Use Git LFS when a large file is part of a specific source revision and must be checked out with that revision. Use CI artifacts for files produced by a workflow run, caches for reusable data that can be regenerated, and release assets or object storage for binaries and generated data meant for distribution or durable retention.
“Artifact storage” is not one interchangeable product. Its correct choice depends on the file’s lifecycle, who needs it, how long it must survive, and whether it belongs in source history at all.
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The short decision: match storage to the file’s job
Start by asking what the file represents rather than how large it is. A 700 MB 3D model used as source input has a different home from a 700 MB installer built by continuous integration. Size affects limits and cost, but lifecycle is the deciding factor.
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|---|---|---|---|
| Large source asset that must match a commit, tag, or branch | Git LFS | A small pointer is committed into Git while the binary content is fetched through the LFS workflow. | Developers, build jobs, and tools checking out that revision |
| Output created by one CI run | CI workflow artifact | The run can pass files between jobs and preserve them for review without adding generated bytes to source history. | Later jobs, reviewers, and the team investigating that run |
| Dependency or intermediate that can be recreated | CI cache | A cache accelerates future runs but is not the authoritative copy of the file. | Subsequent workflow runs |
| Installer, package, model, or dataset intended for users | Release asset or object storage | Distribution and durable retention are separate concerns from source revision history. | Customers, operators, or downstream systems |
If a build consumes a large model that developers deliberately version, keep the model in Git LFS and let CI download it during checkout. If the build creates a package, upload that package as a workflow artifact for short-term inspection and attach the approved version to a release or external object store for distribution.
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How Git LFS works
The repository contains a pointer, not the binary
Git Large File Storage (Git LFS) replaces the large file in ordinary Git history with a text pointer. The pointer records the LFS specification version, an object identifier, and the final file size. The repository therefore remains relatively small, while the actual content is stored in LFS storage managed by the hosting service.
When a user checks out a revision containing an LFS-tracked path, the LFS client resolves the pointer and downloads the matching object. The result in the working tree is the real binary, so editors and build tools can use it normally. Git still versions the path and the pointer, which ties the requested content to a commit.
Changing the file creates another stored version
Each new full LFS version is uploaded when a tracked file changes. GitHub’s billing documentation illustrates the consequence with a 500 MB file: even a one-byte change results in another 500 MB version counted toward LFS storage. Downloads count toward bandwidth as well. This is a billing illustration, not a performance benchmark, but it shows why frequently changing large binaries can consume storage quickly.
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Everyone who needs the underlying content must have a Git LFS-capable workflow. A collaborator who clones or checks out without LFS configured may see pointer text instead of the binary. Make LFS installation part of workstation setup and CI images, and verify that automated checkouts actually fetch LFS objects before a build starts.
Source archives have a special behavior
GitHub source archives include LFS pointer files by default, not the large objects. Repository settings can include LFS objects in archives, but those downloads count against LFS bandwidth. External LFS files configured through .lfsconfig are not included. Do not assume that downloading a ZIP of a repository is equivalent to an LFS-aware checkout.
What “artifact storage” can mean
Teams often use “artifact storage” as a catch-all term. In a repository workflow, separate at least four services with different guarantees.
CI workflow artifacts are run-scoped outputs
GitHub Docs defines an artifact as “a file or collection of files produced during a workflow run.” A job uploads files such as compiled packages, test reports, screenshots, coverage data, or logs. Other jobs can download them, and people can inspect them after the run finishes.
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Artifacts are associated with a particular run, commit, and workflow attempt. That association is useful for answering “what did this build produce?” It does not make the artifact part of every future checkout, and it does not make the artifact a permanent archive.
Caches are disposable acceleration data
A cache stores reusable dependencies or intermediates so later jobs do less work. Examples include package-manager downloads, compiler caches, and generated indexes. A cache key normally identifies the inputs that make the cached data valid.
Caches and artifacts serve different contracts. A job should still be able to recreate or download a cached input when the cache is missing or evicted. Treating a cache as the only copy of a release, test result, or required source asset creates an unreliable build.
Release assets are delivery files
A release asset is attached to a versioned release for people or systems that need to download a finished binary. This is appropriate for installers, command-line packages, firmware images, and signed archives that users should obtain without cloning source.
GitHub documents releases as a way to distribute binaries outside tracked repository files. Its documentation states that total release binary size and bandwidth are not limited there, while each individual file must remain below the applicable LFS plan file-size maximum. Confirm the current plan rules before publishing unusually large assets.
Object storage is for durable or high-volume data
External object storage is usually a better home for generated datasets, nightly builds, archives, and machine-to-machine downloads that outlive a workflow run. It can provide lifecycle rules, replication, access policies, and a stable download endpoint without inflating a Git repository. GitHub recommends keeping programmatically generated files outside Git, such as in object storage.
