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Use Git LFS when a large file is part of a particular source revision and must be checked out with that revision. Use CI workflow artifacts for files a workflow run produces and other jobs or people need afterward. Use caches for reusable, regenerable dependencies; use release assets or object storage for distribution and durable storage outside source history.
“Artifact storage” is not one interchangeable product category. A run-scoped CI artifact, a reusable cache, a release binary and an object-storage archive have different lifecycles. Choosing by file role prevents bloated Git history, broken builds and unexpected retention or bandwidth costs.
Git LFS and artifact storage solve different problems
Git LFS (Large File Storage) keeps a small pointer in ordinary Git history while storing the corresponding large content separately. The pointer records the LFS version, an object identifier and the file’s final size. When a collaborator checks out a revision, the Git LFS workflow retrieves the content associated with that pointer.
A CI artifact is output from one workflow run. GitHub Docs defines it as “a file or collection of files produced during a workflow run.” A workflow can upload a compiled package, test report, coverage data or diagnostic bundle, then make it available to later jobs or reviewers. The artifact belongs to that run and follows the platform’s retention policy.
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A cache has a different contract: it accelerates future jobs by keeping reusable inputs such as dependency downloads or compiler caches. A job must still be able to recreate or download a cache miss. Treating a cache as the only copy of an important file is a reliability mistake.
Release assets and external object storage are distribution or durability paths. They are appropriate for binaries users download, generated datasets, archives and other files that should not become part of every clone. They are not substitutes for LFS when a file must be versioned alongside source, and they are not equivalent to a short-lived CI artifact.
Choose by the file’s lifecycle
| File role | Best default | Why | What to watch |
|---|---|---|---|
| Large source asset required by a specific commit | Git LFS | The commit records a pointer, and checkout can retrieve the matching content. | Every changed version consumes LFS storage; users need Git LFS configured. |
| Build output, test report or diagnostic bundle from one CI run | CI workflow artifact | It can be passed between jobs and inspected after the run without entering source history. | Retention is finite and deleting the run deletes its artifacts. |
| Dependency download or compiler intermediate reusable across runs | CI cache | It reduces repeated work and can be regenerated after eviction. | Cache misses must be safe; do not use it as archival storage. |
| Binary intended for users to download | Release asset or object storage | Distribution is separated from source checkout and can have a deliberate access path. | Define retention, permissions, integrity checks and a versioning policy. |
| Large generated dataset or long-term archive | Object storage or another dedicated archive | Durability and lifecycle rules can be managed independently of Git history. | Document the exact version or commit that produced it and how it can be retrieved. |
When Git LFS is the right choice
The file is an input to source-controlled work
Use LFS for assets that are logically part of the project: media used by an application, trained model weights that a particular commit expects, CAD or design files, large fixtures, or other binary inputs that must match a source revision. A branch or tag then identifies both the source and the intended asset version.
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LFS keeps the path and revision visible in the repository while moving the heavy bytes out of normal Git objects. A developer can clone and check out a commit, then let LFS fetch the content. This is different from a CI artifact, which a developer would have to locate in a workflow run and download separately.
Understand the pointer and client dependency
Without Git LFS installed and initialized, a collaborator may receive the pointer file rather than the underlying content. Make LFS a documented prerequisite for contributors, build agents and release automation. A healthy checkout should be verified with git lfs ls-files and by opening or hashing the expected binary, not merely by confirming that a path exists.
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Account for version multiplication
Each new full LFS version is stored when a tracked file changes. GitHub’s billing documentation illustrates the effect with a 500 MB file: even a one-byte change creates another 500 MB version for storage accounting. That illustration is a billing example, not a performance benchmark or a recommendation to split files. Frequently changing generated binaries can therefore consume storage quickly.
Know what source archives contain
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. LFS files referenced through an external .lfsconfig are not included. Decide whether archive users should receive pointers, full assets or a separate packaged download.
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When a CI artifact is the right choice
The file is produced by a run
Upload artifacts for outputs such as a compiled application, installer, test results, screenshots, logs, coverage reports or a signed package generated by a workflow. A later job can download the artifact without rerunning the producer, and a reviewer can inspect the exact output of a failed or successful run.
The output should not alter source history
Generated files that change on every build do not belong in ordinary Git merely because they are useful. Committing them grows clones and fetches, creates noisy diffs and makes source revisions harder to review. GitHub recommends keeping programmatically generated files outside Git; a workflow artifact is one suitable short- to medium-term destination.
Retention is part of the design
GitHub Actions documentation states a 90-day default artifact retention. For public repositories, the configurable range is 1–90 days; for private repositories, it is 1–400 days. Deleting a workflow run deletes its associated artifacts. These rules describe GitHub Actions and can change, so set and audit retention in repository or organization settings before promising that an artifact will remain available.
