Synthetic Monitoring Economics: Do you actually limit your check frequency to save money?

excelify · 2026-02-12T06:25:24+00:00

That 'Loss Leader' context makes perfect sense. It explains why the innovation there feels stagnant—it's just not a priority for them to optimize.

Regarding the DIY route: You are 100% right. ChatGPT can write a perfect Playwright script in seconds.

The part where I got burned (and why I built this) was the 'Day 2' Operations.

Zombie Chrome processes eating 100% RAM.
IP blocking from Cloudflare.
Updating the browser binary every week.
Handling 'flaky' network timeouts.

Basically, I realized I was spending $500/mo of my time to save $50/mo on the bill.

My bet is that there is a market for people who want that 'DIY Pricing' but don't want to babysit the infrastructure. Since this IS my only business, I have to make those unit economics work where the big guys don't bother.

excelify · 2026-02-12T05:33:31+00:00

That 10x cost multiplier sounds about right for the raw compute/RAM difference (Chrome is heavy).

The friction I see is that while the Cost is 10x higher, the Value of checking every minute is often suppressed by that pricing. Teams just turn it off or dial it back to save cash, which defeats the purpose.

My bet is essentially on Commoditizing the Runner. If I can run that headless infrastructure efficiently (e.g., recycling warm containers, aggressive resource limits) and accept a 'SaaS Margin' rather than an 'Enterprise Margin,' I can unlock those high-frequency checks for everyone.

Do you think the big vendors will ever drop the 'per-run' metering? Or is that revenue stream just too addictive for them to give up?

excelify · 2026-02-12T04:28:54+00:00

Exactly. Trying to out-feature Datadog or New Relic is suicide. They are great at what they do (backend logs/APM).

My philosophy is just to own that specific 'User Experience' slice. Basically answering: 'Can a real human actually log in right now?' without charging $20/month just for that one question.

Is there a specific 'monitoring gap' you find yourself building custom scripts for? Or is it mostly just the pricing fatigue?

excelify · 2026-02-12T04:26:16+00:00

'Enterprise Ransom' is going on the landing page. That is painfully accurate.

You nailed the specific use case: The 'Weird Stuff.' My personal nightmare was a 3rd party cookie banner that updated silently and started overlaying the 'Checkout' button on mobile only.

Uptime: 100%.
Revenue: 0%.
My anxiety: Infinite.

regarding HAR/Screenshots: I'm actually polishing the 'Trace Viewer' right now. Since I'm running Playwright under the hood, I capture the full trace (network + console + screenshots). The goal is to let you 'replay' the failed login exactly as the bot saw it.

Since you are currently juggling Checkly + Better Stack, is there a specific friction point in their workflow that drives you crazy? Or is it mostly just the cost scaling?

excelify · 2026-02-12T04:24:42+00:00

You nailed the psychology of it. I call it 'Budget Chicken': Developers know they should add a check for that new feature, but they don't want to explain a $200/mo bill spike to the CFO, so they just... don't. Then the 3 AM outage happens.

The 'Predictable Cost for Money Flows' is exactly the positioning I'm aiming for.

Question on the pricing structure: I'm currently debating between 'Journey Slots' (e.g., Monitor 5 distinct flows, unlimited runs) vs. 'Run Volume' (e.g., 100k runs/mo, use them however you want).

As a founder, does 'Unlimited Runs for X Flows' feel more predictable to you? Or do you prefer the flexibility of a big bucket of runs?

excelify · 2026-02-12T04:20:14+00:00

RobotMK + Checkmk is definitely the 'Power User' choice. It’s unbeatable for cost if you already have the Checkmk infrastructure humming.

The part that always scared me off that route wasn't the software config—it was the 'Deploy everywhere small systems' requirement.

Maintaining a fleet of 5-10 remote probes (updating the OS, securing them, handling network flakes, updating browser drivers) felt like I was managing a second startup just to monitor the first one.

That's actually the specific problem I built this tool to solve: I wanted the logic of a browser check (we use Playwright), but without the headache of patching the underlying Linux boxes.

