Search "cloud engineer" on LinkedIn right now. You'll find roughly four AWS job postings for every one that mentions Google Cloud Platform by name. That gap has been consistent for years, and it's the single most useful data point when deciding between AWS vs Google Cloud for your career.
That said, the choice isn't as simple as "pick AWS because it's bigger." Google Cloud has carved out real territory in data engineering, machine learning infrastructure, and certain enterprise verticals. The right answer depends on what kind of role you're targeting and what's actually available in your job market.
This article breaks down the practical differences between AWS and Google Cloud certifications, salary outcomes, and where each platform dominates—so you can make a decision based on your actual goals.
AWS vs Google Cloud: Where the Jobs Are
AWS currently holds approximately 31% of the global cloud infrastructure market, compared to Google Cloud's roughly 12% (Synergy Research, Q4 2024). That gap translates directly into job listings. When you filter Indeed or LinkedIn for roles requiring cloud certification, AWS-related positions consistently outnumber GCP positions by a wide margin across most geographies.
This matters for a few reasons:
- Entry-level options: AWS has more junior-level roles because more companies are running AWS workloads. Getting your first cloud job is genuinely easier on the AWS side of the market.
- Geographic variation: In some markets—particularly the San Francisco Bay Area and major tech hubs with heavy ML/AI focus—GCP roles are more competitive than national averages suggest.
- Enterprise vs. startup: Enterprise companies tend to run mixed environments, often with a legacy AWS footprint. Startups show more variation, but AWS still dominates in raw numbers.
Google Cloud is not a dead end. It's the preferred platform at companies running heavy BigQuery workloads, using Vertex AI for model deployment, or deeply embedded in Google's own ecosystem. If you're targeting data engineering or ML ops specifically, GCP certifications are well-regarded and sometimes preferred over AWS credentials.
AWS vs Google Cloud Certification Paths Compared
Both platforms have tiered certification programs, but they're structured differently and test different things.
AWS Certification Track
AWS has the most developed certification ecosystem in the industry:
- AWS Certified Cloud Practitioner (CLF-C02): Entry-level, no hands-on prerequisite. Useful for non-technical roles or as a starting point before the associate exams.
- AWS Certified Solutions Architect – Associate (SAA-C03): The most recognized AWS cert. Widely listed as a requirement or preference in job postings. This is the benchmark certification for cloud roles.
- AWS Certified Developer / SysOps Administrator – Associate: More specialized tracks for developers and operations engineers respectively.
- Professional and Specialty tiers: Solutions Architect Professional, DevOps Engineer Professional, and specialty certs in Advanced Networking, Security, and Machine Learning.
The SAA-C03 is the certification most worth prioritizing if you're entering the AWS job market. It appears in more job listings than any other AWS cert and tests a useful combination of architectural judgment and service breadth.
Google Cloud Certification Track
- Cloud Digital Leader: Entry-level, business-focused credential. Analogous to AWS Cloud Practitioner.
- Associate Cloud Engineer: Hands-on operational cert. Tests your ability to actually deploy and manage GCP workloads.
- Professional Cloud Architect: GCP's flagship certification, similar in positioning to AWS Solutions Architect Professional.
- Professional Data Engineer: One of GCP's most respected certs, particularly valued in data engineering roles. BigQuery expertise is central to this exam.
- Other Professional certs: Cloud Developer, Cloud DevOps Engineer, Machine Learning Engineer, and Network Engineer.
Google's professional certs have a reputation for being harder than AWS associate-level equivalents. The Professional Data Engineer exam requires genuine depth in BigQuery, Dataflow, and Pub/Sub—not just familiarity with what the services do. Passing it on the first attempt typically requires hands-on lab work, not just video courses.
Salary Outcomes by Platform
Salary data for cloud certifications is noisy because job titles vary widely and certifications rarely appear alone on a resume. That said, some patterns hold up across multiple sources:
- AWS Solutions Architect Associate: Median base salary in the $110,000–$140,000 range in the U.S., with significant variation by geography and company size.
- AWS Solutions Architect Professional: Typically $140,000–$180,000 base.
- GCP Professional Cloud Architect: Similar to AWS SAP, roughly $140,000–$175,000.
- GCP Professional Data Engineer: Often commands a premium at data-heavy organizations, with ranges of $135,000–$170,000 at companies where GCP is the primary data platform.
The honest summary: AWS certs translate into more job offers at a wider range of companies. GCP certs can pay equally well or better at the right companies, but those opportunities are narrower. If you're early in your career and optimizing for optionality, AWS has the edge.
Which Platform Fits Your Target Role
Rather than declaring a winner, match the platform to what you're actually trying to do:
- Cloud / DevOps Engineer: AWS. The tooling ecosystem is more mature, and job volume is higher. Start with SAA-C03.
- Data Engineer: GCP is worth serious consideration. BigQuery is genuinely excellent, and the Professional Data Engineer cert is well-recognized. That said, many data engineering roles use AWS (Redshift, Glue, EMR) or multi-cloud stacks, so check job listings before committing.
- ML Engineer / AI: GCP has strong tooling (Vertex AI, TPUs), but AWS SageMaker has significant enterprise adoption. Learn whichever platform your target companies actually run.
