As one of the core contributors to Packer, I’ve often seen the tool misunderstood—even by experienced DevOps and infrastructure engineers. Over the years, Packer has grown well beyond its original purpose, yet many myths still persist about what it can (and cannot) do. Let me take this opportunity to clear up some of these misconceptions and show why Packer remains a critical part of modern infrastructure automation at scale. Myth 1: Packer is only for building VM images Myth 1: Packer is only for building VM images This is perhaps the most common misconception. Early on, Packer became synonymous with building AMIs and traditional VM images. While that reputation is deserved, it’s no longer the whole story. Today, Packer supports a wide range of builders—from Docker and OCI to VM images across all major cloud providers, including AWS, Azure, Google Cloud, and Alibaba Cloud. It also integrates seamlessly with tools like QEMU, Ansible, and Vagrant, enabling diverse workflows across hybrid and multi-cloud environments. From a single declarative template, teams can build consistent base images for both VMs and containers, ensuring alignment across platforms and clouds. Combined with the plugin ecosystem, Packer can adapt as your environment and scale evolve. Myth 2: Packer doesn’t integrate well into CI/CD pipelines Myth 2: Packer doesn’t integrate well into CI/CD pipelines Another myth is that image building is too slow or manual to fit modern CI/CD workflows. While this may have been true years ago, today it’s quite the opposite. Packer integrates directly with CI/CD platforms like GitHub Actions and GitLab runners. Even better, with HCP Packer, you get automatic metadata pulled from these pipelines. This metadata is SLSA-compliant, ensuring supply chain security from build to deployment. This means every commit can trigger automated builds, inject metadata, and publish artifacts to shared registries. Instead of being a bottleneck, image building becomes a first-class citizen in delivery pipelines—fully auditable, traceable, immutable, and secure. Myth 3: If you use Terraform, you don’t need Packer Myth 3: If you use Terraform, you don’t need Packer This misconception comes from the overlap in cloud-native toolchains. While both are part of HashiCorp’s ecosystem, they serve complementary but distinct purposes. Terraform provisions infrastructure resources.
Packer builds the secure, pre-baked images those resources run on. Terraform provisions infrastructure resources. Terraform Packer builds the secure, pre-baked images those resources run on. Packer By using the two together, enterprises can achieve true immutable infrastructure. With Packer, images are hardened, consistent, and boot fast without fragile startup scripts. Terraform then deploys these images seamlessly, whether in single-cloud or multi-cloud environments. This tight integration is why Packer and Terraform are often paired to deliver scalable, predictable, and secure infrastructure workflows. Myth 4: Packer can’t scale to enterprise-level operations Myth 4: Packer can’t scale to enterprise-level operations This one is especially outdated. At enterprise scale, security, compliance, governance, and automation aren’t optional—they’re mandatory. That’s why we built HCP Packer, which extends the open-source foundation with enterprise-grade features. Here’s how HCP Packer takes image management to the next level: Software Supply Chain Security: Image ancestry tracking lets you see the full lineage of images and their relationships.
Artifact Visibility & Security: SBOM (Software Bill of Materials) storage provides deep insight into image components.
Compliance & Governance: Audit logs deliver visibility into how images are used across your organization.
Role-Based Access Control (RBAC): Bucket-level RBAC enforces fine-grained security and access policies. Software Supply Chain Security: Image ancestry tracking lets you see the full lineage of images and their relationships. Software Supply Chain Security: Artifact Visibility & Security: SBOM (Software Bill of Materials) storage provides deep insight into image components. Artifact Visibility & Security: Compliance & Governance: Audit logs deliver visibility into how images are used across your organization. Compliance & Governance: Role-Based Access Control (RBAC): Bucket-level RBAC enforces fine-grained security and access policies. Role-Based Access Control (RBAC): With these capabilities, HCP Packer provides the reliability, governance, and compliance that enterprises demand—while still empowering teams to build at scale. Final Thoughts Final Thoughts Packer is not just a legacy VM image builder—it’s a modern, extensible, and enterprise-ready platform for image automation across clouds, containers, and hybrid environments. By combining broad builder support (AWS, Azure, GCP, AliCloud, Docker, QEMU, Ansible, Vagrant), seamless CI/CD integration with SLSA-compliant metadata, and tight interoperability with Terraform, Packer enables enterprises to build truly immutable, auditable, and secure infrastructure. And with HCP Packer, organizations can now bring image automation under the same umbrella of compliance, visibility, and supply chain security that’s demanded at global scale. For those still thinking of Packer as “just a VM builder,” it’s time to revisit it. Far from fading into obscurity, Packer is becoming the quiet powerhouse of infrastructure automation—shaping how enterprises deliver consistent, secure, and reliable platforms in the cloud era.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Relearning Packer: 4 Common Myths About Image Automation Debunked

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Streamline Your Visual Content with Advanced Image Tagging APIs

5 Best Microservices CI/CD Tools You Need to Check Out

Best CI/CD Tools to Consider in 2022

Building a CI Pipeline with Databricks dbx Tool and GitLab

Building Continuous Delivery Pipeline using CDK Pipelines Modern API

Comparing The Top 10 Kubernetes CI/CD Tools

Streamline Your Visual Content with Advanced Image Tagging APIs

5 Best Microservices CI/CD Tools You Need to Check Out

Best CI/CD Tools to Consider in 2022

Building a CI Pipeline with Databricks dbx Tool and GitLab

Building Continuous Delivery Pipeline using CDK Pipelines Modern API

Comparing The Top 10 Kubernetes CI/CD Tools

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps