CI/CD for Modern Deployment: From Manual Deploys to PR Environments

Shipping code to production used to mean logging into a server, pulling the latest changes, and restarting a process. Teams that outgrew this approach moved to shell scripts, then to CI pipelines, then to container orchestration. Each step reduced manual intervention but added configuration surface area.

The underlying goal remains constant: move code from a developer's machine to production quickly and reliably. CI/CD is the set of practices that makes this possible. The implementation details vary widely, and the wrong approach introduces its own friction.

This guide covers what CI/CD involves, why shared staging environments create coordination problems, and how PR environments (also known as preview deployments or deploy previews) provide isolation without operational overhead.

CI/CD combines two practices:

• Continuous Integration (CI): merging code changes frequently and running automated validation on every merge. The goal is to surface integration problems early. A CI system typically runs on every push: it builds the code, executes the test suite, and reports whether the change is safe to merge.
• Continuous Delivery (CD): automating the path from a successful build to a deployed environment. Once tests pass, the code is packaged and ready for deployment. Some teams deploy automatically to production on every merge. Others deploy to a staging environment first and promote to production manually.

The alternative is batching changes and deploying on a fixed schedule. This creates predictable problems:

• Large releases contain more changes, which makes them harder to test and more likely to contain defects.
• When something breaks, identifying the responsible change requires investigation across the entire batch.
• Developers lose context on code they wrote weeks before deployment.
• Rollbacks become complex when multiple unrelated changes ship together.

CI/CD compresses the feedback loop. A developer pushes code, sees test results within minutes, and can deploy the change the same day. Problems surface while the context is fresh. Releases stay small enough that rollback means reverting a single change.

A CI/CD pipeline involves several components:

1. Source control (GitHub, GitLab, Bitbucket) stores code and tracks changes.
2. A CI service (GitHub Actions, CircleCI, Jenkins) runs builds and tests on every push.
3. Artifact storage holds build outputs: Docker images, compiled binaries, bundled assets.
4. Infrastructure (AWS, GCP, Kubernetes, VMs) runs the deployed application.
5. Deployment tooling (Terraform, Helm, custom scripts) moves artifacts to infrastructure.

Each component requires configuration. The CI service needs workflow definitions. Infrastructure needs provisioning and network configuration. Deployment tooling needs credentials and environment-specific settings. Secrets need secure storage and runtime injection.

Teams with dedicated platform engineers manage this complexity as part of their role. For teams without that specialization, the operational overhead becomes a tax on feature work. Every pipeline change, credential rotation, or debugging session pulls attention away from product development.

Deployment platforms emerged to absorb this overhead. The platform handles builds, hosting, scaling, and deployment orchestration. It integrates with source control and manages operational details internally. The tradeoff is flexibility: you accept the platform's deployment model in exchange for reduced configuration burden.

A common pattern is to maintain a shared staging/pre-production environment that resembles production in terms of configuration and connected services. While this approach works, it introduces coordination issues as soon as multiple developers work in parallel:

• If staging is down or misconfigured, no one can validate their work.
• If a developer needs to test a branch in isolation, they effectively block others from using staging.
• Multiple changes pile into the same staging cycle, increasing the size and risk of each release.
• When something breaks, it is difficult to determine which change in the batch caused the problem.

Some teams respond by provisioning additional staging environments: one per team, or a rotation system with scheduling. This reduces contention but increases infrastructure cost and introduces configuration drift. Environments diverge from production over time. Validation on staging becomes a weaker signal for production behavior.

The fundamental mismatch is structural. Developers work on branches in parallel. A single shared environment serializes that parallel work into a queue.

PR environments assign each pull request its own isolated deployment. The environment uses the same deployment configuration as production but maintains independent state: separate databases, separate URLs, separate network boundaries.

PR environments are created automatically when a PR is opened and destroyed when the PR is merged or closed. The lifecycle is tied to the pull request state.

This structure has several properties:

• Developers test features in parallel without interference. A broken branch affects only its own environment. Other team members continue working without interruption.
• Each PR represents a single reviewable change. Merging and deploying happen incrementally. Releases stay small, and rollback means reverting a single PR rather than untangling a batch.
• Code reviewers can interact with the running application. Review includes product validation, not just reading diffs. This surfaces issues that static code review cannot detect.
• PR environments are removed automatically when the PR closes. There is no manual teardown step, no orphaned infrastructure, no gradual accumulation of unused resources.

