Generative AI certifications compared: AWS, Google, Azure

The three credentials, described fairly

The AWS Certified AI Practitioner (AIF-C01) is the broadest of the three. It covers AI and machine-learning fundamentals, generative AI, responsible AI, security and governance, and a substantial layer of AWS’s own AI services — how the platform’s managed offerings map to problem types. It suits people who want general AI/ML literacy anchored to the most widely used cloud.

Google Cloud’s Generative AI Leader is the most explicitly business-oriented. Its domains cover generative-AI fundamentals, Google Cloud’s own generative-AI offerings (the largest section), techniques for improving model output such as prompting and grounding, and business strategies for a successful solution, including secure and responsible AI. It reads as a credential for decision-makers first. Microsoft’s Azure AI Fundamentals changed shape in 2026: its current exam, AI-901, weights AI concepts — responsible AI, how models work, and common workloads such as text, speech, vision and information extraction — at 40–45%, and implementing solutions in Microsoft Foundry at 55–60%: deploying models, building a simple chat client and agent, and small text, speech, vision and extraction apps. That makes it the most hands-on of the three. All three are foundational with no formal prerequisite, and our individual guides to the AWS AI Practitioner and the Google Generative AI Leader cover each in depth.

Side by side

The essential differences compress into a short list:

• AWS Certified AI Practitioner (AIF-C01) — vendor: AWS; focus: broad AI/ML plus generative AI, responsible AI and AWS AI services; audience: anyone wanting general AI fluency on the AWS platform; level: foundational.
• Google Cloud Generative AI Leader — vendor: Google Cloud; focus: generative-AI strategy, output-improvement techniques and business adoption; audience: leaders, decision-makers and business professionals; level: foundational.
• Microsoft Azure AI Fundamentals (AI-901) — vendor: Microsoft; focus: AI concepts plus building small generative-AI, agent, speech, vision and extraction apps in Microsoft Foundry; audience: people starting out in AI solution development who are comfortable with basic Python; level: foundational.

How to choose

The first filter is the cloud you use or target. These are vendor credentials, and a meaningful share of each syllabus is the vendor’s own services — so a certification aligned with your organisation’s platform, or the platform named in the job adverts you care about, is worth more in practice than a marginally “better” one on a cloud you never touch. If your employer runs on Azure, AI-901 converts directly into workplace vocabulary; the same logic applies to AWS and Google Cloud.

The second filter is framing. If your role is genuinely strategic — approving initiatives, scoping products, advising organisations — Google’s Generative AI Leader matches that job most directly. If you want the widest conceptual grounding across AI and machine learning without writing code, the AWS exam covers the most territory beyond generative AI. If you want to start building — and are comfortable with basic Python — Microsoft’s AI-901 is the only one of the three that has you put working AI applications together. And if you are cloud-agnostic, it is reasonable to choose by employer demand in your market: read live job adverts rather than certification marketing.

The honest shared limit

None of these certifications makes you an AI or machine-learning engineer, and all three vendors are upfront about that. They are exam-based evidence of fluency: that you understand what these systems are, how they fail, how to use them responsibly and what the vendor’s services do. The path to building AI systems runs through programming, mathematics and real projects — territory our machine learning engineer career path maps in detail — and no foundational exam substitutes for it.

That limit is not a reason to skip them; it is a reason to hold them to their honest job. Paired with visible hands-on work — even small projects — a foundational AI credential opens conversations and clears screens. Presented as engineering capability, it invites the interview question that deflates it. Our guide on whether AI certifications are worth it makes the full case, including why we publish no salary or demand statistics: no honest ones exist at the precision the marketing implies, and your own evidence is the live job market.

Exam mechanics: three vendors, three rulebooks

A practical trap when comparing across vendors is assuming they all run exams the same way — they do not. AWS scores the AI Practitioner on a scaled 100–1000 range with a pass mark of 700, delivers it through Pearson VUE, and the certification is valid for three years. Microsoft also uses Pearson VUE with a pass mark of 700 out of 1000; notably, Microsoft’s Fundamentals certifications, Azure AI Fundamentals included, do not expire. Google delivers its exams through Pearson VUE too — it moved there in early 2026 — but publishes no numeric passing score at all: results are simply pass or fail.

Fees vary by certification and can change, so check each vendor’s official certification page for current pricing rather than relying on third-party figures. The mechanics matter less than they appear — every one of these is a proctored exam a well-prepared candidate passes — but knowing the rulebook you are actually playing under removes a layer of avoidable anxiety.