As of 2025, all three major cloud providers—Azure, AWS, and Google Cloud—have evolved significantly. They’ve introduced new instance types, created powerful analytics platforms, refined their container orchestration solutions, and offered more advanced AI features.
Everyone wants to use the cloud efficiently and cost-effectively, but the sheer number of options can be overwhelming. Should you use Amazon S3 or Azure Blob for storage? Is Redshift better for your analytics needs than BigQuery or Azure Synapse? Which provider has the easiest path for container deployments? Which has the best built-in security tools?
This cloud services comparison cheat sheet aims to answer questions like these. We’ll focus on each major area—storage, databases, compute, analytics, containers, networking, security, and ML—so you can pick the best solution for your projects.
Storage: From block to object
Storage services are critical because almost every application depends on fast, reliable, low-cost data storage. Modern cloud providers offer file, object, block, and other storage options.
Key criteria
- Performance and latency: Ability to quickly read and write data
- Integration: Works smoothly with other cloud offerings, such as triggers for serverless functions
- Pricing: Transparent pricing model for storage, retrieval, or data transfer
- Redundancy: Objects replicated across multiple regions or within a single data center
- Users can optimize costs by choosing the storage tier (hot, cool, archive) that best suits their needs.
- The service integrates with many Azure services like Data Lake, Synapse, and Functions.
- It also supports features like soft delete and point-in-time restore.
- S3 is often viewed as the standard for object storage.
- Its lifecycle management lets you move data to cheaper tiers like Glacier automatically.
- S3 triggers serverless functions—Lambda—when new objects arrive.
- GCS has straightforward classes (Standard, Nearline, Coldline, Archive).
- It also features global replication options with built-in versioning.
- Easy integration is available with services like BigQuery and Dataflow.
When comparing these solutions in 2024, all three are mature and offer robust object storage. However, if you’re in a fully Amazon Web Services Azure environment, you might pick S3 or Blob based on your primary provider. If you want simplicity and global reach, GCS often wins.
Database: RDBMS, NoSQL, and beyond
Databases are at the core of any application that stores structured or semi-structured data. The debate often revolves around choosing managed relational databases vs. NoSQL solutions vs. multi-model databases.
Key criteria
- Scalability: Ability to handle both read and write scale-out easily
- Data model flexibility: SQL, NoSQL, or a mix
- Migration tools: Solutions from the cloud provider to migrate from on-premises or another cloud
- Cost: Pricing model that fits your workload (e.g., pay-per-use or monthly instances)
- Azure SQL is fully managed and offers compatibility with on-premises SQL Server.
- A multi-model, globally distributed database, Cosmos DB supports APIs for MongoDB, Cassandra, or SQL. It’s designed for minimal latency so your app can read or write data fast—no matter where your users are located.
- RDS supports multiple engines, e.g., MySQL, MariaDB, Oracle, PostgreSQL, Aurora, and SQL Server.
- DynamoDB is a NoSQL key-value store known for its low-latency performance on a massive scale, which is ideal for high-traffic applications.
- Cloud SQL manages MySQL, PostgreSQL, and SQL Server instances with automated backups.
- Firestore is a serverless NoSQL solution offering strong consistency, quick integration with Firebase, and easy real-time data sync.
If you’re looking for highly available SQL, all providers have good solutions. For global NoSQL, both Cosmos DB and DynamoDB are popular. If you prefer an easier learning curve and are already using GCP, Firestore might be your top choice. Each provider has migration services, so an AWS to Azure cheat sheet or a Google Cloud cheat sheet can help you figure out how to port data between them.
Compute services let you run code—whether a small website, a machine learning batch job, or a large-scale HPC cluster. Each provider offers a variety of instance types (CPU, GPU, memory-optimized), as well as serverless options.
Key criteria
- Scalability: Auto-scaling, pay-per-invoke, or cluster orchestration
- Diverse compute models: Traditional VMs, containers, serverless, HPC, or specialized hardware like GPUs/TPUs
- Pricing Models: On-demand, reserved instances, or spot
- Integration: Compute layer easily connects to storage, networking, or analytics
- Azure VMs are straightforward for IaaS (infrastructure as a service), with Windows and Linux options.
