GCP vs AWS: Choosing the Right Cloud Provider for ML Projects in 2025

Introduction

As organizations increasingly leverage machine learning to drive innovation and competitive advantage, the choice of cloud platform has become a critical strategic decision. In 2025, Google Cloud Platform (GCP) and Amazon Web Services (AWS) continue to dominate the cloud computing landscape, each offering comprehensive suites of ML services and infrastructure options.

The migration to cloud-based ML solutions offers numerous benefits: improved scalability, reduced operational overhead, access to specialized hardware, and the ability to leverage managed services that simplify the ML lifecycle. However, choosing between GCP and AWS for your ML initiatives requires careful consideration of various factors including performance, cost, available services, and alignment with your existing technology stack.

This comprehensive guide examines both platforms through the lens of machine learning requirements, providing you with the insights needed to make an informed decision that aligns with your organization's specific ML objectives, technical requirements, and business constraints.

Market Position & Growth

Before diving into technical comparisons, it's valuable to understand the market position and growth trajectories of both cloud providers, as this can influence long-term platform stability, innovation pace, and investment in ML capabilities.

AWS: The Established Leader

AWS continues to maintain its position as the market leader in cloud computing, with an estimated annual revenue run rate exceeding $90 billion in 2025. Since pioneering the cloud computing market in 2006, AWS has built a comprehensive ecosystem of services that spans the entire ML workflow, from data preparation to model deployment and monitoring.

While AWS's growth rate has moderated to around 25-30% annually (down from 40-50% in previous years), its sheer scale and established enterprise presence make it a formidable player in the ML cloud space. The platform's maturity is reflected in its extensive documentation, large community, and robust partner ecosystem.

GCP: The Fast-Growing Challenger

Google Cloud Platform has experienced remarkable growth, with an annual revenue run rate approaching $40 billion in 2025. GCP's growth rate consistently outpaces AWS, often exceeding 40% year-over-year, as it continues to gain market share and expand its enterprise footprint.

GCP's strength in ML is deeply rooted in Google's DNA as an AI-first company. The platform leverages Google's extensive experience in developing and deploying ML at scale, offering services that often incorporate cutting-edge research from Google's AI teams. This heritage gives GCP a unique advantage in certain ML workloads, particularly those involving natural language processing, computer vision, and large-scale data analytics.

Machine Learning Services Comparison

Both AWS and GCP offer comprehensive suites of machine learning services, ranging from fully managed platforms to specialized tools for specific ML tasks. Let's examine the key ML services offered by each provider.

AWS Machine Learning Services

AWS provides a rich ecosystem of ML services designed to support the entire machine learning workflow:

• Amazon SageMaker: A fully managed service for building, training, and deploying machine learning models at scale. SageMaker includes tools for data labeling, feature engineering, model training, hyperparameter optimization, and deployment.
• Amazon Bedrock: A fully managed service that provides access to foundation models from leading AI companies through a unified API, with strong data privacy guarantees.
• AWS AI Services: Pre-trained AI services for common ML tasks, including Amazon Rekognition (computer vision), Amazon Comprehend (natural language processing), Amazon Transcribe (speech-to-text), Amazon Polly (text-to-speech), and Amazon Lex (conversational interfaces).
• Amazon EMR: A managed big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Hive, HBase, Flink, and Presto.
• Amazon Personalize: A machine learning service that makes it easy to create individualized recommendations for customers.
• Amazon Forecast: A fully managed service that uses machine learning to deliver highly accurate forecasts.
• Amazon Fraud Detector: A fully managed service that uses machine learning to identify potentially fraudulent activities.

GCP Machine Learning Services

Google Cloud Platform offers a comprehensive suite of ML services that leverage Google's expertise in AI:

• Vertex AI: A unified platform for building, deploying, and scaling ML models, combining AutoML and AI Platform into a single environment with a focus on MLOps.
• Gemini API: Access to Google's most advanced multimodal AI models through a simple API, with capabilities spanning text, code, images, and more.
• Google Cloud AI APIs: Pre-trained models for common ML tasks, including Vision AI (image analysis), Natural Language AI, Speech-to-Text, Text-to-Speech, Translation, and Document AI.
• BigQuery ML: Enables users to create and execute machine learning models in BigQuery using standard SQL queries, eliminating the need to move data.
• Dataproc: A managed Spark and Hadoop service that lets you take advantage of open-source data tools for batch processing, querying, streaming, and machine learning.
• Recommendations AI: Delivers highly personalized product recommendations at scale.
• Contact Center AI: A solution that provides conversational AI capabilities for contact centers.