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Artifacts are not caches
An artifact preserves a run’s output for transfer or inspection. A cache stores reusable acceleration data, and jobs should continue to work when that cache is unavailable. If a cache is evicted, expired or invalidated, the workflow must be able to download dependencies or regenerate intermediates again. If a file is irreplaceable, upload it as an artifact or store it in a durable repository instead.
GitHub limits that affect the decision
The following figures are current guidance and enforcement details in GitHub documentation, not universal limits of Git itself or of other hosting services.
| Area | Documented GitHub figure | How to interpret it |
|---|---|---|
| Regular Git file size | More than 50 MiB triggers a warning; files over 100 MiB are blocked. | Use LFS or another store before a large file reaches the blocking threshold. |
| Repository size guidance | Below 1 GB is ideal; below 5 GB is strongly recommended. | These are repository-health recommendations, not a universal Git limit. |
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No larger than 10 GB recommended. | This concerns the local Git directory and is separate from the regular-file warning. |
| Enterprise Cloud push and object guidance | 2 GB push size enforced; 1 MB recommended maximum single object, with 100 MB enforced. | Large history rewrites and unusual objects can hit limits even when individual files appear manageable. |
| Maximum individual LFS file | 2 GB on Free and Pro, 4 GB on Team, 5 GB on Enterprise Cloud. | Plan and account settings determine the applicable limit; verify before uploading. |
| Included LFS storage and bandwidth | 10 GiB on Free and Pro; 250 GiB on Team and Enterprise Cloud. | Both stored versions and downloads consume quota; GiB is not the same unit as decimal GB. |
| Workflow-artifact retention | 90-day default; public repositories 1–90 days; private repositories 1–400 days. | Retention is configurable, and deleting a run removes its artifacts. |
Because limits, billing and policies change, check the current settings for the account, organization and repository before basing a production workflow on these numbers.
A decision framework for a new large file
- Ask whether the file is an input or an output. If a source revision requires a particular binary, model or media file, favor LFS. If a workflow creates it, favor an artifact, release asset or object storage.
- Ask whether it must be reproducible from source. A cacheable dependency or generated intermediate can be recreated and belongs in a cache. An output that must be inspected after a run should be an artifact.
- Ask how long it must survive. Match short review windows to artifact retention. Match user downloads and archives to release storage or object storage with explicit lifecycle rules.
- Ask who needs access. Contributors checking out a branch need the repository’s LFS path. CI jobs need permissions and an artifact download step. Customers need a documented release or download endpoint, not a private workflow run.
- Ask how often it changes and how large each version is. High-churn, large outputs can multiply LFS storage. Keep them out of source history unless revision coupling is essential.
- Ask whether the delivery path is external. If the goal is a downloadable installer, dataset or package, use release assets or object storage and record the producing commit, checksum and version.
Set up Git LFS for a new file
These steps assume a repository where Git LFS is available to every contributor and build agent.
- Install and initialize LFS. Run
git lfs installonce for the user or machine account. - Track the file pattern. For example,
git lfs track '*.psd'orgit lfs track 'assets/models/*.bin'. This writes or updates.gitattributes. - Review the attributes rule. Confirm the pattern covers the intended paths and does not accidentally route source text or small files through LFS.
- Add both metadata and content. Run
git add .gitattributes path/to/file, then commit with a message that explains the asset’s role. - Push and verify. Run
git push origin branch-name. On another machine, clone with LFS installed, check out the commit and verify that the file contains real content rather than a pointer.
Keep large files that are generated during CI out of this flow. Tracking a build directory with LFS only hides the growth in a different quota; it does not make generated output part of a maintainable source history.
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Upload and consume a CI artifact
Producer job
Have the build job create a uniquely named output directory, include a manifest or checksum, and upload that directory as an artifact. Names should include the platform, architecture and build identifier when multiple variants are produced. Avoid uploading secrets, temporary credentials or unfiltered workspace contents.
Consumer job
Make the consuming job download the artifact explicitly and fail if the expected file or checksum is missing. Do not assume that an artifact from an earlier run is present unless the workflow records the run identifier and has permission to access it.
Human review
Publish test reports, screenshots and logs as separate, clearly named artifacts so a reviewer can find evidence without downloading a multi-gigabyte archive. Set retention to the shortest period that satisfies debugging, compliance and release-support needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Moving an existing file into LFS
Adding an LFS rule does not remove bytes that are already in ordinary Git history. If a large file has been committed normally, migration requires rewriting history so old versions are replaced by LFS pointers. GitHub’s migration guidance calls for removing the existing file from repository history, configuring LFS tracking and pushing the rewritten history.
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- Coordinate first. Notify every contributor and automation owner. Existing clones contain the old history and will need to be recloned or carefully realigned after the rewrite.