- Azure Functions is event-driven serverless compute, great for quick triggers or background tasks.
- EC2 offers the widest selection of instance families, including ARM-based Graviton for cost-effective performance, GPU instances for accelerated computing like machine learning or 3D rendering, and storage-optimized instances for high-volume data processing.
- Lambda pioneered the serverless model with pay-per-invoke pricing, which is perfect for microservices and event-driven architectures.
- Compute Engine bills per second, offers custom machine types, and feature preemptible VMs for cheaper HPC or batch jobs.
- Cloud Run functions is a straightforward serverless option that integrates well with GCP services.
If you have unique hardware requirements like GPU or FPGA, you might lean toward AWS’s large selection or Google’s custom setups. Azure integrates closely with on-prem Microsoft environments, which can be a deciding factor for enterprise workloads.
Analytics: Data lakes and warehouses
Analytic services allow you to gain actionable insights from your raw data. Modern solutions combine big data storage with data warehousing, BI integrations, and ML capabilities.
Key criteria
- Query performance: Support for petabyte-scale queries with fast response times
- BI integration: Ability to connect easily to Looker, Tableau, Power BI, and other business intelligence tools.
- ML support: Ability to embed machine learning models or integrate with ML services
- Pricing: Pay-per-query vs. provisioned capacity vs. serverless pricing
- The service brings together SQL, Spark, and Data Lake functionalities.
- It also supports integration with Power BI and Azure Machine Learning.
- Redshift is a mature data warehouse with massively parallel processing.
- It includes features like Redshift Spectrum to query data directly in S3 with ML integration.
- Serverless data warehouse with pay-per-query pricing.
- Built-in machine learning BigQuery ML and straightforward integration with GCP storage services.
If you’re using the Microsoft ecosystem heavily, Synapse is appealing. Redshift remains a strong choice for AWS. BigQuery excels if you want a nearly zero-operations approach to analytics. When you see references to a 2024 cloud comparison cheat sheet, you’ll likely notice BigQuery’s serverless model frequently ranked highly for simplicity.
Containers and orchestration
Bundling applications into portable containers makes it possible to deploy software across environments. Meanwhile, Kubernetes and similar orchestration platforms simplify day-to-day operations—scaling, rolling updates, and ensuring high availability.
Key criteria
- Kubernetes integration: Managed control planes, automated upgrades, and autoscaling
- Multi-cloud support: Ability to run containers across multiple providers or on-premises
- CI/CD: Built-in pipelines or easy integration with external tooling
- Serverless options: Solutions like AWS Fargate or Google Cloud Run that run containers without managing nodes
- AKS simplifies identity with Azure Active Directory.
- It also features auto-upgrades, auto-scaling support, and strong integrations with GitHub Actions and Azure DevOps.
- EKS provides a managed Kubernetes control plane.
- ECS is a simpler, AWS-native container service.
- Both can work with Fargate for serverless containers.
- GKE is often praised for ease of use, auto-upgrades, and deep Kubernetes roots since Google created Kubernetes.
- It also works nicely with Google Cloud Logging and Monitoring.
If you want a pure Kubernetes experience with minimal friction, many developers prefer GKE. AKS is a good fit for companies already using Active Directory and Azure tooling. EKS/ECS is extremely flexible and has features like AWS Fargate that remove the need to manage servers.
Networking and content delivery
A global, reliable network can make or break user experience. You need content delivery networks (CDNs), load balancing, and advanced security to deliver data quickly to users around the world.
Key criteria
- Global footprint: Edge locations for caching and latency reduction
- Load balancing: Ability to handle L4/L7 load balancing
- Security: Built-in DDoS protection, WAF, SSL certificate management
- Pricing: Transparent egress fees to help avoid surprise bills
- Front Door uses intelligent routing to spread traffic across global endpoints, reducing latency and boosting application performance worldwide.
- Azure CDN integrates with other Azure services, including security options.
- CloudFront ties directly into S3, EC2, and Lambda@Edge, enabling on-the-fly content transformations and dynamic delivery for a seamless user experience.
