Laravel Model Stats Laravel Package

Product Decisions This Supports

• Build vs. buy for internal analytics. For an early-stage Laravel product, the first version of an "admin dashboard" (how many accounts, projects, orders, or records were created this week vs. last) is a classic distraction — days of Eloquent aggregation queries, chart wiring, timezone/date-bucketing bugs, and cache invalidation. This package is a candidate to ship that view in hours, freeing the roadmap for customer-facing work.
• Activation and growth tracking without a full analytics stack. Model creation trends (new users, new projects, new orders) give a coarse but immediate proxy for signup and core-action velocity. Useful for answering "is the top of the funnel moving?" before the team is ready to commit to an event pipeline.
• Churn and destructive-action signals. Update and delete counts surface data-loss behavior, account deletions, and record-editing patterns — inputs to retention hypotheses and to support/escalation tooling priorities.
• Capacity and infrastructure planning. Entity growth rates per model inform database sizing, archiving policy, and when to introduce partitioning or read replicas. This converts a vague "we should probably scale" conversation into a charted trend line.
• Roadmap prioritization by domain. Seeing which entities compound fastest tells you where users actually invest effort. If projects grow 10x faster than invoices, that order of investment is defensible in a roadmap review.
• Data-quality and migration validation. Creation/deletion spikes or gaps right after a backfill, seed, or schema migration are an early warning that something wrote (or didn't write) unexpectedly.
• Feature-adoption sanity checks. For a product whose core objects map to features, per-model volume is a cheap way to detect that a launched feature is actually being used before instrumenting it properly.

When to Consider This Package

Adopt when:

• The application is Laravel with Eloquent-based domain models and a reasonably conventional schema.
• The need is internal visibility for the team or admins — not customer-facing reporting, and not user-level behavioral analytics.
• You need something shipped this sprint. Cost is near zero (MIT), and the surface area is small enough for a spike to fully de-risk it in under a day.
• You accept that value accrues forward-only: trend data starts when tracking begins, so this is not a historical-analysis tool unless you backfill.
• You want a stopgap that is easy to rip out later in favor of PostHog, Metabase, or a warehouse — the exit cost is low, which is a legitimate reason to say yes.

Look elsewhere when:

• You need funnels, cohorts, retention curves, segmentation, or per-user event streams. That is a product-analytics problem; use a product-analytics tool. This package reports on models, not people.
• You need BI-grade querying, joins across sources, or self-serve exploration for non-engineers — a warehouse plus Metabase/Looker is the right shape.
• You are at a scale where per-write event hooks are a measurable cost, or where multi-tenancy makes a global dashboard meaningless. Instrument your write path deliberately instead.
• You require vendor support, an SLA, or guaranteed framework-version currency. Stars and activity are modest and the last release is 2023 — treat this as community-maintained code you may need to own.
• Your stack isn't Laravel, or models are heavily customized such that event coverage would be incomplete and the numbers misleading.

Mitigations if you proceed: pin the version, confirm it works against your current Laravel/PHP version in a branch, verify the migration and storage footprint, and confirm whether tracking is synchronous on writes. Assume you may fork it. Budget a half-day for evaluation and a small ongoing maintenance allowance.

How to Pitch It (Stakeholders)

For executives — the one-liner: "A free, MIT-licensed dashboard that shows how fast each core object in our product is being created, updated, and deleted — we can have it live this week instead of spending an engineering sprint building it ourselves, and we can replace it later without rework."

Frame the value as decision velocity at near-zero cost: it answers recurring questions ("are signups growing?", "are accounts deleting records?", "which part of the product is compounding?") that today cost ad-hoc engineering time or go unanswered. Emphasize the low commitment: no vendor contract, no lock-in, low switching cost, and it buys time to decide whether a real analytics investment is justified.

For engineering — the case and the caveats: it's a small, self-contained Laravel package that observes Eloquent model events and renders an in-app dashboard — no external service, no new infrastructure. Integration should be a composer require, a migration, and a config publish; scope the spike as a timeboxed branch and confirm three things before committing: compatibility with our current Laravel/PHP versions, the write-path overhead of event tracking on high-volume models, and the storage/migration footprint. Flag the maintenance risk honestly — last release mid-2023 and a small maintainer base — and agree that we pin the version, exclude noisy models from tracking, and treat a fork as an acceptable fallback. The ask is one engineer for roughly a day, not a quarter's commitment.