While both platforms offer comprehensive ML services, there are notable differences in their approaches:

Compute Resources for ML

Machine learning workloads, particularly deep learning, often require specialized hardware to achieve acceptable performance. Both AWS and GCP offer a range of compute options optimized for ML workloads.

AWS Compute Options for ML

AWS offers several compute instance types optimized for ML workloads:

• P4 Instances: Powered by NVIDIA A100 Tensor Core GPUs, offering up to 8x NVIDIA A100 GPUs, 320 GB of GPU memory, and 400 Gbps networking.
• P5 Instances: The latest generation featuring NVIDIA H100 Tensor Core GPUs, delivering up to 8x NVIDIA H100 GPUs with 640 GB of GPU memory.
• G5 Instances: Powered by NVIDIA A10G GPUs, offering a more cost-effective option for less demanding ML workloads.
• Inf2 Instances: Featuring AWS Inferentia2 chips, custom-built by AWS for high-performance, cost-effective ML inference.
• Trn1 Instances: Powered by AWS Trainium chips, designed specifically for training deep learning models.

GCP Compute Options for ML

GCP provides a variety of compute options for ML workloads:

• A3 Instances: Featuring NVIDIA H100 Tensor Core GPUs, with configurations offering up to 8x NVIDIA H100 GPUs.
• A2 Instances: Powered by NVIDIA A100 Tensor Core GPUs, with configurations offering up to 16x NVIDIA A100 GPUs in a single instance.
• G2 Instances: Featuring NVIDIA L4 GPUs, designed for graphics-intensive and ML inference workloads.
• Cloud TPU v5e and v5p: Google's custom-designed Tensor Processing Units, optimized for TensorFlow and JAX workloads, offering exceptional performance-per-dollar for compatible models.

When comparing compute resources for ML, several key differences emerge:

• Custom Silicon: Both providers offer custom-designed chips for ML workloads. AWS provides Inferentia for inference and Trainium for training, while Google offers TPUs that excel at both training and inference for compatible frameworks.
• GPU Availability: Both providers offer the latest NVIDIA GPUs, but GCP's A2 instances can scale to 16 NVIDIA A100 GPUs in a single instance, compared to AWS's maximum of 8 GPUs per instance.
• Interconnect: GCP's instances benefit from Google's custom network interconnect, which can provide advantages for distributed training across multiple nodes.
• Framework Optimization: GCP's TPUs are highly optimized for TensorFlow and JAX, potentially offering significant performance advantages for these frameworks, while AWS's custom silicon works with a broader range of frameworks.

Storage Options for ML Datasets

Efficient storage and access to large datasets are critical for ML workloads. Both AWS and GCP offer a range of storage options optimized for different ML scenarios.

AWS Storage for ML

• Amazon S3: Object storage service that offers industry-leading scalability, data availability, security, and performance. Commonly used for storing training datasets, model artifacts, and other ML assets.
• Amazon EFS: Fully managed elastic file system that can be mounted by multiple instances simultaneously, making it suitable for shared datasets in distributed training scenarios.
• Amazon FSx for Lustre: High-performance file system optimized for fast processing of workloads such as ML training. It can be linked to S3 buckets, allowing you to access and process datasets without having to copy them.
• Amazon EBS: Block storage volumes that can be attached to EC2 instances, providing low-latency access to data for ML workloads.

GCP Storage for ML

• Cloud Storage: Object storage for companies of all sizes, offering global edge-caching for fast access to datasets from anywhere.
• Filestore: Fully managed file storage service that provides high-performance storage for ML workloads that require a file system interface.
• Persistent Disk: Block storage for VM instances, offering both standard (HDD) and SSD options with the ability to resize on the fly.
• BigQuery Storage: Specialized storage optimized for analytics and ML workloads, with tight integration to BigQuery ML for in-database machine learning.

Key differences in storage options include:

• Performance Tiers: AWS offers more granular performance tiers for S3 (Standard, Intelligent-Tiering, Standard-IA, One Zone-IA, Glacier, Glacier Deep Archive), while GCP's Cloud Storage offers Standard, Nearline, Coldline, and Archive.
• High-Performance File Systems: AWS's FSx for Lustre is specifically designed for high-performance computing workloads like ML, offering advantages for certain training scenarios.
• Analytics Integration: GCP's BigQuery Storage offers unique advantages for ML workloads that leverage BigQuery ML, allowing models to be trained directly on data stored in BigQuery without data movement.
• Global Availability: GCP's storage snapshots are available globally by default, while AWS EBS snapshots are regional and must be explicitly copied to other regions if needed.

