Docker Laravel Package

Product Decisions This Supports

Feature & platform strategy

• Decides whether "containerized Laravel deploys" becomes a supported platform capability or stays a per-team/DIY concern.
• Informs the golden-path question: do we standardize one blessed Docker recipe for Laravel services, or let each squad assemble its own?
• Roadmap input for internal developer platform (IDP) work — a production-shaped reference environment is often the first artifact a platform team ships.

Build vs buy (really: build vs adopt vs fork)

• This is not a Composer dependency; it is a config/template package. The real decision is "adopt as a starting template and own the fork" versus "build our own baseline" versus "use a managed PaaS." Frame the business case against engineering weeks, not against a license fee (MIT = $0).
• The opportunity score (4.24, high) alongside a low composite score and 0 dependents tells the real story: strong demand for the problem, weak validation of this specific artifact. That pattern supports "harvest it as a reference architecture," not "standardize on it."

Use cases that justify evaluation

• Greenfield Laravel services with a conventional footprint: app server, PHP-FPM, database, cache/queue worker, scheduler.
• Teams migrating off single-VM/Forge-style deploys who need repeatable local-to-prod parity.
• Internal tools, MVPs, and cost-sensitive workloads where a full platform investment is not yet warranted.

Decisions it explicitly should not drive

• Multi-service or polyglot architectures, Kubernetes-native shops, or regulated environments needing hardened, audited, CVE-tracked base images.

When to Consider This Package

Consider it when:

• You have a small or mid-sized Laravel team without a dedicated platform/DevOps owner, and shipping deploys is the bottleneck.
• You need a production-shaped baseline now and are willing to treat the repo as a fork-and-own template rather than an upstream dependency.
• Your topology matches the described shape — one Laravel app plus supporting services — with no unusual networking, GPU, or compliance constraints.
• You can absorb the cost of an internal upgrade and patching cadence (pinned base image versions, image scanning, dependency refresh).
• You want a concrete artifact to benchmark against your own design before committing engineering time.

Look elsewhere when:

• You require an audited security posture, SBOMs, signed images, or an SLA on base-image patching — a 42-star, single-maintainer project with 0 dependents offers no community backstop and no abandonment insurance.
• You are already on a managed platform (Vapor, Cloud, Kubernetes, or a mature PaaS) — duplicating the runtime layer adds ops surface without product value.
• You need orchestration-level concerns (autoscaling, service mesh, multi-tenant isolation) — this is an environment definition, not a platform.
• You expect composer require-style upgradeability. You will not get it; changes arrive as diffs you manually reconcile.

Evaluation gate before adoption: verify maintenance recency, run the stack in a staging environment, confirm image provenance and pinning, and require a named internal owner before the first production deploy.

How to Pitch It (Stakeholders)

For executives — the 30-second version This is an MIT-licensed, ready-made Docker setup for running Laravel in production. The business case is time-to-market: it can compress what is typically weeks of environment plumbing into days, at zero license cost. The honest caveat is ownership — a 42-star project with no downstream dependents is a template, not a vendor. We adopt it the same way we'd adopt a well-written reference design: copy it, pin it, own it, and scan it. That keeps the savings while removing the abandonment risk. Recommendation: fund a short, time-boxed spike to validate it against one real service before it influences any platform roadmap commitment.

For engineering — what we're actually asking for

• Treat the repo as a baseline to fork into our own repo, not an upstream we track. Expect to reconcile changes by hand.
• Do the unglamorous work up front: pin base image tags and digests, add image scanning to CI, define a review cadence for base-image bumps, and document the config surface so it's supportable by someone who didn't write it.
• Benchmark it against our current deploy path on three axes — setup hours, deploy frequency, and mean time to restore — and bring back numbers. If it doesn't beat the status quo on at least two, we stop and build the golden path ourselves with these findings as input.

The one-line framing: high market demand for this problem, low validation of this specific solution — so we borrow the design, not the dependency.