Pick AWS when you want the broadest catalog of services, the deepest partner and hiring ecosystem, and the most proven path for almost any workload; it remains the largest cloud with 28% of the market in Q2 2026. Pick Google Cloud when your center of gravity is analytics (BigQuery), Kubernetes (GKE), or Google's AI stack (Gemini and the Gemini Enterprise Agent Platform, formerly Vertex AI), or when your organization already runs on Google Workspace and identity. Both are enterprise-grade; follow where your data, skills and contracts already are.
Per Synergy Research Group, worldwide enterprise spending on cloud infrastructure services passed $143 billion in Q2 2026, with Amazon at 28%, Microsoft at 20% and Google at 15%. Synergy notes that Microsoft and Google continue to grow substantially faster than Amazon, which still holds a strong lead. Among developers, the 2025 Stack Overflow Developer Survey showed 43.3% of respondents using AWS and 24.6% using Google Cloud. For the Microsoft comparison, see AWS vs Azure.
Both offer VMs, managed Kubernetes, serverless containers and functions. AWS has more instance families, including its own Graviton Arm processors and Trainium and Inferentia accelerators for ML. Google offers its own Axion Arm processors and TPUs, which matter if you train or serve large models on Google's accelerators.
For Kubernetes, GKE is often considered the most polished managed offering: Autopilot runs the nodes for you, upgrades are smooth and Google's long Kubernetes history shows. EKS benefits from sitting next to the rest of AWS, which is where most companies already run. Both charge a cluster management fee: $0.10 per cluster per hour on EKS (standard support) and on GKE, which includes a $74.40 monthly free-tier credit per billing account. For lighter needs, Cloud Run on Google Cloud and ECS with Fargate or App Runner on AWS run containers without Kubernetes at all.
Data is where Google Cloud is most differentiated. BigQuery is serverless, scales without cluster management and integrates with Looker, Dataflow, Pub/Sub and Google's AI services. Many companies choose Google Cloud because of BigQuery alone. AWS's analytics stack (Redshift, Athena, EMR, Glue, Kinesis, Lake Formation) is broad and capable but requires more assembly and more choices. Many AWS shops run Snowflake or Databricks on AWS instead.
For operational databases, both offer managed Postgres and MySQL. AWS has Aurora and DynamoDB, both widely used at very large scale. Google has Cloud SQL, AlloyDB (a Postgres-compatible engine) and Spanner for globally distributed relational data with strong consistency.
On AWS, Amazon Bedrock gives API access to models from Anthropic, OpenAI, Meta, Mistral AI, Amazon and others, with guardrails, knowledge bases and agent tooling, while SageMaker covers custom training and hosting. On Google Cloud, Vertex AI's services became part of the Gemini Enterprise Agent Platform in April 2026; the Vertex AI documentation now points there. It offers Gemini models, partner models including Claude, agent tooling and TPU-backed training. If Gemini is your primary model family, Google Cloud is the natural home. If you want many model providers behind one API in an AWS account, Bedrock is. For a related platform comparison, see AWS Bedrock vs Azure OpenAI.
Both clouds meet the major compliance frameworks and offer customer-managed encryption keys, private connectivity, audit logging and organization-wide policy controls. The design philosophies differ. AWS organizes everything around accounts: production, staging and each team often get separate accounts under AWS Organizations, with service control policies setting guardrails. Google Cloud organizes around a resource hierarchy of organization, folders and projects, with IAM policies and organization policies inherited down the tree.
In practice, AWS's model gives very strong blast-radius isolation but needs careful tooling (Control Tower, IAM Identity Center) to keep many accounts manageable. Google's hierarchy is simpler to reason about and integrates naturally with Google Workspace identities, but projects are a softer boundary than AWS accounts. Whichever you pick, build the landing zone, logging and least-privilege roles before the first production workload, not after.
List prices for comparable VMs and storage are close enough that architecture and commitments decide your bill. The models differ:
AWS skills are the most common cloud skills on the market, which makes AWS easier to staff at every level, from DevOps engineers to solution architects. Google Cloud talent is scarcer, though GKE and BigQuery experience is well represented among data and platform engineers. Most cloud concepts transfer, so a strong AWS engineer can become effective on Google Cloud within weeks; the gaps are usually IAM and resource hierarchy, networking and service-specific operational knowledge. Our AWS interview questions help you test depth, and our AWS developer rates page shows pay bands.
Ryz Labs cloud engineers work on your team on US business hours, in your accounts and repos, with same-day code review. Only the top 1% of the tens of thousands of engineers we interview make it. You can hire AWS developers or cloud engineers for either platform, or bring in a team through our cloud migration and AWS development services. Engineers work in your accounts, and you control and can revoke access at any time.
Typical cost is $7,000 to $15,000 per engineer per month by seniority; leads are quoted per team. If you need a board-level cloud strategy program rather than engineers who build, a large consultancy is the better fit.
Neither is consistently cheaper. List prices are close for comparable resources; your bill depends on architecture, commitments (AWS Savings Plans or Google committed use discounts), data egress and enterprise agreements. Google's sustained use discounts apply automatically, which can help steady workloads without commitments.
AWS. Synergy Research Group put AWS at 28%, Microsoft at 20% and Google at 15% of worldwide cloud infrastructure services in Q2 2026, with Microsoft and Google growing faster.
Google Cloud is the natural choice if you want Gemini models, TPUs or BigQuery-centered data for AI. AWS is strong if you want many model providers, including Anthropic and OpenAI, through Bedrock in your AWS account. Both host Claude.
In April 2026, Google folded Vertex AI's services into the Gemini Enterprise Agent Platform. Existing capabilities continue under the new name, and Google's Vertex AI documentation now points to the Agent Platform documentation.
GKE is often considered more polished, especially with Autopilot, and Google created Kubernetes. EKS is the natural choice for companies already running on AWS. Both charge $0.10 per cluster per hour, and both are production-proven.
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