AWS vs Google Cloud (GCP)
Amazon Web Services and Google Cloud Platform are two of the three dominant hyperscalers competing for enterprise cloud workloads. AWS leads in breadth of services and market share, while GCP leads in AI/ML capabilities, data analytics, and Kubernetes. The right choice often depends on existing technical stack and use cases.
Build your own internal tools freeSide-by-side
The world's most comprehensive cloud platform vs Build what's next with Google Cloud.
| Feature | AWS | Google Cloud (GCP) |
|---|---|---|
| Pricing from | Pay-as-you-go | Pay-as-you-go |
| Pricing | Pay-as-you-go; significant discounts with Reserved/Savings Plans | Pay-as-you-go; Committed Use Discounts up to 57% |
| Best for | General-purpose workloads and largest service catalog | AI/ML, data analytics, and Kubernetes |
| AI/ML services | SageMaker, Bedrock, Rekognition | Vertex AI, BigQuery ML, TPU access |
| Managed Kubernetes | EKS (Elastic Kubernetes Service) | GKE (Google Kubernetes Engine) – Kubernetes creator |
| Global regions | 33 regions, 105 availability zones | 40+ regions worldwide |
| Data analytics | Redshift, Glue, Athena | BigQuery (serverless, auto-scaling) |
AWS or Google Cloud (GCP)? Who each tool is best for
AWS
The world's most comprehensive cloud platform
- Pricing: Pay-as-you-go; significant discounts with Reserved/Savings Plans
- Best for: General-purpose workloads and largest service catalog
- AI/ML services: SageMaker, Bedrock, Rekognition
- Managed Kubernetes: EKS (Elastic Kubernetes Service)
Starting from Pay-as-you-go
Google Cloud (GCP)
Build what's next with Google Cloud
- Pricing: Pay-as-you-go; Committed Use Discounts up to 57%
- Best for: AI/ML, data analytics, and Kubernetes
- AI/ML services: Vertex AI, BigQuery ML, TPU access
- Managed Kubernetes: GKE (Google Kubernetes Engine) – Kubernetes creator
Starting from Pay-as-you-go
How Appaca works
Appaca is not another SaaS tool to evaluate. It builds you a working app from a plain description — with database, dashboards, and team access — and runs it on the platform.
Describe what you need
Tell Appaca what you need in plain language. No forms, no setup wizard — just describe the job to be done.
Chat with AI to refine it
Appaca AI builds your app and stays available to refine it. Change behaviour, add fields, adjust flows — all in chat.
Use it immediately
Your app runs on Appaca with a built-in database, file storage, and team access. No deployment, no devops.
Everything your team needs, built in
Appaca provides the full stack for internal and personal software — no integrations to wire up, no hosting to manage.
Build and update apps by chatting with AI
Describe what you need and Appaca builds a working app. Come back any time to refine it — add new fields, change behaviour, or extend functionality — all without writing code.
Built-in database and file storage
Every Appaca app comes with a secure database and file storage ready to use. No external service to connect, no schema to design — Appaca handles the data layer automatically.
Connect to services your team already uses
Appaca apps can connect to Google Sheets, Slack, Airtable, and any service that supports an API or webhook — so your app fits into your existing workflow instead of replacing it.
Building software for how your team actually works?
While you're comparing AWS and Google Cloud (GCP), you might have other tools your team actually builds and maintains — trackers, dashboards, internal workflows. Appaca builds those from a plain description, with a database and team access included. No code, no devops.
- Describe what you need, get a working app in minutes
- Built-in database, dashboards, and team access
- Iterate with chat — no engineer needed
- Free to start, no per-seat pricing
Common questions
GCP often wins on compute pricing and offers sustained use discounts automatically. AWS and Azure require manual configuration of Savings Plans or Reserved Instances to achieve equivalent savings. Actual cost depends heavily on workload mix.
GCP has an edge in ML infrastructure thanks to its TPUs, Vertex AI managed pipelines, and BigQuery ML integration. AWS SageMaker is more mature as an end-to-end ML platform for enterprise teams.
Yes, multi-cloud is common for enterprises. Terraform or Pulumi manage both from a single configuration. Data transfer costs between clouds are a consideration in multi-cloud architectures.
Appaca is a platform for personal software. You describe what you need and Appaca builds a working app with a database, dashboards, and team access — no code or deployment required. It is not a replacement for the tools compared on this page. Try it free at appaca.ai.