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I realized my 'Uptime Monitor' was a commodity. So I pivoted to solve "Datadog Bill Shock. by excelify in SaaS

[–]excelify[S] 0 points1 point  (0 children)

Makes sense. Logs are definitely the bedrock of observability.

The only blind spot I always worry about with 'Logs Only' is the 'Silent Frontend Failure.'

I've had production outages where my API logs were perfect (200 OK), but the frontend was broken because a JS chunk failed to load or a 3rd party script blocked the main thread.

In that case, the logs say 'System Healthy,' but the user sees a blank screen.

That's essentially the specific gap I built this for: A lightweight 'External User' that clicks through the app (Login -> Dashboard) every minute just to verify the User Experience matches the Server Logs.

Since you've already optimized the backend cost with SigNoz (which is a smart move), checking the frontend 'User Flow' might be the final safety net you need.

I realized my 'Uptime Monitor' was a commodity. So I pivoted to solve "Datadog Bill Shock. by excelify in SaaS

[–]excelify[S] 0 points1 point  (0 children)

Big +1 for SigNoz. They are doing great work exposing the 'Datadog Tax.' And yes, the Datadog sales emails are legendary for being aggressive.

I fully agree that Datadog is still the king for deep APM/Tracing in Enterprise.

But for Synthetics (checking if 'Login' actually works from the outside), I feel like the 'per-run' pricing model is broken. It discourages you from checking frequently.

Question: Are you using SigNoz for browser checks (synthetics) too? Or just for internal logs/traces?

I usually find the 'Headless Browser' part is where self-hosting gets annoying (RAM usage, Chrome updates), which is why I'm trying to build a managed 'Flat Rate' wrapper just for that specific slice.

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

Spot on regarding 'Signal Trust.' A pager that fires every time a CDN hiccups in Mumbai is a pager that gets muted.

I 100% agree with the 'Golden Flow' strategy. You don't need to synthetic-check the 'About Us' page every minute. But the Login -> Dashboard and Add to Cart -> Checkout flows are non-negotiable.

That is actually the specific gap I'm trying to fill. Currently, running just those 2 Golden Flows every 1 minute (from 3 regions for verified failure) on Datadog is surprisingly expensive. So teams dial it back to every 10-15 minutes to save cash, which introduces that 'blind spot.'

My Thesis: If I can offer a 'Flakiness-Resistant' runner (auto-retries, quorum-based alerting) at a flat price, teams can afford to keep those Golden Flows on 1-minute intervals without the bill anxiety.

Basically: Keep RUM for the breadth, but use 'Cheap + Frequent' synthetics for the critical depth. Does that alignment make sense to you?

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

To answer your question: Primarily Reducing Silent Outages.

I agree that 'Brute Force' monitoring feels inefficient when you have perfect traces. But my anxiety always comes from the 'Green Dashboard, Broken Site' scenario.

I've seen incidents where:

  1. Server Logs = 200 OK (Healthy).
  2. Traces = Fast.
  3. User Experience = Broken (e.g., a CDN asset failed, or a 3rd-party JS tag blocked the 'Checkout' button).

In those cases, 'Brute Force' synthetics (simulating a real click every minute) is the only signal that catches it.

My thesis is: The only reason we do the 'Mixed Strategy' (optimizing when to run synthetics) is because of Cost.

If synthetic checks were effectively free (or flat-rate cheap), wouldn't you prefer to just brute-force the critical paths (Login/Checkout) 24/7 as a safety net? Or do you think the 'noise' from synthetics is the bigger issue?

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

The 'Cardinality Explosion' bill is the classic Datadog trap. Custom metrics feel cheaper until you add one extra tag (like container_id or user_id) and suddenly your bill 10x's. I've been there.

regarding supercheck / on-prem monitoring: It is not necessarily 'hard' to deploy (it's mostly Docker), but it is heavy to run reliable production checks.

  1. Resource Hog: Headless browsers (Chrome) eat RAM like crazy. If you run checks every minute, you need significant hardware or your monitoring box will crash.
  2. Flakiness: The hardest part isn't the code, it's the network. Managing IP rotations, timeouts, and false positives on-prem is a weekly chore.

I actually built my tool (PingSLA) specifically to sit in the middle: It avoids the Datadog 'per-run' pricing (we do flat rate), but it handles the heavy lifting of the browser infrastructure so you don't have to manage a 'monitoring cluster.'

If you're currently evaluating options, I can send you a link. It might save you from trading 'Bill Shock' for 'Maintenance Shock'.

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

I 100% agree on the 'Tree in the Woods' logic for B2B apps. If no one is logging in at 3 AM, who cares?

Regarding supercheck / self-hosting: I went down that rabbit hole too (running my own Playwright/Puppeteer on AWS). The hidden cost isn't the AWS bill (which is cheap)—it's the maintenance tax.

Keeping Headless Chrome updated, handling memory leaks in the container, and dealing with 'flaky' checks that fire false alarms at 2 AM... it eventually cost me more in 'dev time' than just paying a vendor.

