At some point, every growing company hits the same wall. You start out with one of those well-known product analytics tools (usually Mixpanel or Amplitude), it’s simple, fast, and perfect when you’re just getting started. Everyone loves it.
Then your data starts to explode. A few thousand users turn into a few million. Your product logs every click, every screen view, every action. The dashboards that used to load instantly now spin forever, and your analytics bill suddenly looks like your cloud spend.
If this sounds familiar, you’re not alone. It’s what happens when success outgrows your stack.
Where Traditional Tools Break Down
Most analytics tools still rely on an old approach: they copy your event data into their own system. It’s how they keep things fast and user-friendly at small scale. But when your company hits real volume (e.g: billions of events) that setup becomes a bottleneck.
Every event you track gets duplicated and shipped out to someone else’s servers. You end up paying to store and process the same data twice. And once those two copies drift out of sync (they always do), you get the dreaded “Why don’t these numbers match?” conversations between teams.
Worse, many of these tools charge based on how many events you send them. It feels fair in the beginning, pay for what you use, right? But as your product grows, your costs skyrocket. You end up spending more just because your users are active and your team is tracking smarter. That’s backwards. You shouldn’t be penalized for doing analytics well.
The Scale Problem
The truth is, most analytics platforms were never built to handle the size of data modern teams are collecting. They do great on millions of events. They break at billions.
Queries slow down. Funnels time out. Reports that used to take seconds now take minutes. And when your dashboards lag, people stop exploring. Curiosity fades. Teams stop asking deeper questions because they know they’ll have to wait for answers.
When that happens, you don’t just lose speed, you lose momentum. Your culture of data-driven decision-making starts to erode.
A Better Way Forward
So what’s the fix? Instead of copying your data into someone else’s system, run analytics directly where it already lives -->in your own data warehouse.
That’s the warehouse-native model. Your data stays put in Snowflake, Databricks, BigQuery, or Redshift (or you name it), and your analytics layer (like any warehouse-native tool) just connects to it. No duplication. No sync issues. No arbitrary event limits.
Because everything runs on your existing infrastructure, you get near-infinite scalability without paying extra for each event. You can track everything your teams care about: every product interaction, campaign touchpoint, and customer journey. All without worrying about hitting a ceiling.
And since pricing is based on seats, not volume, you can give more people access to insights without watching your bill spike.
What to Do Instead
If your analytics stack is starting to feel like a constraint instead of a tool, that’s your cue to rethink it. Traditional 3rd party analytics was built for a different era, smaller data, fewer users, simpler funnels.
Today’s products generate a large amount of behavioral data, and you need a system that embraces that, not fears it.
A warehouse-native platform, such as Mitzu lets you move faster, keep your data consistent, and stay in control of cost. Usually these are all self-service analytics for every team, not just the analysts.
Because when your data lives where it should, and your tools scale with you, analytics stops being a burden and starts being what it was always meant to be: a growth engine.
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