Govern Like an Enterprise, Ship Like a Startup

Many organizations have a data problem they won't admit out loud.

They’re tracking millions of events. Product, marketing, and analytics teams are instrumenting differently. Access controls are a mess. Nobody's quite sure which metrics are "official." Somewhere in all that noise, critical decisions are being made based on data that may or may not be trustworthy.

Traditional fixes to unreliable data just shift the pain around and create new problems. Lock everything down with rigid governance, and innovation grinds to a halt. Let everyone self-serve without guardrails, and chaos takes over. It's a lose-lose situation that costs companies speed, trust, and ultimately, competitive advantage.

There's a better way, and it starts with rethinking governance. Today we’re announcing new features to help you govern faster, control better, and trust completely.

Rethinking access and control

The problem with current permission systems is simple: you can't see what people actually have access to, and you can't configure it to match how your teams work. You're either locking people out of tools they need or giving them capabilities that make your security team nervous.

Role-Based Access Control (RBAC) changes this with 40+ configurable permissions across Amplitude. Craft a "Product Analyst" role with dashboard creation and chart access but no taxonomy editing. Build a "Customer Insights" role with session replay viewing but no data export capabilities. Design a "Marketing Reporter" role with read-only dashboard access and nothing more.

Your roles finally mirror your actual org structure. You get complete visibility into every permission for every role, team members get exactly the access they need to be productive, and security stops being a compromise between protection and enablement.

Solving the metric definition problem

Access control is important, but it’s only half the battle. There's another trust issue lurking in most organizations, and it sounds like this: "Wait, which customer lifetime value number is correct?"

Marketing has their definition. Finance has theirs. Product built something different six months ago. Suddenly, three people are presenting three different versions of a number in the same meeting and nobody knows who is right. This metric definition chaos erodes trust in data across the entire organization. When different tools and teams calculate core metrics differently, people stop believing any of the numbers.

Warehouse Metrics completely cuts through this noise. Your data warehouse becomes the single source of truth for metric definitions, and those trusted calculations flow directly into Amplitude. Marketing, product, and engineering all work from one identical definition. The "which number is right?" debates disappear. Cross-functional alignment becomes possible because everyone's looking at the same data and the same definition.

Automation that actually works

Here's something that doesn't scale: humans manually checking data quality across millions of events. They might catch a tiny fraction of the issues, but that bad data could impact downstream analysis for weeks or months before it's fixed.

Automated Tasks flips this completely. It lets teams build workflows that monitor and maintain cleaner data. Set a rule once, and the system implements it and watches 24/7. Your data team can address issues in minutes instead of months, fixing problems before they cascade. Even better, they can spend their time generating deeper new insights instead of playing data detective.

Data teams shift from reactive firefighters to proactive data managers. Instead of wondering what problems might be lurking, they can operate with the certainty that the system is monitoring everything all the time. That confidence is productivity fuel.

Making instrumentation less painful

Let's be honest about something everyone knows. Instrumentation is tedious. Always has been. Each click isn’t just a click—you’re deciding what to call the event, how to describe it, and whether you’re capturing the right thing. At scale, that turns into a slow, error-prone process that’s difficult to keep consistent.

AI-assisted Visual Labeling takes that pain away. Use AI to help identify, label, and instrument UI elements. It means less manual work, fewer errors, and more consistency. Here's the game changer for most teams: Non-technical team members can help instrument without touching code. A PM can help define what needs tracking. A designer can tag the elements they built. Suddenly, instrumentation becomes collaborative instead of an engineering bottleneck.

Eliminating the deployment bottleneck

Then there's the deployment problem that slows everyone down. Your product evolves constantly, but your instrumentation is locked into code releases. Want to start tracking where your users experience frustration? Wait for the next deploy. Want to limit your click tracking to certain pages? Better file a ticket.

This mismatch between product velocity and instrumentation flexibility creates real problems. Questions emerge that your current tracking can't answer. Opportunities to understand user behavior slip away while you wait for deployments. Your instrumentation becomes a lagging indicator of your product instead of keeping pace with it.

Autocapture Remote Configuration eliminates this lag completely. Change your tracking configuration instantly, without touching code or waiting for deployments. As the product evolves, instrumentation evolves with it, and teams can answer new questions as fast as they think of them. The deployment bottleneck vanishes.

Building the foundation for AI

Everyone's excited about AI-powered insights, personalized experiences, and predictive analytics. The promise is real. But bad data foundations kill those AI initiatives before they start.

AI doesn't fix messy data. It amplifies the mess. Feed an AI model inconsistent metrics and it learns inconsistency. Train it on ungoverned data and it produces unreliable outputs. Give it unrestricted access and you've created compliance nightmares waiting to happen.

That's where governance capabilities become critical for your future, not just your present. Robust access controls mean AI systems operate safely within defined boundaries. Standardized metric definitions give AI models the consistent truth they need to learn meaningful patterns. Automated quality monitoring ensures that as AI systems scale, the data feeding them stays trustworthy. Comprehensive instrumentation provides the rich behavioral context that AI needs to generate meaningful insights.

The companies that will win with AI are the ones building bulletproof governance today. They'll deploy AI faster because their data is ready. They'll trust the outputs more because the inputs are reliable. They'll scale AI confidently because governance makes it safe. Meanwhile, competitors will spend years patching their foundations before AI can deliver any value.

Accelerating governance accelerates business

Stack all of this together and you get something powerful: governance that doesn't feel like governance. Controls that protect without blocking. Automation that eliminates tedious work. Access that scales without becoming a security nightmare. And data that teams actually trust.

The companies that solve governance problems don't just move faster than their competitors. They make better decisions because everyone's working from trusted data. They build better products because teams can focus on customers instead of fighting their own infrastructure. Now is the time to let your organization operate with the speed of a startup and the confidence of an enterprise.