Networking Capabilities

Networking performance is particularly important for distributed ML training, where multiple nodes need to communicate efficiently. Both AWS and GCP have invested heavily in their networking infrastructure to support demanding ML workloads.

AWS Networking for ML

• Elastic Fabric Adapter (EFA): A network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale, critical for distributed ML training.
• AWS Enhanced Networking: Provides higher bandwidth, higher packet per second (PPS) performance, and consistently lower inter-instance latencies.
• AWS Global Accelerator: A networking service that improves the availability and performance of applications with global users.
• Amazon CloudFront: A fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency.

GCP Networking for ML

• Google Cloud's Premium Tier Network: Delivers traffic via Google's globally deployed, high-performance network, resulting in lower latency and higher throughput.
• Tier_1 Networking for A2/A3 Instances: Provides 100+ Gbps networking between instances, optimized for distributed training workloads.
• Cloud CDN: Google's content delivery network, which accelerates content delivery for websites and applications.
• Network Service Tiers: Allows you to optimize for either performance (Premium Tier) or cost (Standard Tier) based on your specific requirements.

Key networking differences include:

• Global Network: GCP's Premium Tier leverages Google's extensive global network infrastructure, potentially offering advantages for globally distributed ML workloads.
• Specialized ML Networking: Both providers offer specialized networking for ML workloads, with AWS's EFA and GCP's Tier_1 networking for GPU instances.
• Network Tiering: GCP uniquely offers Network Service Tiers, allowing you to optimize for either performance or cost based on your specific requirements.
• Inter-Region Connectivity: GCP's global network can provide advantages for ML workloads that span multiple regions, with potentially lower latency and higher throughput between regions.

ML Framework Support

Both AWS and GCP support all major machine learning frameworks, but there are differences in the level of optimization and integration for specific frameworks.

AWS ML Framework Support

• TensorFlow: Fully supported with optimized containers and integration with SageMaker.
• PyTorch: Comprehensive support with optimized containers and deep integration with SageMaker.
• MXNet: Strong support as AWS contributed significantly to this framework.
• Scikit-learn: Well-supported with optimized containers and integration with SageMaker.
• XGBoost: Excellent support with native integration in SageMaker.
• Hugging Face Transformers: Deep integration with SageMaker, including optimized containers and deployment options.

GCP ML Framework Support

• TensorFlow: Exceptional support as Google developed TensorFlow, with deep optimization for Google's TPUs.
• JAX: Outstanding support as Google developed JAX, with native optimization for TPUs.
• PyTorch: Strong support with optimized containers and integration with Vertex AI.
• Scikit-learn: Well-supported with integration with Vertex AI.
• XGBoost: Good support with integration in Vertex AI and BigQuery ML.
• Hugging Face Transformers: Strong integration with Vertex AI and optimization for TPUs for compatible models.

Key differences in framework support include:

• TensorFlow Optimization: GCP offers superior optimization for TensorFlow, particularly when using TPUs, which can provide significant performance advantages for TensorFlow workloads.
• JAX Support: GCP provides exceptional support for JAX, a framework developed by Google that's gaining popularity for research and advanced ML applications.
• PyTorch Support: Both platforms offer strong PyTorch support, but AWS has historically had a slight edge in PyTorch optimization and tooling.
• Framework Integration: AWS SageMaker offers a more unified experience across frameworks, while GCP's support varies more by framework, with TensorFlow and JAX receiving the most optimization attention.

MLOps & Deployment

MLOps—the practice of applying DevOps principles to machine learning workflows—has become increasingly important as organizations move ML models from experimentation to production. Both AWS and GCP offer comprehensive MLOps capabilities, but with different approaches.

AWS MLOps Capabilities

• SageMaker Pipelines: A purpose-built CI/CD service for ML workflows, allowing you to automate and manage steps of your ML workflow.
• SageMaker Model Registry: A centralized repository for model versions and metadata.
• SageMaker Projects: Templates for setting up MLOps environments with CI/CD pipelines.
• SageMaker Model Monitor: Automatically monitors models in production for data and model quality issues.
• SageMaker Feature Store: A repository for storing, sharing, and managing features for ML models.
• AWS Step Functions: A serverless orchestration service that can be used to coordinate ML workflows.