PR environments are most effective for stateless services, applications with seedable test data, and frontends or APIs where reviewers benefit from interacting with a live deployment. They work well with microservice architectures, where a PR modifying one service gets its own deployment while other services run stable versions.

PR environments have limitations that make them less suitable for certain testing scenarios.

Testing against shared external services with rate limits or quotas creates contention similar to shared staging. If multiple PR environments hit the same third-party API with limited capacity, they interfere with each other. Mocking external dependencies or using separate test accounts per environment addresses this, but adds configuration overhead.

Data migration testing on production-scale datasets is difficult to replicate in ephemeral environments. PR environments typically start with empty databases or minimal seed data. Validating that a migration runs correctly against millions of rows requires a different approach: a dedicated environment with a production data snapshot, or a separate migration testing process.

Load testing and performance benchmarking require sustained traffic and consistent infrastructure. PR environments are sized for functional testing, not for measuring performance characteristics under load.

Integration with systems that cannot be duplicated (payment processors in production mode, single-tenant external services) may require a shared environment or manual coordination regardless of PR environment availability.

Teams evaluating CI/CD platforms for deployment typically need the following capabilities:

• Git integration: The platform should deploy automatically when commits are pushed. Manual deployment steps slow feedback and introduce opportunities for human error.
• Rollback support: When a deployment causes problems, reverting to the previous version should be immediate. Platforms that retain deployment history and container images allow rollback without rebuilding.
• Secrets management: Applications require credentials, API keys, and configuration values that should not exist in source control. The platform should provide secure variable storage with scoping: different values for different environments, different values for different services.
• Scaling: Services should scale vertically (increased CPU and memory) and horizontally (additional instances) without reconfiguration. The platform should handle load balancing and orchestration internally.
• Database support: Most applications require persistent storage. Deploying databases alongside application services, with automatic credential injection, reduces the configuration required to connect components.
• Multi-service support: Production applications typically involve multiple components: APIs, background workers, frontends, databases, caches. The platform should deploy and network these components as a coordinated unit.
• Cost model: PR environments are ephemeral. A platform that charges for provisioned capacity makes frequent environment creation expensive regardless of actual utilization. Usage-based pricing aligns cost with the duration environments actually run.

Railway is a deployment platform with integrated CI/CD and PR environment support. Teams easily deploy apps, services and databases without manually assembling a pipeline.

Railway uses a custom builder that takes your source code and builds a container without any configuration. Whether you deploy a Go API, a Python worker, a Rust binary, a Node.js application, a WebSocket server, or something entirely custom, the platform treats them all the same.

You also have the option to provide your own Dockerfile. When Railway detects one in your repository, it uses that instead of the automatic builder. This gives you full control over the build process when your project requires specific dependencies, multi-stage builds, or non-standard configurations.

Railway integrates directly with GitHub. When you connect a repository, the platform monitors it for changes and builds a new deployment whenever commits are pushed to the linked branch.

Services can also be deployed from pre-built Docker images hosted on Docker Hub, GitHub Container Registry, Quay.io, or GitLab Container Registry.

When you configure a service to track an image, Railway monitors the registry for new versions. An update button appears in the service settings when a new image is available. For images that use a versioned tag (such as nginx:1.25.3), updating stages the new version. For tags without explicit versions (such as nginx:latest), Railway redeploys the existing tag to pull the latest digest.

Automatic updates can be enabled in the service settings. You specify a schedule and maintenance window, and Railway handles redeployments when new images appear. Private registry access requires the Pro plan and authentication credentials configured in the service settings.

This approach is useful for teams that build images in their own CI pipelines, use third-party software distributed as containers, or want to decouple the build step from the deployment platform.

Every push to a connected branch creates an immutable deployment. Railway retains the full deployment history for each service, so rolling back to a previous version is immediate: select a past deployment and promote it. There is no rebuild, no waiting for CI to run again. The previous container image is already available.

This model provides rollback safety without additional configuration. If a deployment introduces a bug, revert to the last known-good state in seconds.

