DevOps
GitHub Actions vs GitLab CI vs CircleCI: How We Choose for Clients
The CI/CD market has effectively consolidated. Here is what actually differentiates the three main options and which context suits each one.
Three tools that claim to solve the same problem. Here is what the differences actually mean in practice, and when each one makes sense.
Infrastructure as code is no longer optional. The real question in 2025 is which tool. Terraform, Pulumi, and AWS CDK are the three options you will encounter in almost every serious evaluation. Each has won for different reasons. Understanding what separates them makes the choice much easier than reading feature comparison tables.
Terraform has been the industry standard long enough that the ecosystem is mature. The provider library covers every major cloud and dozens of SaaS services. The declarative HCL syntax is learnable in a day. Most importantly, if you hire a DevOps engineer today, they almost certainly know it. That fluency advantage compounds over time. You can swap a Terraform practitioner for another without re-training anyone.
HCL is not a programming language. You cannot loop with conditionals in a readable way. Testing is awkward. Refactoring a large Terraform codebase requires careful coordination of state moves. For teams with complex logic embedded in their infrastructure — conditional resource creation, environment-aware configurations with many branches — Terraform can become difficult to maintain at scale.
Pulumi lets you write infrastructure code in TypeScript, Python, Go, or C#. If your team already codes in one of those languages, the shift to Pulumi is smaller than it looks. You get proper loops, conditionals, functions, and unit tests. The Pulumi ecosystem is smaller than Terraform's but covers the major providers. For teams who find HCL a bottleneck, this is the genuine alternative.
The flexibility is also the cost. Pulumi programs can grow complex in ways that Terraform configurations cannot. Without discipline, a team can write infrastructure code that is difficult for the next engineer to understand. The provider coverage gaps are also real. If your stack includes less common services, you may hit missing features that Terraform handles natively.
AWS CDK is compelling if you are AWS-only and your team writes TypeScript or Python. The constructs library is excellent, the L2 and L3 constructs abstract away significant boilerplate, and the integration with AWS services is deeper than third-party tools. It also generates CloudFormation, which means it runs inside your existing AWS security model.
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As soon as you have infrastructure outside AWS — a managed database from a different provider, an external DNS, a CDN not controlled through AWS — CDK requires you to break out of its model. Multi-cloud or hybrid environments are not what it was designed for.
For most teams we work with, Terraform is the default choice. The ecosystem, the community, and the talent pool make it the lowest-risk option for teams that do not have a strong reason to use something else. We move clients to Pulumi when they have complex conditional logic that is unreadable in HCL and their team writes TypeScript or Python fluently. We use CDK for AWS-native projects where the team is already writing CDK or where CloudFormation integration is a hard requirement.
The tool matters less than most people think. A well-maintained Terraform codebase run by a team that understands it will outperform a poorly maintained Pulumi codebase by a team still learning it. The consistency and discipline around how you use the tool is the variable that determines outcomes.
Written by
Khaled Kayyali
Co-Founder & CEO
AWS Certified Solutions Architect with 10+ years across software development, cloud infrastructure, and IT leadership. Leads cloud architecture and DevOps strategy for funded startups and scaling companies across MENA and global markets.
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