Git LFS and CI artifacts compared by the decisions that matter
| Decision axis | Git LFS | CI workflow artifact | Cache | Release asset or object storage |
|---|---|---|---|---|
| Tied to a source revision? | Yes. The pointer is committed and resolves to content for that revision. | Indirectly. The file belongs to a particular workflow run, usually triggered by a revision. | Usually by a cache key, not by a permanently addressable source revision. | Release assets are tied to a release; object storage can use any versioning scheme you define. |
| Produced by CI? | Normally an input to CI, although a generated file can technically be tracked. | Yes. Designed for outputs, reports, and files passed between jobs. | Often. Designed for reusable dependencies and intermediates. | Often, when a pipeline promotes a build for users or long-term retention. |
| Expected retention | As long as the repository history and LFS object are retained. | Configurable and commonly short- or medium-term. | Disposable; jobs must tolerate a miss. | Chosen for distribution or durable storage, subject to the service policy. |
| How users obtain it | Install Git LFS, then clone or pull the revision. | Open the run or have a later job download its named artifact. | Let the workflow restore the key; regenerate on a miss. | Download from a release or an authenticated object-storage location. |
| Main cost pressure | Stored versions and LFS download bandwidth. | Artifact storage duration, transfer, and CI-service quotas. | Cache storage and transfers, balanced against rebuild time. | Storage, egress, replication, and retention policy. |
| Best access audience | Contributors and automation building a particular revision. | Developers and jobs examining one run. | Automation rerunning similar work. | End users, operators, or external systems. |
GitHub limits and quotas to check before choosing
The following figures are current GitHub documentation guidance and enforcement details, not universal Git limits. GitHub, GitHub Actions, GitLab, Bitbucket, self-hosted servers, and independent artifact stores can apply different policies.
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| Area | Documented GitHub figure | What it means |
|---|---|---|
| Regular Git file warning | More than 50 MiB | GitHub displays a warning. The file is not automatically rejected at this threshold. |
| Regular Git file block | More than 100 MiB | GitHub blocks the file in ordinary Git. |
| Recommended repository size | Below 1 GB ideally; below 5 GB strongly recommended | These are GitHub recommendations intended to protect clone and fetch performance. |
Enterprise Cloud on-disk .git guidance |
No larger than 10 GB recommended | This is a separate repository-disk recommendation, not the same as the 1 GB/5 GB guidance. |
| Enterprise Cloud enforced push size | 2 GB | A push above this limit is rejected on that platform. |
| GitHub LFS maximum individual file | 2 GB on Free and Pro; 4 GB on Team; 5 GB on Enterprise Cloud | Check the account’s current plan and policy before adding a large object. |
| Included LFS storage and bandwidth | 10 GiB on Free and Pro; 250 GiB on Team and Enterprise Cloud | Storage and download usage consume the included allowance; billing terms can change. |
| GitHub Actions artifact retention | 90-day default; public repositories can configure 1–90 days; private repositories 1–400 days | Retention is configurable, and deleting a workflow run deletes its artifacts. |
The regular Git thresholds explain why “it fits on my disk” is not a sufficient reason to commit a binary normally. Even files below 100 MiB can accumulate across history and slow clones. Conversely, moving every large file to LFS does not solve retention, distribution, or bandwidth questions.
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Recipe: version a large source asset with Git LFS
Use this path for assets such as audio stems, training checkpoints, textures, CAD files, or test fixtures that must be reproducible with a commit.
- Install and initialize Git LFS. On a developer machine or CI image, install the Git LFS client and run
git lfs install. - Choose an explicit tracking rule. For a single extension, run
git lfs track "*.psd". For a particular path, rungit lfs track "assets/large-model.bin". This writes or updates.gitattributes. - Commit the attributes file. Stage
.gitattributestogether with the asset so every clone knows which paths use LFS. - Add and commit the asset. Run
git add .gitattributes assets/large-model.binfollowed bygit commit -m "Track large model with Git LFS". - Push both Git history and LFS content. Run
git push origin your-branch. The Git push sends the pointer; the LFS transfer sends the binary object. - Verify from a clean checkout. Clone into a fresh directory, check out the commit, and run
git lfs ls-files. Confirm that the working-tree file has the expected size and that a build can open it.
Keep the tracking pattern narrow. Tracking every binary extension in a monorepo can move files that are better handled as build outputs or releases. Review the resulting LFS storage and bandwidth usage when a large asset changes frequently.
Recipe: publish a CI output without polluting Git history
Compile, test, and package in CI; then upload only the files needed by later jobs or reviewers. A GitHub Actions workflow can use an artifact-upload step with a named path and an explicit retention period:
- name: Upload test report
uses: actions/upload-artifact
with:
name: test-report
path: reports/
retention-days: 14The exact action version and organization policy should be checked in the workflow’s environment. Choose a retention period that matches the review window, not an assumed archival requirement. A 14-day report is useful for debugging a run; it is not a substitute for a compliance archive.
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For a multi-job build, upload the package in the producer job and download the named artifact in a signing or integration-test job. This keeps the hand-off tied to the run and avoids a second checkout of generated files. If the output becomes a public or customer-facing binary, promote it to a release asset or object storage after validation.
