Inferensys

Glossary

Helm Chart Offline Packaging

The process of bundling a Helm chart and all its dependent container images into a single transportable archive for deployment into a disconnected Kubernetes cluster.
DevOps engineer deploying LLM to production on laptop, Kubernetes dashboards visible, late night deployment session.
AIR-GAPPED DEPLOYMENT STRATEGY

What is Helm Chart Offline Packaging?

Helm chart offline packaging is the systematic process of bundling a Helm chart, its subchart dependencies, and all referenced container images into a single, transportable archive for deployment into a disconnected or air-gapped Kubernetes cluster.

Helm chart offline packaging is the technical procedure of resolving and bundling all external dependencies of a Kubernetes application definition into a self-contained artifact. This process involves executing helm dependency update to fetch and cache subcharts, then using helm package to create a compressed .tgz chart archive. The critical distinction from standard packaging is the parallel extraction of every container image referenced in the chart's templates and values files, which are then exported via tools like skopeo or docker save to create a co-located image tarball, ensuring no runtime pull from an external registry is required.

The resulting offline bundle is physically transferred to the air-gapped environment using a data diode or removable media, where a private registry is primed with the container images and the chart is pushed to a local chart museum or OCI-compliant repository. This methodology eliminates the ImagePullBackOff errors that occur when a disconnected cluster attempts to contact public registries like Docker Hub or gcr.io. For sovereign AI infrastructure, this guarantees that the entire software supply chain for a model-serving stack—including the GPU Operator, inference server, and autoscaler—is verified, immutable, and deployable without any external network dependency.

OFFLINE PACKAGING

Key Characteristics

Helm chart offline packaging transforms a dynamic, network-dependent deployment artifact into a static, self-contained archive suitable for air-gapped environments. This process ensures that every dependency, from the chart metadata to the container images, is locally resolvable.

01

Complete Dependency Resolution

The packaging process recursively resolves and bundles all subcharts and their transitive dependencies into a single archive. This eliminates the need for the target cluster to reach out to external Helm repositories. The final package is a .tgz file containing the full chart tree, ensuring that a helm install command in a disconnected environment will not fail due to a missing requirements.yaml dependency.

02

Container Image Relocation

A critical companion step is the relocation of all referenced container images. Tools like helm chart save or skopeo are used to:

  • Scan the rendered manifests for every image reference
  • Pull images from external registries
  • Re-tag them to point to a private registry accessible in the air gap
  • Push them to that internal registry This ensures that when a pod is scheduled, the kubelet can pull the image without an internet connection.
03

Immutable Archive Creation

The output is a single, versioned, immutable .tgz artifact. This archive becomes the source of truth for that specific release in the disconnected environment. Its immutability guarantees that the exact same set of files and metadata tested in staging is what gets deployed to production, eliminating configuration drift caused by dynamic repository lookups. Verification is done via a sha256 checksum.

04

OCI-Compliant Storage

Modern Helm packaging leverages Open Container Initiative (OCI) registries as a universal storage layer. Instead of relying solely on legacy chart repositories, a chart and its associated images can be bundled and pushed as OCI artifacts to a single private registry. This unifies the storage and access control model for both application code and its runtime dependencies under one standard API.

05

Air-Gap Deployment Readiness

The packaged chart is explicitly designed for environments with a physical air gap. It assumes zero external network egress. All hooks, init containers, and custom resource definitions (CRDs) must be pre-cached. The packaging workflow often integrates with a private registry mirroring script to automate the entire bundling process, converting a dynamic Helm chart into a static, air-gap-compliant deployment unit.

HELM CHART OFFLINE PACKAGING

Frequently Asked Questions

Answers to the most common technical questions about bundling, transporting, and deploying Helm charts into air-gapped and disconnected Kubernetes environments.

Helm chart offline packaging is the process of bundling a Helm chart and every OCI-compliant container image it references into a single, transportable archive for deployment into a disconnected Kubernetes cluster with no internet access. The workflow begins by rendering the chart to identify all image: fields, then using a tool like helm chart save or skopeo to pull each image into a local cache. These images are exported as .tar files alongside the chart's .tgz package. The entire directory structure is then compressed into a monolithic archive, physically transferred via removable media, and loaded into a private container registry on the air-gapped side. This guarantees that a helm install command can succeed without any external pull requests, satisfying strict data residency enforcement and air-gapped model deployment requirements.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.