A repository becomes easier to maintain when every file has a clear job. Source code explains how an application works. Design assets may be inputs to that application. Build packages are outputs. Customer uploads are changing application data. Putting everything into the same Git history can blur those responsibilities and make routine cloning, reviewing, and recovery harder than necessary.

Git LFS is useful when a large asset must follow the same revision decisions as the code. It is less useful as a general dumping ground for videos, database exports, or every generated download. Start by classifying your files, then choose storage. Our Git file fundamentals explain the underlying repository model; this guide focuses on deciding where larger objects belong.

Understand the pointer and the payload

Git LFS records a small pointer in Git while storing the corresponding file content through a separate LFS service. A working checkout can contain the actual image, audio file, or dataset after the client retrieves it. This creates two recovery obligations: preserve the Git history containing the pointers and preserve the referenced LFS objects.

The official Git LFS project guide documents installation, tracking patterns, and the role of the committed .gitattributes file. Treat those attributes as shared project configuration. A colleague should not need to guess which files require LFS or reconstruct your local setup from memory.

Choose storage by purpose

For each large file, ask whether a developer needs its exact version to reproduce a particular commit. If the answer is yes, decide whether ordinary Git remains practical or whether LFS gives the team a better workflow. File size matters, but so do revision frequency, contributor connectivity, review habits, and the number of historical versions you intend to retain.

  • Ordinary Git: readable source, small configuration files, documentation, and modest assets that belong directly in the project history.
  • Git LFS: substantial source assets whose exact revisions must remain associated with specific code revisions.
  • Artifact storage: compiled applications, generated archives, release packages, and other outputs identified by the build that produced them.
  • Application storage: user uploads, operational exports, and changing media libraries with their own access and retention policies.

A small reference dataset for a repeatable test may deserve version control. A growing collection of customer records usually demands a separate data lifecycle. Likewise, an editable design master and the optimized image generated from it can have different storage destinations even though they represent the same visual.

Walk through one complete asset lifecycle

Consider a web design project with an editable animation master, an exported promotional video, and thumbnails generated for the homepage. The master may belong in LFS because the team must reproduce earlier edits. The finished video can be a release artifact delivered from media storage. The thumbnails may be reproducible build output. Record the connection among them using an asset name and release identifier.

Ask what happens when the master changes. Who reviews it, which exports must be regenerated, and which pages need the replacement? This exercise often reveals missing automation or ownership before file size becomes the immediate problem. It also prevents a team from preserving one large source file while losing the instructions needed to turn it into the finished website.

Create a narrow tracking policy

Agree on path patterns before adding a large asset. A narrow rule such as tracking the design directory is easier to understand than indiscriminately routing every image through LFS. Record why a rule exists, who owns the assets, and whether collaborators need specialist software to inspect them.

git lfs install
git lfs track "design/*.psd"
git add .gitattributes
git status

Run this example from the intended repository after installing Git LFS. Review the generated attributes before adding the actual design files. Commit the policy with a clear explanation so later contributors can connect the rule to the project requirement.

Tracking a pattern does not automatically rewrite older commits or convert every previously committed object. Also, adding a path to .gitignore does not remove a file already tracked by Git. Inspect the repository state before assuming either configuration change has cleaned up existing history.

Make a fresh checkout part of the workflow

A developer with a populated local cache may never notice that an asset was not uploaded correctly. A build on a fresh machine exposes that dependency. Test a clean checkout using the same access level as the deployment job, then verify that required media files contain usable content rather than pointer text.

Give automated builds the minimum access needed to retrieve their inputs. Keep access configuration outside committed files. Decide whether preview builds from outside contributors should retrieve private assets, and ensure the build can explain when an intentionally unavailable asset prevents completion.

Separate input validation from application compilation. If an image is missing, fail with the asset path and the expected retrieval step. A generic rendering failure later in the pipeline is harder to diagnose. Preserve the commit identifier and build configuration alongside the resulting release package.

Review binary changes deliberately

Large binary files rarely support the same line-by-line review as text. Establish a review format that matches the asset: before-and-after screenshots for design work, a short change note for audio, or a schema and provenance description for a dataset. The pointer change alone cannot explain whether the new content is correct.

For assets that people cannot safely edit concurrently, agree on an ownership or locking process supported by your tools. Avoid assuming that a clean Git merge means the underlying creative work was reconciled. Two contributors can make individually reasonable changes that still need a human decision about the final asset.

Store generated previews where reviewers can inspect them without requiring the full production dataset. Keep those previews small and identify the asset revision they represent. This reduces review friction without turning preview images into a second, unexplained source of truth.

Plan historical migration separately

Moving an established repository to LFS deserves a coordinated maintenance task. First determine whether you only need a better policy for future commits or whether old large objects must leave ordinary Git history. Those are different projects with different disruption costs.

A history rewrite can change commit identifiers and affect branches, tags, open reviews, automation references, and existing clones. Prepare a recoverable copy, map the branches that matter, and rehearse on an isolated repository. Ask contributors to pause conflicting pushes during the agreed migration window.

Afterward, validate representative old releases as well as the current branch. A successful checkout of the latest commit does not prove that every historical asset survived. Provide clear instructions for refreshing local clones, and retain the pre-migration recovery material until the team has checked its important workflows.

Budget for retrieval and recovery

Storage policies should include how often assets are downloaded and how long older objects remain available. Check your actual host's supported limits, billing terms, and retention behavior before making a project dependent on its LFS service. Avoid designing around a limit remembered from another provider or an older plan.

A repository mirror alone should not be treated as proof that the LFS payloads were preserved. Capture and verify the required LFS objects as a separate step, and test restoration without relying on an already populated development machine. The backup and security hub connects these checks to a complete recovery plan.

Give every storage location an owner. Someone must know where the authoritative objects live, how access is recovered, and which retention rule applies. An undocumented bucket or personal account can become a more serious dependency than the Git host itself.

Questions teams commonly ask

Should every image use Git LFS?

No universal rule fits every repository. Small website images may work comfortably in ordinary Git, while frequently revised design masters may justify LFS. Choose a policy based on the asset's role and the team's workflow, then measure actual checkout and build behavior.

Does an exported ZIP contain the real LFS files?

Do not assume so. Archive behavior depends on the export mechanism and host configuration. Inspect the package you intend to distribute, open representative files, and verify that it contains the expected payloads. A readable source archive and a complete development backup serve different purposes.

Can LFS replace a media CDN?

Use LFS to coordinate versioned development assets. Deliver public website media through the serving infrastructure chosen for the application. Review the large-file storage guide when separating repository inputs from downloadable products and visitor-facing content.

Make the storage decision explicit

A useful policy fits in the repository handbook: what ordinary Git tracks, what LFS tracks, where generated artifacts go, and how all required objects are restored. Apply that policy in a clean checkout and a recovery rehearsal. The goal is a project whose source, assets, and release history remain understandable to the next person who needs to build it.