Recipe: use a cache only when a miss is safe
Typical cache candidates are package-manager directories, compiler intermediates, and downloaded toolchains that can be recreated from a lockfile or pinned URL. A cache key should include the platform and the inputs that affect validity, such as the lockfile hash.
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Design the job so a cache miss follows the normal download or build path. Never make a cache the only copy of a test report, signing output, release package, or source asset. If restoring stale data could produce an incorrect binary, include a complete invalidation key or skip caching that data.
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Use a release asset when the file belongs to a named software version and users need a straightforward download. Record checksums and signatures alongside the binary when your release process requires verification. Use object storage when files are generated continuously, exceed convenient release-management workflows, need custom lifecycle rules, or are consumed by services rather than people.
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Keep the source repository as the place for the recipe and metadata: build configuration, manifests, reproducibility instructions, and the release process. Keep the generated payload in the distribution system. This separation prevents routine rebuilds from adding large, immutable blobs to every clone.
Migrating a file already committed to ordinary Git
Adding an LFS rule does not retroactively shrink existing commits. If the large file is already in normal Git history, migration has repository-wide consequences: commit identifiers can change, every contributor may need to reclone or carefully reset branches, and forks or pull requests can become difficult to reconcile.
- Inventory references. Find every branch, tag, fork, automation job, and release process that expects the old path or commit IDs.
- Coordinate a maintenance window. Tell contributors to stop pushing while history is rewritten, and make a backup of the repository.
- Rewrite the history with the Git LFS migration tooling. GitHub’s migration guidance calls for removing the existing file from repository history and then configuring LFS tracking. Use an include pattern that targets only the intended paths.
- Update attributes and working files. Add the relevant
git lfs trackrule, commit.gitattributes, and confirm the migrated paths resolve to LFS objects. - Force-push the coordinated result. Push rewritten branches and tags only after the team agrees. Protected-branch rules may require an administrator to perform this step.
- Have every consumer refresh its clone. A fresh clone is often safer than merging rewritten history into an old working copy. Recreate CI credentials and verify that all LFS objects are available.
For a repository with many external consumers, leaving historical blobs in place and moving only future revisions may be less disruptive, even though the repository remains larger. The right choice depends on the size problem, compliance requirements, and the number of downstream clones.
Common mistakes and how to correct them
Putting build outputs in Git because the build needs them
A build output is still generated data. Commit the source and build instructions, then transfer the output as an artifact or publish it through a release or object store. This avoids repeated binary revisions that make clones and fetches slower.
Using a CI artifact as an archive
Artifact retention is configurable, defaults to 90 days in GitHub Actions, and differs for public and private repositories. Deleting the workflow run deletes its artifacts. Export records that must survive that policy to a release or durable storage system.
Using a cache as a source of truth
When a cache expires or is evicted, the workflow should continue by regenerating the content. If it cannot, move the file to LFS, a release, or object storage according to its role.
Assuming LFS removes all cost of versioning
LFS keeps pointer files in Git, but every changed large object consumes storage and downloads consume bandwidth. Split assets when your toolchain permits, avoid rewriting large files for insignificant metadata changes, and monitor usage against the plan allowance.
Forgetting the checkout mode in CI
A CI checkout that leaves LFS pointers in the workspace can fail with confusing “invalid format” or unexpectedly tiny-file errors. Ensure the runner has Git LFS installed, fetches LFS objects, and verifies the resulting file before compilation.
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By default, a GitHub source archive contains pointers. Use an LFS-aware clone for development, configure archive inclusion when appropriate, or publish a release asset for users who need a complete bundle.
Ignoring the audience
Contributors who need a file alongside source revisions favor LFS. Customers who need a downloadable installer favor releases. Services that consume large generated datasets favor object storage. A single repository can use all three without treating them as duplicates.
A practical selection checklist
- Does the file need to be exactly reproducible from a commit, branch, or tag? Choose Git LFS.
- Is it created by one workflow run and needed by another job or a reviewer? Upload a CI artifact.
- Can it be downloaded or regenerated whenever required? Consider a cache.
- Must users download it without cloning source? Use a release asset or object storage.
- Must it survive longer than the workflow’s retention policy? Do not leave it only as a CI artifact.
- Will the file change often at hundreds of megabytes? Estimate LFS version growth and bandwidth before committing to it.
- Does the chosen GitHub plan permit the individual file size and provide enough LFS storage and bandwidth? Check current account limits.
- Will people without Git LFS need access? Publish a conventional archive or release asset as the user-facing delivery path.
- Is the file already in ordinary Git history? Plan the migration and communicate rewritten commit IDs before running history-rewrite commands.
Bottom line
Git LFS is the right answer when a large binary is source: its pointer belongs in repository history, and the matching content should arrive with a checkout. CI artifacts are the right answer when a workflow run produces a file that must move between jobs or remain available for review. Caches are disposable accelerators, not archives. Releases and object storage are the right destinations for distributable binaries and generated data that must outlive individual runs. Choosing by lifecycle keeps source history usable, makes retention intentional, and prevents a run artifact or cache from being mistaken for a versioned source asset.
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