Why I'm Building an API Monitoring Tool That Tells You Why Things Broke — Not Just That They Did

Monitoring tells you something broke. Pinghawk tells you why.

The problem I kept running into

While exploring monitoring tools for a side project, something kept bothering me.

Most monitoring tools are great at telling you when something breaks.

But they rarely help you understand why it broke.

You get an alert like this:

Your API is down.

Endpoint: api.myapp.io/payments
Status: 503

And then what?

You still have to investigate the root cause yourself.

The usual debugging workflow

Most developers I've talked to describe a very similar process:

1. Get the alert
2. SSH into the server
3. Check logs
4. Run curl manually
5. Try to reproduce the failure

The frustrating part is that by the time you investigate, the issue often no longer exists.

It might have been:

• a DNS lookup delay
• a temporary database overload
• a TLS handshake issue
• a short network timeout
• a container restart

If the incident lasted only a few seconds, the debugging context may already be gone.

So you end up debugging without the moment of failure itself.

What if monitoring captured the evidence automatically?

That's the idea I've been exploring with Pinghawk.

Instead of only sending an alert, the system captures a debugging snapshot at the exact moment a request fails.

Things like:

• DNS lookup timing
• TLS handshake duration
• Time to first byte
• First part of the response body (which often contains the real error)
• Which region detected the failure first

Snapshots also come from multiple regions, which helps distinguish between a local network issue and a global service failure.

I've been calling this feature Hawk Mode 🦅.

The goal is simple:

When the alert arrives, you already have clues about what likely broke.

What a Hawk Mode snapshot looks like

🦅 HAWK MODE CAPTURE — 14:03:47 UTC

Region:             ap-south (Mumbai)
DNS lookup:         340ms   ← abnormally high
TLS handshake:      48ms
Time to first byte: 28,400ms  ← critical
Status code:        503
Response body:      {"error": "db pool exhausted"}

A second region detects the same failure shortly after:

🦅 HAWK MODE CAPTURE — 14:04:17 UTC

Region:             us-east (Virginia)
DNS lookup:         12ms    ← normal
TLS handshake:      45ms
Time to first byte: 30,000ms  ← critical
Response body:      {"error": "db pool exhausted"}

From these two snapshots alone you can quickly see:

• DNS is working globally — not a DNS issue
• It's not a regional outage — both regions affected
• The response body already hints at the cause

Database connection pool exhausted.

No SSH session required.

Reducing noisy alerts

Another small design decision I'm experimenting with:

Pinghawk doesn't alert on the first failure.

Instead, it waits for three consecutive failed checks before triggering an alert.

Check 1 fails → snapshot #1 captured silently
Check 2 fails → snapshot #2 captured silently
Check 3 fails → snapshot #3 captured + alert sent with all three

This avoids the classic situation where a server briefly restarts and your monitoring wakes you up at 3am for something that already fixed itself.

The result:

• fewer false alarms
• a progression timeline of the failure
• debugging data captured before the issue disappears

Keeping setup simple

Another goal with Pinghawk is keeping the setup extremely lightweight.

The approach is intentionally minimal:

• paste an endpoint URL
• choose a check interval
• start monitoring immediately

No agents to install.
No SDKs.
No configuration files.

Just something that works in under 60 seconds.

Tech stack (so far)

The current architecture I'm experimenting with:

• Node.js for the API
• PostgreSQL for storage
• BullMQ for scheduled monitoring jobs
• Cloudflare Workers for global multi-region checks

Still early — this may evolve as the system grows.

Where things currently stand

Pinghawk is pre-MVP and being built in public.

I recently finished the landing page and started collecting early feedback while building.

V1 (currently building):

• HTTP endpoint monitoring
• Hawk Mode debug snapshots
• Email and Slack alerts
• Public status pages

Coming later:

• Smart API response validation
• Developer CLI
• Custom domain status pages
• Synthetic workflow testing
• GitHub integration

A small personal note

This is actually my first attempt at building a SaaS product from scratch.

I'm building Pinghawk in public partly to stay accountable, and partly because feedback from other developers helps shape the product early.

If you've dealt with debugging production failures before, I'd really love to hear how you approach it.

Quick poll for backend developers

When an API fails in production, what is the first thing you usually check?

• A) Application logs
• B) Infrastructure metrics
• C) Tracing / APM tools
• D) Reproduce the request with curl
• E) Something else

Curious what the most common workflow actually is.

I'd love your thoughts

I'm still early and trying to understand what would actually help developers the most.

A few things I'm curious about:

• When your API fails in production, how do you usually debug it?
• Would automatic failure snapshots actually save you time?
• What's missing from your current monitoring setup?

If you're curious about the idea:

👉 https://pinghawk.io

But more importantly — I'd really love to hear how others approach debugging production failures.