Packer is an incredibly powerful tool for automating the creation of machine images, but it serves two distinct purposes. On one side, it is a production tool, responsible for generating final, immutable artifacts that will be deployed to run real workloads. On the other side, it is a development tool, used to iterate, refine, and troubleshoot the process of building those artifacts. Many users approach Packer as though it exists only for the first purpose — treating it as a fire-and-forget automation tool — but the reality is that most of the time spent with Packer is on the development side, debugging and refining configurations before they ever become production-ready.

Building a reliable Packer image is an iterative process. You don’t just write a template and immediately get a perfect, production-ready image. Instead, you work with a virtual machine, applying trial and error to figure out how software needs to be configured, determining the right dependencies, and scripting the installation and setup in a repeatable way. Whether you’re using Bash, PowerShell, or some other scripting language, the process requires experimentation, debugging, and refinement before you can lock in a final image. To get the most out of Packer, it helps to embrace three key principles: create a repeatable process, test locally, and promote images rather than rebuilding them from scratch.

№1: Create a repeatable process to build packer images

One of the most frustrating things about working with Packer is that building images can take a long time. This makes it imperative to set up a repeatable, automated build process. The last thing you want is to manually kick off long-running image builds every time you make a change. Instead, your automation platform of choice (e.g., Azure DevOps, GitHub Actions, etc.) should have a Packer build pipeline that automatically triggers when environment configurations change.

A well-structured Packer build pipeline must account for two things: network connectivity and authentication. First, network connectivity is critical because Packer needs to connect to the virtual machines it creates. If your build process runs on a private network, Packer’s VM must be able to attach to that network so that it can be accessed. Since Packer primarily uses SSH (or RDP for Windows), you need to ensure that it has line-of-sight from the build agent. There are two approaches: either configure your networks to allow direct connectivity, or expose the build agent’s public IP so that Packer can reach it. Private networking is preferable for security reasons, but it introduces higher costs and complexity, so weigh the trade-offs carefully.

Authentication is the next concern. Ideally, your Packer builds should not rely on long-lived secrets stored in environment variables. Instead, use a federated identity approach like OpenID Connect (OIDC), which allows your build process to authenticate without storing credentials. Most cloud providers now support OIDC authentication for build agents, making it a secure and scalable option.

№2: Test Locally

While a fully automated build pipeline is essential, it’s just as important to be able to test and iterate locally. Developing a Packer image without a local test setup is like trying to write software without a debugger — you’re flying blind, and every iteration takes exponentially longer.

If you’ve implemented an OIDC authentication strategy in your pipeline, you’ll need a way to swap in local credentials when testing. Likewise, your network configuration will dictate how you connect to the virtual machine during development. If you’ve opted for private networking, you may need to connect via a Point-to-Site (P2S) VPN. If you’re using public IPs for development, you can simply open a firewall rule to allow SSH access from your local machine. When debugging locally, always use Packer’s -debug flag and enable logging:

export PACKER_LOG=1

The -debug flag causes Packer to pause at each step, allowing you to inspect the environment before proceeding. Additionally, when running in debug mode, Packer generates a .pem file that can be used to SSH directly into the temporary VM it creates. This allows you to manually test the provisioning steps within your image. If something fails, you don’t have to guess why—you can log in, investigate, and adjust your scripts accordingly.

For example, if your VM has the IP 10.1.2.3, you can SSH into it with the generated key:

ssh -i ./vm-pkrvmtang1jjpc9.pem packer@10.1.2.3

This ability to pause, inspect, and iterate makes local testing invaluable. You can rapidly tweak configurations, verify software installations, and troubleshoot dependency issues without waiting for full builds to complete.

№3 Promote Images to Upstream Environments — don’t re-build from code

Unlike Terraform, where infrastructure is rebuilt per environment, Packer creates immutable artifacts that should be promoted through environments rather than rebuilt. When a Packer image is built, it represents a specific moment in time, complete with all dependencies, configurations, and software versions. If you rebuild the same Packer template at a later date, even with the same code, there’s no guarantee that the result will be identical. This is because software dependencies and system packages change over time. If your Packer template installs the “latest” version of a package, you’ve already introduced non-idempotent behavior. Even if you pin specific versions, transient network issues or upstream repository changes can still cause inconsistencies between builds. The safest approach is to build, test, and promote a single image.

In Azure, for example, you should build an image in a development environment and then promote it by copying it from a DEV image gallery to a PROD gallery. Production workloads should only pull from the PROD gallery, ensuring that they always use a tested and verified image. This approach has an added benefit: it provides a clear semantic versioning trail from development to production. You may see version gaps — jumping from 1.0.0 to 1.0.5—but that’s a feature, not a bug. Versions 1.0.1 through 1.0.4 may have existed in development but were rejected before reaching production. This aligns with a continuous delivery mindset, where integration and testing happen continuously, but promotion to production is a deliberate decision.

Conclusion

Packer is more than just an image-building tool — it’s a development tool that requires iteration, testing, and careful management of the images it produces. The key to success is treating it like a software development process: build a repeatable workflow, test locally, and promote tested images rather than constantly rebuilding.

By embracing these principles, you can ensure that your Packer-based image pipeline is both efficient and reliable, reducing surprises in production while maintaining a smooth development experience for your Virtual Machine-based workloads!