Yes, you can build it.
Here's what that actually costs.
SQL queries, a scheduler, and a Slack webhook is a real solution — whether you wrap it in a script or a simple UI. A lot of teams start there. This page is an honest look at what you're actually signing up for long-term, and when it makes more sense to hand that off.
Calculate the real cost
Plug in your numbers. See the true cost over time.
Lighthouse
Plan
Users
Your team
Initial build
Days to ship v1 (typically 4–6 weeks for a real system)
Monthly upkeep
Days/month to tune, fix, and extend
Monthly infra
Scheduler, DB, cloud compute to run the monitoring system
Monthly salary
Engineer's gross monthly pay
Employer overhead
Benefits, payroll tax — typically 25–35%
Total cost of ownership
Build in-house
$67.5k
over 3 years
Lighthouse
$5.3k
over 3 years
92% less than building in-house over 3 years.
$1.2k/mo cheaper to run
Lighthouse ongoing cost vs engineer time + infra to maintain the build system — every month, forever.
Cumulative cost over time
Lighthouse pro defaults to $49/user/mo. Build cost uses a $591/day rate (30% overhead on $10,000/mo salary) plus $200/mo in cloud infra. Does not include opportunity cost of engineering time or feature requests gated on the data team.
The realistic arc of a home-built monitoring stack
It works. Fast.
You write a SQL query, hook it to a scheduler, wire a Slack notification. Done in a day — or you ship a simple UI around it. Either way, this is genuinely the easy part.
Alert fatigue sets in.
Your static threshold fires every Monday morning because Monday is always slower than Friday. The team starts ignoring the alerts. You spend a weekend tuning numbers that make sense for this week but break next month.
The requests start.
Marketing wants a metric added. The CEO asks why a specific segment isn't being tracked. A new data source gets connected and the query doesn't cover it. Each change is a ticket to the data engineer.
The system owns you.
There are 30 metrics, each a SQL query someone wrote months ago. Nobody remembers why half the thresholds are what they are. A schema change breaks three checks silently. The alerts have been so noisy that a real issue went unnoticed for two days.
More users, zero visibility.
The analyst team grew. Now five people want access — but your script has no permissions model. Everyone sees everything, or no one does. You can't tell who acknowledged an alert, which team owns which metric, or why a threshold was changed last Tuesday. The system was built for one engineer. It was never built for a team.
You built v1. Now it's becoming a second job.
Most teams who find this page aren't starting from scratch — they already have SQL queries running on a scheduler, a Slack channel getting noisy, and a backlog of metric requests they haven't gotten to. Sound familiar?
Your static thresholds fire on weekends and holidays, and the team has started ignoring them
Every new metric request means a data engineer writes SQL, tests it, and wires it up manually
You have no idea who acknowledged the last alert, or whether anyone did
The system works — but you spend real hours every month keeping it alive
Migrating to Lighthouse doesn't mean starting over. You connect your warehouse, recreate your existing metrics in plain English in minutes, and get everything you built — plus adaptive thresholds, permissions, audit logs, and self-serve for your analysts — without maintaining any of it.
Build vs Lighthouse
Across the dimensions that actually matter long-term.
Lighthouse
Build in-house
When building genuinely makes sense
We're not going to pretend there's never a good reason to build. Here's when there actually is.
Your metrics are stable and unchanging
If you have 3 metrics that never change and are happy tuning thresholds manually, a script might be fine forever.
You have a genuinely unique monitoring need
If your metrics require proprietary logic that no off-the-shelf tool could support, building makes sense.
Full infrastructure control is a hard requirement
Some compliance environments or highly sensitive data situations require everything to run in your own infrastructure.
For most teams — especially small data teams supporting many stakeholders — none of these apply. The honest question isn't whether you can build it. It's whether building it is the best use of your engineering time.
What Lighthouse handles for you
The hard parts of a home-built system — solved.
Adaptive thresholds that learn your data
No more manually tuning static numbers. Lighthouse learns day-of-week patterns, seasonality, and normal variance. You get fewer false positives without any configuration.
Business users can self-serve
Any analyst on your team can create, edit, and manage their own metrics in plain English — without writing a line of code or filing a ticket to the data team.
Meaningful context in every alert
Each Slack alert includes the metric trend, direction of change, and historical context. Your team immediately knows whether this requires action or is within normal range.
Zero maintenance burden
No SQL queries to maintain when schemas change. No thresholds to re-tune after a holiday. No scheduler to babysit. You connect your warehouse and Lighthouse handles the rest.
Governance built in
Every alert run is logged — segment values, SQL text, who acknowledged what. Metrics carry severity levels, owners, and access controls. When a teammate asks why an alert fired three weeks ago, the answer is there.
Fine-grained ReBAC permissions
Control exactly who can see each data source, dataset, and metric. Every team works in a scoped view — analysts see only what they own. Building this level of relationship-based access control from scratch is a multi-week project with ongoing maintenance.
Domain-specialized AI — not a generic prompt
Lighthouse's AI is purpose-built for metric monitoring. It understands business KPI patterns, seasonality, and anomaly context. Wiring a general LLM to your warehouse gets you 20% of the way there — the domain-tuning and guardrails are what you'd spend months building.
See what 10 minutes gets you.
Connect your warehouse, describe your first metric in plain English, and get a Slack alert before you finish your coffee. No SQL required.
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