GCP MLOps Capabilities

• Vertex AI Pipelines: A serverless orchestration tool for automating ML workflows, based on Kubeflow Pipelines.
• Vertex AI Model Registry: A centralized repository for managing ML models and their metadata.
• Vertex AI Experiments: A service for tracking and comparing ML experiments.
• Vertex AI Model Monitoring: Automatically monitors models in production for data drift and model quality issues.
• Vertex AI Feature Store: A managed service for storing, serving, and sharing ML features.
• Cloud Build: A CI/CD platform that can be integrated with Vertex AI for ML workflows.

Key differences in MLOps capabilities include:

• Integration: GCP's Vertex AI provides a more unified experience, with all MLOps capabilities integrated into a single platform. AWS's approach is more modular, with separate services that can be combined as needed.
• Kubernetes Integration: GCP's Vertex AI Pipelines is based on Kubeflow Pipelines, providing better integration with Kubernetes-based workflows. AWS's SageMaker Pipelines is a proprietary solution that doesn't require Kubernetes knowledge.
• Deployment Options: AWS offers more deployment options for ML models, including SageMaker Endpoints, SageMaker Serverless Inference, and SageMaker Batch Transform. GCP's deployment options are more streamlined but may offer less flexibility for certain use cases.
• Monitoring Capabilities: Both platforms offer robust monitoring capabilities, but AWS's SageMaker Model Monitor provides more out-of-the-box monitoring types, including data quality, model quality, bias drift, and feature attribution drift.

Pricing Structures

Pricing is a critical consideration when choosing a cloud provider for ML workloads, as these can be compute-intensive and potentially costly. Both AWS and GCP offer various pricing models, but there are important differences to consider.

AWS Pricing for ML Workloads

• Pay-as-you-go: Standard pricing model where you pay for what you use, with no upfront commitments.
• Savings Plans: Commitment-based pricing that offers savings of up to 72% compared to on-demand pricing, with 1 or 3-year terms.
• Reserved Instances: Commitment-based pricing for specific instance types, offering up to 75% discount compared to on-demand pricing.
• Spot Instances: Unused EC2 capacity available at up to 90% discount compared to on-demand pricing, but can be interrupted with 2 minutes' notice.
• Free Tier: Limited free usage for certain services, including some SageMaker capabilities.

GCP Pricing for ML Workloads

• Pay-as-you-go: Standard pricing model with per-second billing for most services.
• Committed Use Discounts: Commitment-based pricing that offers savings of up to 70% for 1 or 3-year terms.
• Sustained Use Discounts: Automatic discounts for running instances for a significant portion of the billing month, up to 30% discount.
• Preemptible VMs: Low-cost, short-duration VMs that can be interrupted by GCP, offering up to 80% discount compared to on-demand pricing.
• Free Tier: Limited free usage for certain services, including some Vertex AI capabilities.

Key pricing differences include:

• Automatic Discounts: GCP's Sustained Use Discounts are applied automatically without requiring upfront commitments, while AWS requires explicit commitments for its Savings Plans and Reserved Instances.
• Billing Granularity: Both providers offer per-second billing for most services, but GCP has historically been more aggressive in implementing per-second billing across its services.
• Specialized Hardware: GCP's TPUs can offer better price-performance for compatible workloads compared to GPU-based solutions, potentially resulting in significant cost savings for certain ML workloads.
• Network Pricing: GCP's network pricing is generally more favorable, especially for data transfer between regions and for egress to the internet.

Security & Compliance

Security and compliance are paramount considerations for ML workloads, particularly those involving sensitive data. Both AWS and GCP offer robust security capabilities, but with different approaches and strengths.

AWS Security for ML

• Identity and Access Management (IAM): Fine-grained access control for AWS resources.
• VPC: Network isolation for your resources.
• KMS: Key management service for encryption.
• CloudTrail: Logging of API calls for auditing.
• SageMaker Security Features: Private VPC connectivity, encryption at rest and in transit, IAM integration, and more.
• Compliance Certifications: Extensive list including HIPAA, PCI DSS, SOC 1/2/3, ISO 27001, and more.

GCP Security for ML

• Identity and Access Management (IAM): Fine-grained access control for GCP resources.
• VPC: Network isolation for your resources.
• Cloud KMS: Key management service for encryption.
• Cloud Audit Logs: Logging of API calls for auditing.
• Vertex AI Security Features: Private VPC connectivity, encryption at rest and in transit, IAM integration, and more.
• Compliance Certifications: Extensive list including HIPAA, PCI DSS, SOC 1/2/3, ISO 27001, and more.

