Buy vs Build in the Age of AI (Part 2)

The Real Cost of Owning Monitoring Isn’t Code — It’s Everything Else

In Part 1, we explored how AI has dramatically reduced the cost of building monitoring tooling. That much is clear. You can scaffold uptime checks quickly, generate alert logic in minutes, and set-up dashboards faster than most teams used to schedule the kickoff meeting.

So the barriers to entry have fallen. But there’s a quieter question that rarely gets asked in the excitement of building. Have you ever calculated what it would actually cost to replace your monitoring provider? Not in lines of code, but in terms of infrastructure, attention, engineering hours, and in long-term responsibility. Arguably the code is the “cheap” part.

Start With the Obvious Number

Most monitoring platforms cost somewhere between $25 and $400 per month, depending on scale and feature set. When teams look at that number, the calculation often feels simple:

“We’re paying $3,000–$5,000 a year for this.”

With AI making internal builds “easier” than ever, it’s tempting to think:

“Why don’t we just build it and remove that line item?”

On the surface, it looks like an easy saving. But that monthly subscription isn’t just a feature fee. It represents infrastructure, redundancy, maintenance, protocol updates, alert delivery systems, and ongoing operational attention. And once you start unpacking those components, the savings become less obvious.

You’re not buying “a ping check.” You’re buying:

• a distributed global check network;
• redundant infrastructure across regions;
• alert dispatching systems (email, SMS, webhooks, integrations);
• historical uptime data storage;
• TLS and certificate monitoring logic;
• DNS resolution handling;
• ongoing security patching; and
• continuous availability of the monitoring platform itself.

Even modest global infrastructure is not free. SMS alerts cost money. Multi-region redundancy costs money. Data retention costs money. Network egress costs money. And that’s just the infrastructure.

Now Add Engineering Time

Let’s run conservative numbers. A senior engineer in the U.S., fully loaded (salary, benefits, overhead), typically costs $140,000–$180,000 per year. That translates to roughly $75–$90 per hour.

Now assume your team spends a modest amount of time maintaining an internal monitoring stack:

• 6 hours/month adjusting alert thresholds
• 6 hours/month reviewing changes to services and endpoints
• 4 hours/month managing dependencies or infrastructure updates
• 4 hours/month investigating monitoring-related anomalies

That’s 20 hours per month. At even $80/hour, that’s $1,600 per month in engineering time.

All of this is before infrastructure, before SMS delivery costs, before global probe replication, and more critically of all – and often overlooked – before opportunity cost.
That’s already 4x the cost of a $400/month monitoring plan.

And most teams underestimate how much time monitoring quietly absorbs, because the work is distributed and rarely labelled “monitoring maintenance.” It just gets done between other priorities.

The Cost You Don’t See: Opportunity

But direct cost isn’t the most important number. Opportunity cost is. If you’re a logistics platform, a fintech company, a SaaS billing provider, a healthcare application, or e-commerce business your competitive advantage is not uptime monitoring.

Every hour spent updating TLS validation logic, adjusting DNS behaviour, managing certificate expiry edge cases, debugging regional connectivity quirks, tuning timeout thresholds, reviewing protocol changes and so on, is an hour not spent improving your core product.

So whilst AI reduces development friction, it does not eliminate opportunity cost. And opportunity cost compounds. You may not notice it in a sprint, but you’ll notice it in your roadmap.

Monitoring Is Not Static Infrastructure

There’s a misconception that monitoring is something you build once and leave alone. It isn’t.

The web evolves constantly. TLS versions are deprecated. Cipher suites are retired. HTTP standards evolve. CDNs introduce new routing behaviours. IPv6 adoption increases. Browsers enforce new security expectations.

Think about how many organisations were caught off guard by:

• early TLS deprecations;
• certificate expiration automation failures;
• SHA-1 retirement;
• HTTP/2 proxy quirks; or
• DNS resolution inconsistencies across regions.

Monitoring systems must evolve in lockstep with the internet itself. If you build internally, that responsibility becomes yours. You must track protocol changes. It’s you who must update validation logic, and ensure your checks reflect modern standards.

That’s not a one-time engineering task; it’s an ongoing discipline.

When Your Site Goes Down

The real test of monitoring isn’t configuration; it’s incident response.

Imagine your primary site becomes unavailable. Your customers are complaining, leadership is asking for updates, and revenue is potentially at risk.

Your monitoring tool fires an alert.

Then what?

An internal system might tell you:

“Endpoint unreachable.”

But diagnosis is another matter.

• Was it DNS propagation?
• Was it a TLS handshake failure?
• Was it a regional routing issue?
• Was it a certificate mismatch?
• Was it origin latency behind a CDN?
• Was it a transient ISP anomaly?