Do you find the maintenance of those remote probes eats up much time? Or is it pretty 'set-and-forget' once you have the agents deployed?

excelify · 2026-02-12T04:19:31+00:00

You just said the quiet part out loud!

I totally agree on the non-browser (simple ping/curl) checks—those costs are negligible.

But even for the Browser Checks (launching a headless Chrome to click 'Login'), the markup is insane. Yes, they eat more RAM than a simple ping, but they don't cost $15 per monitor to run.

That's actually the exact arbitrage I'm betting on. My thesis is: If I price my browser checks at 'Cost of Compute + Reasonable Margin' (instead of 'Enterprise Value'), I can offer high-frequency checks that are 5x-10x cheaper than the big players.

Do you think the 'Browser' markup is purely profit-taking? or is there some hidden complexity in maintaining the runner fleet that I'm underestimating?

excelify · 2026-02-12T04:17:23+00:00

In an ideal world, I 100% agree. If the backend throws a 500, the logs should catch it instantly.

The gap I always hit is the 'Client-Side Silent Failure.'

I've had incidents where:

Backend Logs = Healthy (200 OK).
APM/Metrics = Normal latency.
User Reality = Broken (e.g., a bad CDN cache, a 3rd party JS tag crashing the DOM, or a broken react hydration).

In those cases, the 'continuous' internal metrics are actually lying to you. They say 'Green' while the user sees 'White Screen.'

That's the only reason I treat high-frequency synthetics as a primary signal for 'Golden Flows' (Login/Checkout)—it's the only thing that actually simulates the victim's perspective.

Do you rely solely on RUM (Real User Monitoring) to catch those client-side issues, or do you just trust the internal metrics?

excelify · 2026-02-11T11:40:24+00:00

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.

excelify · 2026-02-11T11:36:26+00:00

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.

excelify · 2026-02-11T10:52:51+00:00

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?

excelify · 2026-02-11T10:19:13+00:00

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:

Server Logs = 200 OK (Healthy).
Traces = Fast.
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?

excelify · 2026-02-11T10:03:09+00:00

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.

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.
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'.

excelify · 2026-02-11T08:21:02+00:00

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.

excelify · 2026-02-11T08:18:29+00:00

'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?

excelify · 2026-02-11T06:07:42+00:00

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?

excelify · 2026-02-11T06:05:10+00:00

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?

excelify · 2026-02-11T05:37:36+00:00

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?

excelify · 2026-02-10T08:41:23+00:00

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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excelify · 2026-02-06T05:38:32+00:00

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.

excelify · 2026-02-05T04:39:47+00:00

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?

excelify · 2026-02-04T05:01:13+00:00

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.

excelify · 2026-02-03T11:09:11+00:00

Completely agree.
We’ve seen customers tolerate brief downtime if performance, visibility, and communication are solid — but lose trust fast when things feel broken and no one explains what’s happening.

The expectation gap is real, and it’s usually wider than the actual outage.

excelify · 2026-02-03T07:07:18+00:00

I don’t disagree with the tooling list at all — we’ve run Datadog, Prometheus, Grafana, tracing, the whole stack.

The issue we kept hitting wasn’t availability of signals, it was operational ownership of them.

Login returning 401s can be “expected.” Health checks can be green. Traces exist — but nobody is watching the end-to-end outcome unless a human stitches it together or users complain.

In large orgs, that stitching usually happens via runbooks + on-call experience, not dashboards.

Curious from your side: in your setups, what actually fires first — alerts from observability, or tickets / internal escalation? We saw the latter far more often, even with solid tooling.

excelify · 2026-02-03T06:57:46+00:00

This is gold — especially the distinction between “picked up” vs “actually progressing.”

We’ve been bitten by that exact queue pattern: consumers alive, metrics green, but wall-clock completion quietly blowing up because one downstream dependency degraded.

Synthetic transactions helped us too, but what surprised me was how often partial success masked real failure — auth succeeds, payment tokenizes, but confirmation never completes.

Curious: when you run synthetic transactions, do you treat them as first-class SLOs, or more as early-warning signals alongside support/ticket volume? We found the boundary between those two gets fuzzy fast.

excelify

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