How QA Strategies Differ Across AWS, Azure, and GCP

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When it comes to deploying applications on cloud platforms like AWS, Azure, or GCP, a Quality Assurance (QA) professional has to adapt their strategies to align with the platform-specific capabilities, tools, and challenges.

Let’s break down how testing strategies differ across these giants, with a conversational tone that feels like chatting with a fellow QA enthusiast over coffee.

The Cloud is the Same, But Not Really

While all three platforms provide the essential building blocks for scalable applications — compute, storage, and networking — they are not identical. Each has unique services, configurations, and quirks that can impact your QA process.

Let’s dive into these differences:

1. Understanding Service Integrations and APIs

AWS: Known for its extensive list of services, AWS is a playground of possibilities. But with so many options, ensuring compatibility and performance of integrations becomes a challenge. For instance, if you’re using AWS Lambda (serverless functions), you’ll need to focus on testing:

• Event triggers (S3 uploads, DynamoDB updates).
• Cold starts (a dreaded latency issue QA teams love to debug).