Established monitoring providers accumulate exposure to thousands of outages across customers and regions.

They observe patterns:

• Global network anomalies
• CDN misconfigurations
• Recurring TLS issues
• ISP-level disruptions
• Regional latency spikes

That pattern recognition becomes embedded in their tooling and support processes. When monitoring is your core business, diagnosis support is not secondary. When monitoring is an internal utility, it often is.

Cheap Tools, Fragile Economics

As AI lowers the barrier to building software, the monitoring landscape is becoming more crowded. We’re seeing in new AI tools generous free tiers, extremely low-cost plans, and AI-driven analysis bundled into entry pricing.

Competition is healthy. It drives innovation and pushes everyone in the market to improve – including here at StatusCake. But as we’ve seen monitoring has hard, unavoidable operational costs such as:

• distributed probe infrastructure;
• redundant networks;
• data storage;
• alert delivery fees;
• security patching; and
• ongoing engineering attention.

Those costs don’t disappear simply because development has become easier. If a monitoring platform costs $5 per month, it’s reasonable to ask what assumptions underpin that price.

• Is it subsidized by another product?
• Is it early-stage and not yet operating at scale?
• Is it dependent on venture funding?
• Is it a strategic experiment rather than a core focus?

We’ve already seen how this can play out.

Freshworks’ Freshping launched with generous free monitoring and gained significant adoption. But over time, it was deprioritized and ultimately closed. For customers who had embedded it deeply into their workflows, that shift wasn’t theoretical — it requires rebuilding checks, migrating alert logic, and rethinking operational processes.

There was nothing malicious in Freshwork’s decision. It was simply a business call. But it highlights an important point. Monitoring is deeply embedded infrastructure.
If pricing shifts dramatically or a product is discontinued, migration is not trivial. Checks must be rebuilt. Historical data may not transfer. Alert logic must be revalidated. On-call playbooks must be rewritten.

Economic fragility at the vendor level can quickly become operational fragility at the customer level. And when the system responsible for telling you your site is down becomes uncertain, that uncertainty carries real risk.

The Attention Problem

When teams choose to build and operate their own monitoring stack, the deepest cost is rarely financial. It’s attention. Owning monitoring internally requires:

• Continuous tuning
• Redundancy testing
• Dependency updates
• Security hygiene
• Alert fatigue management
• Integration upkeep

None of this work is glamorous, none of it shows up in product demos; and none of it directly drives revenue. As such it competes — constantly — with product delivery for engineering time. And product work usually wins.

Monitoring maintenance becomes background noise. Teams find that thresholds aren’t revisited. Dependencies aren’t reviewed as often as they should be, and redundancy assumptions go untested.

Until an outage exposes the gap.

Monitoring’s success is invisibility. If it works, nobody talks about it. If it fails, everyone does.

When you build internally, that dynamic can lead to chronic underinvestment. Not because teams are careless, but because monitoring is rarely the core differentiator.

When you choose a specialist provider, much of that attention burden shifts to a team whose sole job is to think about monitoring every day. That difference in focus compounds over time.

A Specialist Discipline

Monitoring may look simple at the surface.

Check availability.
Send alert.

But beneath that simplicity sits:

• Network engineering
• Protocol awareness
• Global redundancy
• Security sensitivity
• Infrastructure resilience
• Incident support
• Long-term governance

AI can accelerate feature creation, but it cannot replace accumulated operational discipline.

Established monitoring providers — whether StatusCake, Uptime Robot, or other long-standing platforms — exist because this discipline is not trivial.

Their business depends on:

• Maintaining global probe infrastructure
• Keeping pace with protocol evolution
• Ensuring alert reliability
• Supporting customers during real incidents
• Sustaining economic durability

That focus is difficult to replicate internally; not because engineers aren’t capable, but because monitoring is rarely their primary mission.

The Real Question

In the AI era, the question isn’t:

“Can we build this?”

Of course you can.

The question is:

“Should we own this layer of our infrastructure?”

Should you own:

• The global infrastructure?
• The redundancy model?
• The protocol updates?
• The alert delivery reliability?
• The dependency management?
• The diagnostic responsibility?
• The sustained engineering attention?

Or should that responsibility sit with a specialist whose core job is to think about monitoring every day?

AI has reduced the cost of writing monitoring code.

It has not reduced the cost of carrying monitoring responsibility.

And responsibility is what matters when systems fail.

In Part 3, we’ll examine what happens when AI moves beyond assisting development and begins contributing autonomously to ecosystems; and why that introduces new trust boundaries for monitoring and observability.

For now, one calculation is worth doing honestly:

If your monitoring plan costs $100–$400 per month, and your internal engineering time costs $1,600+ per month, before infrastructure…

Are you really saving money?

Or are you just reallocating responsibility?