For teams migrating from manual deployment workflows (pushing to EC2, managing ECS tasks, or running kubectl commands), this removes the operational overhead of tracking what is deployed where. Railway maintains the history, handles the orchestration, and keeps rollback as simple as clicking a button. The retention duration of previously deployed images depends on your plan.

Variables define configuration and secrets for services in a Railway project. Variables can be shared across services, scoped to individual components, or generated dynamically. Reference syntax (${{service.VAR}}) allows services to consume configuration from other services. Finally, variables can be sealed to prevent retrieval after creation.

A Railway project groups related services into a single unit. Most applications involve more than one component: an API, a frontend, background workers, a database, maybe a cache. Each runs as its own service within the project, deployed and scaled independently. Private networking connects them through internal DNS, so services communicate without exposing traffic to the public internet.

An environment is a complete configuration boundary within that project. It contains its own instances of every service, its own set of variables, and its own networking configuration. The same service definitions exist across environments, but each environment maintains independent state. This structure allows a single project to support multiple deployment targets (production, staging, feature branches) without duplicating project-level configuration.

Environments can also be created manually by duplicating an existing environment, which copies all services, variables, and configuration. This is useful for creating long-lived staging environments or testing configuration changes before applying them to production.

PR environments on Railway require toggling a single setting in the project configuration. Once enabled, Railway creates a complete environment for every pull request opened against the connected repository.

Each PR environment includes all services from the project deployed from the PR branch, databases and dependencies provisioned fresh, unique URLs for each exposed service, and isolated networking. Services in one PR environment cannot communicate with services in another PR environment over the private network.

When the PR is merged or closed, Railway deletes the environment and all associated resources.

Railway charges based on resource consumption: CPU time, memory, network egress, and storage. Billing is calculated per minute. PR environments that run for a few hours during review incur costs proportional to that duration.

This model makes ephemeral PR environments economically practical. Creating environments frequently carries no fixed cost penalty, and deleted environments incur no ongoing charges.

Teams moving from manual deployment processes often want to understand what changes when adopting a managed CI/CD platform. Manual workflows include SSH-based deployments, ECS task definition updates, and direct kubectl commands.

The tradeoff is control for reduced operational burden. A managed platform handles orchestration, networking, load balancing, and scaling internally. You do not configure infrastructure directly, manage container registries, or maintain deployment scripts. The platform makes decisions about how deployments execute.

For teams that need to customize their CI/CD workflow, Railway provides a CLI that can be integrated with existing CI/CD tools. The CLI can create environments, deploy services, and execute commands with environment variables injected. Here’s an example GitHub Actions workflows that can define deployment logic while using Railway for hosting:

name: PR Environments
on:
  pull_request:
    types: [opened, closed]

jobs:
  deploy:
    if: github.event.action == 'opened'
    runs-on: ubuntu-latest
    container: ghcr.io/railwayapp/cli:latest
    steps:
      - uses: actions/checkout@v4
      - run: railway link --project ${{ secrets.RAILWAY_PROJECT_ID }}
      - run: railway environment create pr-${{ github.event.pull_request.number }}
      - run: railway up

  cleanup:
    if: github.event.action == 'closed'
    runs-on: ubuntu-latest
    container: ghcr.io/railwayapp/cli:latest
    steps:
      - run: railway link --project ${{ secrets.RAILWAY_PROJECT_ID }}
      - run: railway environment delete pr-${{ github.event.pull_request.number }} --yes

This approach accommodates teams that maintain existing CI pipelines, require custom build steps, or need to run integration tests against PR environments before permitting merges.

CI/CD reduces the distance between writing code and running it in production. The practice compresses feedback loops, keeps releases small, and makes rollback straightforward. PR environments extend CI/CD by giving each change its own deployment, which removes the coordination overhead inherent in shared staging.
The pattern works best for stateless services, applications with seedable test data, and teams that prioritize fast iteration. It is less suited to load testing, production-scale data migration validation, or integration with external services that cannot be isolated per environment. Railway provides CI/CD with PR environments directly. Connecting a repository and enabling the feature causes the platform to create deployments automatically. Each pull request receives its own services, databases, URLs, and network isolation. Merging or closing the PR removes the environment and its resources. The outcome is smaller releases, faster feedback during review, and parallel development without environment contention. CI/CD becomes a property of the infrastructure rather than a pipeline assembled from separate tools.