- Security / Networking: AWS has more advanced specialty certifications and more job listings in these areas by a large margin.
- Solutions Architect / Cloud Consultant: AWS SAA-C03 is the recognized benchmark. Having both platform credentials is valuable at consulting firms, but AWS comes first.
Top Courses for AWS and Google Cloud Certification
AWS Certified Solutions Architect Associate (SAA-C03)
The most practical starting point for the AWS certification track. Covers the full SAA-C03 blueprint with hands-on labs across core services—EC2, S3, VPC, RDS, IAM—with enough architectural depth to pass on your first attempt rather than just memorizing service names.
Google Cloud IAM and Networking for AWS Professionals
If you already have AWS knowledge and want to add GCP credentials, this Coursera course bridges the gap efficiently by mapping AWS concepts to their GCP equivalents rather than starting from scratch—particularly useful for professionals working in multi-cloud environments.
AWS Certified AI Practitioner Practice Exams (AIF-C01)
As AI and ML workloads move to the cloud, the AI Practitioner cert is becoming a meaningful differentiator on a resume. This practice exam set is rated among the most accurate for the AIF-C01 format, which rewards understanding AWS AI services over surface-level definitions.
AWS SAA-C03 Practice: 850+ Questions on Networking
Networking is the section most candidates underperform on in the SAA-C03. This focused practice set covers VPC design, routing, Transit Gateway, and hybrid connectivity at the depth the exam actually tests—not just high-level conceptual overviews.
AWS Certified Advanced Networking Specialty (ANS-C01)
For engineers targeting network architecture or security roles, this specialty cert commands a meaningful salary premium over the associate-level credentials. The course addresses the full ANS-C01 blueprint including Direct Connect, VPN architectures, and network automation.
Master PySpark for Data Engineering (AWS, Azure, GCP, Snowflake)
Platform-agnostic data engineering skills that apply regardless of which cloud you land on. Covers PySpark on AWS EMR, Google Dataproc, and Azure HDInsight—worth considering if you're targeting data engineering roles where multi-cloud exposure appears in job descriptions.
FAQ
Is AWS harder to certify in than Google Cloud?
At the associate level, they're comparable in difficulty, though the exams test different things. The AWS SAA-C03 tests breadth of service knowledge and architectural judgment. Google's Professional Cloud Architect is generally considered harder than AWS associate-level certs, but it sits at a higher tier. Google's professional exams have a reputation for requiring genuine hands-on experience to pass—watching videos alone rarely gets you there.
Which cloud platform pays more—AWS or Google Cloud?
At comparable seniority levels, base salaries are similar. AWS roles have higher volume, which creates more negotiating opportunities but also more competition. GCP roles in specialized areas like data engineering can pay a premium at companies where it's the primary platform. The bigger salary driver is role type and company size, not which cloud certification is on your resume.
Can I get AWS or Google Cloud certification training for free?
The exams themselves are not free—AWS exams run $100–$300 depending on tier, and Google Cloud exams are $200. However, both platforms offer free training materials: AWS Skill Builder has a free tier with hundreds of hours of content, and Google Cloud Skills Boost offers free labs and learning paths. Third-party Udemy and Coursera courses frequently run sales that bring prices down significantly from list price.
Should I get AWS certified before Google Cloud?
For most people, yes. AWS has more job listings at every level, so AWS certification opens more doors earlier in your career. Once you have an AWS foundation and a job, adding a GCP credential is relatively straightforward since many architectural concepts transfer. The exception is if your target employer or specific role explicitly requires GCP experience.
How long does it take to prepare for the AWS Solutions Architect Associate exam?
Preparation time varies widely based on your starting point. Someone with hands-on Linux and networking experience typically needs 6–10 weeks of focused study. Someone coming from a non-technical background should plan for 3–5 months of consistent effort. The exam is 65 questions in 130 minutes and penalizes guessing less than some other certification exams.
Is Google Cloud better than AWS for machine learning roles?
GCP has genuine strengths in ML infrastructure—TPUs, Vertex AI, and tight integration with TensorFlow and Google's research stack. However, AWS SageMaker is widely adopted in enterprise ML deployments, and most ML engineering job listings don't exclusively require GCP. For ML roles, the platform often matters less than your Python, modeling, and MLOps skills. Learn whichever one your target companies actually use.
Bottom Line
The AWS vs Google Cloud decision has a clear answer for most people entering cloud careers: start with AWS. It has more jobs, a more recognized certification track at the entry and mid levels, and lower barriers to getting that first role. The AWS Certified Solutions Architect Associate (SAA-C03) is the most recognized entry point in the market and will make sense of most cloud engineering job descriptions you encounter.
Google Cloud is worth learning if you're targeting data engineering, ML infrastructure, or roles at companies with heavy GCP investments. The Professional Data Engineer cert has earned genuine respect in the data community and is worth pursuing once you have a foundation. Adding GCP as a second platform—after establishing yourself on AWS—is a reasonable move if multi-cloud exposure appears frequently in your target job listings.
Before committing to either path, spend 20 minutes searching your target job title and city on LinkedIn and Indeed. The distribution of AWS vs GCP in those results will tell you more than any general recommendation can.