- Make a backup and identify all paths and refs. Include branches and tags that must remain consistent. Confirm that no legal, audit or release process depends on the original commit identifiers.
- Run an LFS history migration. A command such as
git lfs migrate import --include='path/to/file'rewrites matching history; review the command’s scope before running it. - Inspect the rewritten repository. Check
.gitattributes, verify that the affected revisions resolve to LFS objects, and confirm that unrelated files were not changed. - Push with an agreed force-update procedure. Coordinate protected branches, tags and CI triggers. A forced update can invalidate open pull requests and cached commit references.
- Have collaborators recover deliberately. They may reclone, or reset and fetch the rewritten refs using the team’s documented procedure. Do not mix old and new histories casually.
History migration is a repository-wide change, not a local storage tweak. Schedule it when branch protection, mirrors, deployments and contributor availability can be managed safely.
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Costs, bandwidth and operational trade-offs
Git LFS economics
LFS usage has two dimensions: stored object versions and downloaded bytes. A frequently edited large file can consume storage repeatedly, while CI, clones and archive downloads can consume bandwidth. The 500 MB one-byte-change example demonstrates why apparent small edits can have large quota effects.
Artifact economics
Workflow artifacts consume the hosting service’s artifact storage while retained and use transfer bandwidth when jobs or people download them. The supplied GitHub figures specify retention ranges and LFS quotas, but not a universal artifact price; billing depends on the account and current service terms. Measure peak retained size, upload frequency and download volume before setting long retention.
Cache economics
Caches trade storage for shorter build times. Use bounded keys, avoid embedding secrets and include dependency or toolchain versions in the key. A cache that is too broad serves stale data; one that is too narrow delivers little speedup. Either way, the build must recover from a miss.
Release and object-storage economics
External distribution separates customer downloads from developer clones and CI runs. Apply lifecycle rules, checksums, access controls and replication appropriate to the data. GitHub documents releases as a way to distribute binaries outside tracked repository files; its cited guidance says total release binary size and bandwidth are not limited there, while each individual file must remain below the applicable LFS plan file-size maximum. Verify current plan terms before relying on that behavior.
Common failure modes and fixes
- A clone contains pointer text instead of the asset: install Git LFS, run
git lfs install, then fetch and check out the revision again. Ensure the build runner has LFS enabled. - A push is rejected for a large regular file: adding an LFS rule after the commit is insufficient. Remove the file from the affected history through a coordinated migration, then push the rewritten refs.
- A workflow cannot find yesterday’s artifact: check retention, run deletion, artifact name, permissions and whether the workflow is referencing the correct run. A deleted run cannot supply its artifacts.
- A cache miss breaks the build: move irreplaceable inputs to LFS, an artifact or durable storage, and make dependency installation or generation deterministic.
- Repository size keeps growing despite LFS: inspect LFS version churn, ordinary Git history, tags and generated files. Delete obsolete LFS objects according to the hosting service’s supported process; do not assume deleting a working-tree file removes historical objects.
- Users cannot download a release: separate internal CI artifacts from the public delivery path. Publish a release asset or object-storage object with documented permissions, checksum and version.
- Archives are unexpectedly small: source archives normally contain LFS pointers unless LFS objects are enabled for archives. Decide whether archive consumers should use a full release package instead.
Practical examples
Game or media repository
Source code and a 300 MB texture pack required by commit v2.4 belong in LFS. A nightly playable build belongs in a CI artifact for testers, and a signed public installer belongs in release storage. A shader compiler cache belongs in the CI cache and must be rebuildable.
Machine-learning project
A small, curated model checkpoint that must match an experiment commit can use LFS if collaborators need it during checkout. Training logs, evaluation reports and exported checkpoints from each run should be artifacts. A large production dataset should live in object storage with immutable version identifiers and a manifest recording the source commit.
Library or command-line tool
Keep source and tests in Git. Build archives for each operating system as workflow artifacts while validating them, then copy the approved binaries to release assets. Do not commit every platform’s generated archive merely to make it downloadable.
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- Is this file a versioned source input, a run output, a reusable cache entry or a user-facing distribution file?
- Must a checkout of a commit retrieve it automatically?
- Can the file be recreated from source and dependencies?
- Who needs it, and through which authenticated or public path?
- How long must it remain available, and what deletes it?
- What are the per-file, repository, storage and bandwidth limits for your specific plan?
- Will changing it create another full stored version?
- Have you recorded a checksum, producing commit and version identifier?
- Have CI runners and collaborators been prepared for the required client or permissions?
For GitHub-based workflows, the durable rule is simple: source-coupled large inputs go through Git LFS; files emitted by a particular run go to workflow artifacts; regenerable acceleration data goes to caches; distributable or long-lived generated data goes to releases or object storage. The lifecycle, not the file extension or its size alone, determines the correct home.
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