TrackingPlan Alternative: What MayIA° Does That TrackingPlan Doesn't
- Published
- 6 min read
You're looking for a TrackingPlan alternative, probably for one of two reasons: either you already use it, it alerts you very well about what just broke in production — except things keep breaking in production — or you're evaluating tracking-reliability tools, TrackingPlan shows up everywhere in your research, and you're not sure it actually solves your problem.
Let's be direct: TrackingPlan has no hidden flaw to expose. It's a solid production-monitoring tool that does one specific job very well. The issue isn't that it does that job badly — it's that it only judges your tracking against what it has always done, never against what it should do. If your real pain is a tracking problem that never triggers an alert because it's simply part of the site's habit, switching monitoring vendors will never fix it: you need a tool that compares against something other than the site's own history.
TrackingPlan and MayIA° don't compare your tracking to the same reference
TrackingPlan answers one precise question: "has the tracking's behavior changed from what it usually does?" Its SDK watches real traffic continuously and builds a reference behavior from what it sees — but that reference is what the site already sends, not what the tracking plan or the web analytics team actually expect. As long as there's no drift from that history, TrackingPlan flags nothing, even if what's been sent all along never matched the spec.
MayIA° compares against something else entirely: what the tracking plan, the taxonomy, or a business change define as expected — regardless of what the site has always done. Nothing is left to chance or to whatever habit the site picked up: every event is checked against what it should be, not against what it has always been.
That difference in reference point has a second consequence, on timing: TrackingPlan needs real traffic to exist to learn what "normal" looks like, by design, so it can never, structurally, catch a regression before it reaches at least a few real users. MayIA°, by comparing against a written spec rather than a traffic history, can judge before the first visitor ever loads the page.
TrackingPlan's blind spot: what's always been wrong, and what's too new to have a history
Two concrete situations show the limit.
The chronic error. A purchase event has been sending its value as a string ("29.90") instead of a number since the checkout first launched, two years ago. That behavior has never changed: for TrackingPlan, which learns what the site does, that is precisely the definition of normal. No alert will ever fire on it, because there's nothing to detect as an anomaly — the anomaly is the starting point itself. MayIA° checks the event against the tracking plan on every run, regardless of what it has always returned, and flags it on the very first pass.
The business change. The web analytics team adds a parameter to the tracking plan for a new program, or changes the rule for an existing event. Until enough real traffic with the new behavior has accumulated for TrackingPlan to build a reference, the tool simply has no baseline to compare against — it can note after the fact that something changed shape, but not confirm whether it complies with the new rule. MayIA° works from the updated tracking plan directly, so the check exists as soon as the spec does, before the event has ever been sent once.
What TrackingPlan does better than anyone
Let's be fair: on its own turf, TrackingPlan is solid. 24/7 monitoring learned from real traffic, across web, app and server-side, with AI-assisted debugging to move fast once an alert fires — that's exactly what a team needs when it has to know within hours that a Meta pixel stopped firing or that a consent flow started leaking personal data.
If your need is "keep an eye on what's already live, continuously," there's no reason to look further. MayIA° doesn't claim to do that job, and wouldn't do it better.
The real cost of that delay
Here's where it breaks down in practice. A team shipping several times a week lives the same scenario over and over: a release goes out on a Friday, TrackingPlan catches on Monday that an event's behavior changed, and it took a full weekend of skewed data before anyone noticed. TrackingPlan did its job — it alerted — but the cost was already paid by the time the alert fired.
The only way to bring that cost down to zero is to check the release before it ships, which is not what TrackingPlan, or any production-monitoring tool, is built to do.
