-to-speech, entities extraction, key phrase extraction, language recognition, topic extraction, intention analysis, and sentiment analysis. These features are crucial for applications like virtual assistants, customer service automation, and sentiment tracking in social media.

Distinguishing Features

• Azure ML excels with additional functionalities like spell check, autocompletion, relations analysis, syntax analysis, and tagging parts of speech (POS). It supports 120+ languages for language recognition and 60+ languages for translation.
• AWS SageMaker offers voice verification and low-quality audio handling but lacks in spell check and POS tagging.
• GCP Vertex AI shines with its handling of low-quality audio and translation support for 100+ languages. However, it doesn’t offer spell check or voice verification.

Image Analysis APIs

Shared Features

All three platforms proficiently handle object detection, sense detection, face detection, inappropriate content detection, and text recognition.

Unique Capabilities

• Azure ML and GCP Vertex AI stand out in landmark detection, with Azure additionally offering dominant colors detection.
• GCP Vertex AI uniquely offers logo detection and search for similar images on the web.
• AWS SageMaker has an edge in food recognition, a feature not available in GCP Vertex AI.

Video Analysis APIs

Core Functionalities

Object detection, scene detection, inappropriate content detection, and facial recognition are common across all platforms.

Exclusive Features

• Azure ML provides comprehensive features including activity detection, facial and sentiment analysis, audio transcription, speaker indexing, keyframe extraction, and annotation.
• AWS SageMaker offers person tracking on videos and real-time analytics.
• GCP Vertex AI, while strong in audio transcription, lacks in facial and sentiment analysis, person tracking, and real-time analytics.

Use Cases and Platform Suitability

• Azure ML is user-friendly, offering a flexible building interface for advanced analytics. It’s well-suited for real-time AI applications, sentiment analysis, fraud detection, and customer churn prediction. Its range of algorithms and no-code designs make it ideal for quick scaling of applications.
• AWS SageMaker is ideal for teams with more engineers than analysts, SageMaker offers an all-in-one platform for developing, training, deploying, and serving models. It’s particularly suitable for complex conversational AI and diverse machine learning tasks including business analysis, forecasting, and outlier detection.
• GCP Vertex AI is best for ML training and deployment, particularly in NLP, GCP Vertex AI is favored for its intuitive UI and robust SaaS offerings. It’s highly recommended for organizations already using Google Cloud services, offering seamless integration and strong support for various ML tasks.

User Experience and Community Feedback

• Azure ML is praised by users for its user-friendly interface and efficient hyperparameter tuning. Its integration with VSCode and the ability to run training scripts locally are highly valued. Azure ML is appreciated for its seamless workflow development and reproducibility features.
• AWS SageMaker is noted for its continuous upgrades and introduction of new features. However, some users find its script adaptation for hyperparameter tuning complex. Its all-in-one platform appeal is a significant draw for many users.
• GCP Vertex AI is lauded for its training and deployment capabilities, especially in ML. However, some users find its documentation lacking, which can be a hurdle for those new to Google Cloud services.

Conclusion

In summary, AWS SageMaker, Azure Machine Learning, and GCP Vertex AI each offer distinct strengths. Azure ML stands out for its ease of use and flexibility, making it ideal for teams focused on analytics and advanced ML applications. AWS SageMaker is best suited for engineering-heavy teams and diverse ML tasks, offering a comprehensive platform for the entire machine learning lifecycle. GCP Vertex AI excels in training and deployment, especially for organizations already embedded in the Google Cloud ecosystem.

Choosing the right platform depends on the specific needs of the organization, including the nature of the ML projects, team composition, and existing cloud infrastructure. By carefully evaluating each platform’s features and aligning them with organizational goals and capabilities, businesses can leverage the right cloud-based ML solutions to drive innovation and efficiency.