- It also includes AWS Shield for DDoS protection and easy certificate management.
- Cloud CDN leverages Google’s private fiber network for low-latency delivery.
- It also features a minimal setup, especially if you’re using Cloud Load Balancing.
All providers offer strong global networks. If you already have an entire app on AWS, CloudFront is a natural choice. Azure Front Door and Azure CDN are tailored to Microsoft’s environment. Google Cloud CDN is known for ease of use and fast performance, especially for websites or APIs hosted on GCP.
Management and security
Managing dozens of services and ensuring security is crucial for all cloud users. You need identity and access management (IAM), governance tools, logging, and monitoring.
Key criteria
- IAM: Granular permissions, multi-factor authentication, role-based access
- Governance: Resource tagging, cost allocation, compliance checks
- Observability: Unified metrics, logs, tracing
- Built-in security: Encryption, key management, secrets handling
- Azure Entra ID simplifies identity management, single sign-on, and multi-factor authentication.
- Azure Monitor aggregates metrics and logs and supports custom dashboards and alerts.
- IAM is known for its fine-grained policy control and cross-account trust.
- CloudWatch tracks metrics, logs, and events across AWS services.
- Google’s IAM approach is straightforward, focusing on roles and minimal friction.
- Cloud Monitoring (formerly Stackdriver) is a unified service for logs, metrics, and traces.
You can manage large-scale deployments on any of these clouds, but the choice often depends on existing identity providers or the complexity of your environment. For companies heavily using Active Directory, Azure AD is usually a strong driver toward Azure.
Machine learning (ML) and artificial intelligence (AI)
ML and AI solutions are indispensable today. Each major cloud provider offers a fully managed ML platform, removing much of the complexity around model creation, training, and deployment.
Key criteria
- Model development: Support for notebooks, AutoML, or GPU/TPU
- MLOps: Tools for model versioning, CI/CD, and A/B testing of models
- Integration: Connection to your existing data lake or analytics platform
- Cost efficiency: Pay-as-you-go for training and inference
- Azure Machine Learning covers the entire ML lifecycle, from preparing your data to deploying it.
- OpenAI Services integrates directly with foundational models, unlocking generative AI use cases.
- Both services tie into Azure Synapse and Power BI, enabling straightforward data ingestion and powerful analytics.
- Amazon SageMaker handles data labeling, feature engineering, training, tuning, and deployment in a single environment.
- Bedrock provides out-of-the-box access to foundation models, making it well suited for generative AI.
- Both services integrate seamlessly with AWS data sources such as S3 and Redshift.
- Vertex AI unifies multiple AI products into one platform.
- It also offers AutoML, pipelines, and built-in solutions like Vertex AI Workbench.
- Plus, the platform connects easily with GCS and BigQuery for data.
If you want end-to-end solutions, Azure ML and SageMaker are robust. Google’s Vertex AI focuses on simplifying ML operations and adding AutoML features for less code. Any of these can handle large-scale ML, but the right pick depends on your ecosystem and existing data pipelines.
Cloud development optimization with Incredibuild
After selecting your preferred cloud services, the next big challenge is speeding up your development workflow. Large codebases often require long compilation times, and running automated tests can slow down your release cycle.
Incredibuild helps by distributing build and test tasks across multiple machines—on-premises or in the cloud. How?
- Parallel builds: Incredibuild splits tasks into smaller chunks and distributes them, reducing overall build time
- Integration with CI/CD: It also works with your existing pipeline tools like Jenkins, GitHub Actions, or Azure DevOps, so you don’t need a significant overhaul.
- Cloud-agnosticism: Whether you’re on Azure, AWS, or Google Cloud, Incredibuild can tap into available compute resources.
Shorter build and test cycles mean you can experiment more, fix issues faster, and deploy updates more frequently. In a fast-paced environment, that’s a critical advantage.
If you’re migrating from AWS to Azure or following a Google Cloud cheat sheet for a new project, the last thing you need is long build times. Incredibuild helps you move quickly, no matter which provider you choose.
Sign up for a free 30-day trial to learn how Incredibuild can accelerate your workflows today.
