Software development and application deployments are a daunting task for small teams.
A homemade CI/CD deployment gives the automation velocity a small team needs to deliver value in a timeframe that makes sense while capitalizing on modern AI capabilities and minimizing risk.
With modern AI software development capabilities, the real questions aren't "should we take advantage of this?", it's usually centered around "how can we do this safely if we aren't software engineers ourselves, and cannot hire one?". The potential is sitting directly in technical teams laps but everyone is rightfully caught up in the question of risk.
Risk manifests itself in this scenario in terms of code security, maintainability, reliability, accidental exposure of secrets, broken deployments, dependency vulnerabilities, and lots of other ways. AI dramatically increases software output, but it does not automatically increase software understanding. That asymmetry is where most of the risk comes from.
With a basically free CI/CD pipeline most of these risks are mitigated to a meaningful degree that will most likely be adoptable risk for the disproportionate benefit provided. The goal in this use case is not an enterprise masterpiece, rather the goal is reducing the blast radius of a mistake enough that a small technical team can responsibly move fast and continue to improve their situations.
Goals
- Deploy applications safely without needing a dedicated DevOps engineer
- Keep infrastructure and operational costs close to zero
- Reduce the chance of catastrophic deployment mistakes
- Prevent direct production changes
- Add automated verification between generated code and production
- Create repeatable deployments
- Make rollback possible within minutes
- Keep the entire system understandable by one person
- Avoid building infrastructure more complicated than the application itself
Technologies
My current stack is intentionally simple, and is geared towards public facing deployments. A similar but slightly more technical stack can be repeated leveraging on premise technologies such as Gitlab, Kubernetes and Docker.
- GitHub for source control and pull requests
- GitHub Actions for automated testing, validation, and deployment gating
- Vercel for hosting and preview deployments
- Next.js for application structure
- Node.js for backend execution
- Environment variables and secret separation for credential handling in prod and non-prod environments
- Branch protection rules to prevent direct pushes to production
The point of this stack is not building a perfect enterprise CI/CD platform. The point is creating enough separation between generated code and production systems that obvious mistakes get caught automatically before they become production incidents.
The Pipeline
The pipeline itself is intentionally straightforward.
1. AI assisted code is developed locally 2. Changes are committed into a feature branch 3. A pull request is opened into main 4. GitHub Actions automatically runs:
- linting
- build validation
- dependency checks
- security scanning
- test execution
5. Vercel automatically creates a preview deployment 6. The change is reviewed in a live environment 7. Merge into main triggers production deployment 8. Rollback can happen through deployment history or Git history
This creates a lightweight containment layer between generated code and production infrastructure.
The important thing is that production access now passes through deterministic checks instead of emotion, urgency, or assumptions.
Why This Matters For AI Development
AI generated code is often surprisingly functional if it makes it to a deployment after passing the pipelined checks. The issues and risks presented are typically fast failing errors such as a failed build validation or testing failure, which are caught upstream before they become a production catastrophe.
Small teams fail in software development initiatives because there are no guardrails surrounding deployment, rollback, secrets management, dependency handling, or operational verification, not because they are incapable of writing or generating good code.
A lightweight CI/CD pipeline massively reduces the blast radius of mistakes while still allowing extremely high development velocity.
This is especially important for small technical teams where one person might simultaneously be:
- building the software
- operating infrastructure
- handling deployments
- supporting users
- troubleshooting incidents
- managing integrations
The infrastructure needs to reduce operational anxiety without becoming a full time occupation for a team member.
Small teams need infrastructure that meaningfully reduces risk without slowing development velocity to zero, and that enables them to continue to iterate toward their objectives.
Intended Outcomes
The intended outcome is not "enterprise masterpiece DevOps platform".
The intended outcome is:
- safer deployments
- faster iteration
- fewer production mistakes
- predictable rollback capability
- reduced fear surrounding deployments
- operational leverage for small teams
- enabling one technical person to responsibly maintain multiple products
Good infrastructure should be reassuring and as quiet as possible.
If your deployment process requires a dedicated engineer before your product even has users, the infrastructure has probably outgrown the business.
Your Turn
To start recreating what I have described here, simply point your web-browsing enabled ai of choice at the url of this webpage using the following prompt:
"Read and use the concepts from this article as the primary implementation guide: https://www.madsenmill.com/blog/realistic-ci-cd
Help me recreate this lightweight CI/CD pipeline using:
GitHub GitHub Actions Vercel Next.js Node.js
Prioritize:
low operational overhead deployment safety deterministic validation rollback capability simplicity for a solo developer or small team safe AI assisted software development
Do not over engineer the solution or introduce unnecessary enterprise complexity."