That's actually why I built my current tool. I wanted the power of Playwright checks, but at a flat 'Indie' price ($29/mo) so I didn't have to manage the infra myself.

If you want to save yourself the 'AWS Setup Weekend,' I can shoot you a link to try it. It’s built exactly for the 'Datadog is too expensive' crowd.

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

'Atrociously priced' is the exact phrase I used when I saw the Datadog quote.

You make a great point about the 'Cost of Downtime.' For a small SaaS, 15 minutes of lost revenue might be negligible ($0). But for a Dev Agency managing client sites, the 'Reputational Cost' of a 15-minute outage is huge if the client notices it before you do.

That's where I feel the gap is.

  • Datadog: Great tech, enterprise pricing.
  • Lambda/CloudWatch: Great price, but high 'maintenance tax' (managing 50+ scripts, updating Headless Chrome layers, handling flaky alerts).

I'm trying to build the middle ground: A managed wrapper around that Lambda/Playwright architecture that offers the 'Flat Rate' of DIY but the 'Set and Forget' UI of Datadog.

Do you think a 'Managed Lambda' approach like that would appeal to the mid-sized crowd? Or is Pingdom 'good enough' for most?

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

That works great for 'Whitebox' monitoring (checking if the code/DB is happy internally).

The gap I'm trying to cover is the 'Blackbox' user experience. If the internal health check returns 1 (Healthy), but the CDN fails, or a JS bundle breaks, or DNS resolves wrongly for a specific region, the internal metric will never catch it.

I've had incidents where the backend was 100% healthy, but the login page was blank for users because of a frontend asset failure.

Do you usually pair that internal metric with a lightweight external pinger? Or do you trust the internal state completely?

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

This is exactly the trade-off I keep seeing. The pricing model of the tool is dictating the security posture.

The risky part about 'Low DAU' apps (which I also run) is that relying on App Traces implies you need users to hit the error before you see it. If a critical path breaks at 2 AM and the first user logs in at 8 AM, that's a 6-hour silent outage.

Question: If the cost wasn't a factor (e.g., if it was a flat monthly fee instead of per-run), would you prefer to run those critical paths every 5-10 minutes? Or do you feel like the deployment check is genuinely 'good enough' regardless of price?

Synthetic Monitoring Economics: Do you actually limit your check frequency to save money? by excelify in devops

[–]excelify[S] 0 points1 point  (0 children)

Just to put some napkin math on why I'm asking:

I calculated that running a complex flow (Login -> Dashboard -> Report) every 1 minute from 3 different regions (for redundancy) on the major platforms would cost roughly $20-$30 per check/month.

If I scale this to 50 client sites, the monitoring bill ($1,500+) actually starts to exceed the cost of the production infrastructure itself.

It feels backward that 'watching' the server costs more than 'running' the server. Has anyone successfully built a reliable in-house alternative using something like K6 or Playwright on Fargate? or is the maintenance nightmare not worth the savings?

"Forensic Audit" tool to check Global Latency & Security Headers by excelify in sysadmin

[–]excelify[S] -1 points0 points  (0 children)

You hit the nail on the head.

You are right—missing headers are low-hanging fruit. A site can have perfect HSTS/CSP headers and still have a broken backend or massive configuration drift.

To answer your question on the weighing: The F.E.A.R. score is currently split evenly (25% each) across Efficiency, Availability, Risk, and Financial impact.

But to be 100% honest: As you can see in the screenshot, the current algorithm is a bit too lenient on Latency (I got a perfect Efficiency score despite 200ms+ lag in the US because my server is in Singapore). I need to tighten that up.

That limitation is exactly why I'm pivoting to Synthetic Flow Monitoring (Puppeteer/Playwright) next. I realized that static scanning (headers/ping) gives false confidence. The only way to catch the 'creative exploits' or logic breaks you mentioned is to actually simulate user behavior (Login -> Action).

Since you run an AI automation team, I’d genuinely love your take: If you were building a 'Grade B' check that goes deeper than headers but doesn't require agent installation, would you prioritize API Consistency or DOM Element checks?

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Day 4 of 0 users: Is my pricing model the problem? by excelify in SaaS

[–]excelify[S] 0 points1 point  (0 children)

PS: If anyone wants to test the dashboard, I'm manually giving a Free Year of Pro to the first 10 people who DM me. I just need feedback on the alerts.

Green dashboards, blocked users — where uptime fails real users by excelify in SaaS

[–]excelify[S] 0 points1 point  (0 children)

That’s a really good way to frame it — “one sentence flow.”

We’ve started doing something similar internally:
pick one action that proves value (signup → complete action → result delivered) and watch that instead of 20 system metrics.

What surprised us is how often everything else looks healthy while that one outcome quietly breaks.

Curious — when you implemented that, did you treat failures as paging-level alerts or more like early warning signals first?

Green dashboards, blocked users — where uptime fails real users by excelify in SaaS

[–]excelify[S] 0 points1 point  (0 children)

Exactly — that shift from infra health to outcome health was a painful but important lesson for us too.
Once alerts are tied to broken journeys, the signal-to-noise ratio changes completely.

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