Key security differences include:

• Data Governance: GCP's Data Catalog and Data Lineage services provide more comprehensive data governance capabilities, which can be particularly valuable for ML workloads that involve sensitive data.
• Confidential Computing: GCP's Confidential Computing offers hardware-based isolation for sensitive workloads, providing an additional layer of security for ML models and data.
• Security Posture Management: AWS's Security Hub provides a more comprehensive view of security posture across AWS accounts and services, while GCP's Security Command Center offers similar capabilities but with a different approach.
• ML-Specific Security: Both platforms offer similar security features for their ML services, but AWS's SageMaker offers more granular controls for model deployment and monitoring.

Support & Documentation

Effective support and comprehensive documentation are essential for successfully implementing and maintaining ML workloads in the cloud. Both AWS and GCP offer various support options and extensive documentation.

AWS Support & Documentation

• Support Plans: Basic (free), Developer ($29/month), Business ($100/month), and Enterprise (starting at $15,000/month).
• Documentation: Comprehensive documentation for all services, including tutorials, best practices, and reference materials.
• Community: Active community forums, AWS re:Post (formerly AWS Forums), and extensive third-party resources.
• Training: AWS Training and Certification programs, including specific tracks for ML and AI.
• Partner Network: Extensive network of AWS partners who can provide additional support and expertise.

GCP Support & Documentation

• Support Plans: Basic (free), Standard ($29/month), Enhanced ($250/month), and Premium (custom pricing).
• Documentation: Comprehensive documentation for all services, including tutorials, best practices, and reference materials.
• Community: Active community forums, Google Cloud Community, and extensive third-party resources.
• Training: Google Cloud Training and Certification programs, including specific tracks for ML and AI.
• Partner Network: Growing network of Google Cloud partners who can provide additional support and expertise.

Key support and documentation differences include:

• Support Pricing: AWS's support pricing is generally higher, especially for enterprise-level support, but may offer more comprehensive coverage.
• Documentation Quality: Both providers offer excellent documentation, but GCP's documentation is often praised for its clarity and accessibility, particularly for ML services.
• Community Size: AWS has a larger community due to its longer history and larger market share, potentially offering more community-based support options.
• ML-Specific Resources: GCP offers more ML-specific resources and documentation, reflecting Google's strong focus on AI and ML.

Decision Framework

Choosing between AWS and GCP for ML workloads requires careful consideration of various factors. Here's a decision framework to help guide your choice based on specific requirements and priorities.

Choose AWS for ML if:

• You're already heavily invested in the AWS ecosystem
• You need a wide range of specialized instance types for different ML workloads
• You're using PyTorch as your primary ML framework
• You require extensive MLOps capabilities with fine-grained control
• You need to deploy ML models across a wide range of environments, including edge devices
• You're working with a large team that benefits from AWS's extensive documentation and community resources
• You require specific compliance certifications that AWS may offer

Choose GCP for ML if:

• You're using TensorFlow or JAX as your primary ML framework
• You can benefit from TPUs for your specific ML workloads
• You need tight integration with BigQuery for data analytics and ML
• You value a more unified ML platform experience with Vertex AI
• You have globally distributed ML workloads that can benefit from GCP's network
• You prefer automatic discounts without long-term commitments
• You're working with cutting-edge ML research that aligns with Google's AI focus

For many organizations, a multi-cloud approach may be the optimal solution, leveraging the strengths of both platforms for different aspects of their ML workflows. For example:

• Using GCP's BigQuery and Vertex AI for data processing and initial model development
• Using AWS SageMaker for production deployment and monitoring
• Leveraging GCP's TPUs for TensorFlow training and AWS's GPU instances for PyTorch inference

Conclusion

Both AWS and GCP offer robust, comprehensive platforms for machine learning workloads, each with distinct strengths and approaches. AWS provides a mature, extensive ecosystem with a wide range of options and fine-grained control, while GCP offers a more integrated experience with unique advantages for certain ML frameworks and workloads.

The optimal choice depends on your specific requirements, existing investments, technical preferences, and business constraints. For many organizations, the decision isn't about choosing one platform exclusively, but rather about strategically leveraging the strengths of each platform for different aspects of their ML workflows.

As the ML landscape continues to evolve rapidly, both AWS and GCP are investing heavily in their ML capabilities, introducing new services and features at a rapid pace. Staying informed about these developments and maintaining flexibility in your cloud strategy will be key to maximizing the value of cloud-based ML for your organization.

Ultimately, the most successful ML initiatives focus first on clearly defining the business problem and data strategy, then selecting the cloud platform and services that best support those objectives. By taking a thoughtful, strategic approach to cloud provider selection, you can position your ML projects for success in 2025 and beyond.