TrackingPlan vs MayIA° in one table
| TrackingPlan | MayIA° | |
|---|---|---|
| Core mechanism | Learns real-traffic patterns, alerts on drift | LLM agents run a QA plan against your tracking plan |
| Detection timing | After deployment, once live in production | Before deployment, before the release |
| Reference used | Past real traffic, learned automatically | The tracking plan, written and explicit |
| Catches a chronic error (never fixed since launch) | No — inseparable from the behavior learned as normal | Yes — checked against the tracking plan on every run, not against site history |
| Validates a change or new event before it has any traffic | No — needs real traffic to build a baseline first | Yes — as soon as the tracking plan is updated |
| Primary use case | Real-time alerting on broken pixels, events and consent in production | Blocking pre-release QA, no scripts to maintain |
| Built for | Teams that need to know within hours that production has drifted | Teams that want to stop the regression from shipping at all |
| Origin | SaaS monitoring vendor | Data & analytics consultancy (Smart Bees) that automated its own QA practice |
- Core mechanism
- Learns real-traffic patterns, alerts on drift
- Detection timing
- After deployment, once live in production
- Reference used
- Past real traffic, learned automatically
- Catches a chronic error (never fixed since launch)
- No — inseparable from the behavior learned as normal
- Validates a change or new event before it has any traffic
- No — needs real traffic to build a baseline first
- Primary use case
- Real-time alerting on broken pixels, events and consent in production
- Built for
- Teams that need to know within hours that production has drifted
- Origin
- SaaS monitoring vendor
- Core mechanism
- LLM agents run a QA plan against your tracking plan
- Detection timing
- Before deployment, before the release
- Reference used
- The tracking plan, written and explicit
- Catches a chronic error (never fixed since launch)
- Yes — checked against the tracking plan on every run, not against site history
- Validates a change or new event before it has any traffic
- Yes — as soon as the tracking plan is updated
- Primary use case
- Blocking pre-release QA, no scripts to maintain
- Built for
- Teams that want to stop the regression from shipping at all
- Origin
- Data & analytics consultancy (Smart Bees) that automated its own QA practice
MayIA°: an alternative born from manual QA, not from monitoring
MayIA° wasn't built by a monitoring vendor that looked at the market and bolted on a "pre-release" checkbox. It came from the opposite path: Smart Bees' consultants QA'd data layers by hand, event by event, before and after every release, for years, at their clients. That work is repetitive, time-consuming, and rests entirely on the rigor of whoever happens to be doing it that day.
MayIA° automates exactly that QA step, not the alerting step. The LLM agents work directly from the tracking plan and replay journeys the way a consultant would, before anything goes live. This isn't a TrackingPlan competitor repositioned for SEO — it's the software answer to a job Smart Bees was already doing in the field, for its clients.
Should you replace TrackingPlan, or pair it with MayIA°?
The honest answer: it depends on what you're actually trying to fix, and often the right answer is "both," not "one instead of the other."
- Your pain is "we never know a pixel broke until sales complains" → keep TrackingPlan, that's exactly its job.
- Your pain is "every release has a real chance of breaking an event, and we only find out days later" → that's the gap MayIA° fills, and one TrackingPlan will never fill by design.
- You have both pains → pairing them makes sense: MayIA° blocks regressions before they ship, which mechanically shrinks what TrackingPlan has to catch afterward.
How to tell which side your problem is on
One question settles it: when a tracking regression happens, do you want to learn about it in production, after the fact — or do you want it blocked before it ships? If it's the first, TrackingPlan is already the right tool, no need to switch. If it's the second, that's a conversation worth having with MayIA° — and if you want to see how both approaches stack up in detail against a third, script-based QA tool, the full comparison goes further.
FAQ
Does TrackingPlan do QA before a release ships?
No. TrackingPlan learns your tracking's behavior from real traffic and alerts on drift once it's live in production. It doesn't validate a release before it ships — that's exactly the gap MayIA° fills.
Can MayIA° fully replace TrackingPlan?
It depends on what you need. If you need 24/7 real-time alerting on traffic already in production (a pixel that stops firing, a PII leak), that's TrackingPlan's job and MayIA° doesn't replicate it. If your pain is regressions regularly shipping to production, MayIA° addresses that upstream. Many teams run both together rather than swapping one for the other.
Can TrackingPlan catch a tracking error that's been present since the site launched?
No, or only with great difficulty. TrackingPlan learns normal behavior from historical real traffic: an error that's always been there is part of what it considers normal, since there's never any drift from it to detect. MayIA° checks every event against the tracking plan regardless of its history, so a chronic error is caught on the very first check.
Can TrackingPlan validate that a new event complies with an updated tracking plan?
Not directly. TrackingPlan first needs to observe enough real traffic with the new behavior to build a reference: it notices that something changed, but doesn't verify that the change complies with the new rule. MayIA° checks compliance as soon as the tracking plan is updated, before the event has ever been sent once.
Do I need to write code to use MayIA° as a TrackingPlan alternative?
No, on either side. TrackingPlan learns from traffic with no scripts to write; MayIA° builds its QA plan directly from your dataLayer taxonomy, also with no scripts. The difference isn't the scripting workload — it's when each one acts: after the release ships for one, before it ships for the other.