A faster alternative to Amplitude: Statsig

Product teams often find themselves trapped between two worlds: they need deep analytics to understand user behavior, but they also need rapid experimentation to test new features. Most end up stitching together multiple tools - one for analytics, another for A/B testing, and yet another for feature flags.

This fragmentation creates a simple but costly problem. Data lives in silos, experiments take weeks to set up, and costs balloon as you scale. The question isn't whether you need both analytics and experimentation - it's whether you need separate tools for each.

Company backgrounds and platform overview

Amplitude launched in 2012 as a product analytics platform focused on behavioral insights. The company built its reputation helping teams map user journeys and track conversion funnels. Over a decade later, they've added experimentation capabilities to their analytics core, serving thousands of companies who started with analytics and gradually needed more.

Statsig emerged in 2020 with a fundamentally different architecture. The founding team - engineers from Facebook's core infrastructure who built tools processing trillions of events daily - designed a unified system for experimentation and analytics from day one. No bolt-ons, no separate pipelines, no integration headaches.

This architectural difference shapes everything else. Amplitude users typically combine their analytics platform with tools like LaunchDarkly for feature flags and Optimizely for experiments. Statsig customers like Notion and OpenAI run their entire product development cycle through one platform: testing ideas, rolling out features, and measuring impact without switching contexts.

The technical foundations tell the real story. Amplitude retrofitted experimentation onto their analytics infrastructure. Statsig built a single data pipeline that handles feature flags, A/B tests, and analytics together - which explains why companies report 50% faster experiment velocity after switching.

Feature and capability deep dive

Experimentation and testing capabilities

Here's what most teams discover too late: running experiments isn't just about having an A/B testing tool. You need:

• Statistical rigor to trust your results

• Speed to iterate quickly

• Integration with your feature flagging system

Statsig ships with warehouse-native deployment and implements advanced statistical methods like sequential testing, CUPED, and stratified sampling. These aren't just buzzwords - CUPED alone can reduce variance by 50%, meaning you detect real effects faster with smaller sample sizes. Amplitude's experimentation requires additional tools and setup, adding weeks to your testing cycle.

The integration goes deeper. Every Statsig feature flag includes built-in A/B testing at no extra charge. You literally flip a switch to turn any release into an experiment. Compare that to the typical Amplitude setup: analytics in one tool, feature flags in another, experiments in a third.

Paul Ellwood from OpenAI's data engineering team puts it plainly: "Statsig's experimentation capabilities stand apart from other platforms we've evaluated. Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

Analytics and data processing

Both platforms handle product analytics, but scale tells the real story. Statsig processes 1+ trillion events daily with 99.99% uptime - the same infrastructure that powers OpenAI and Microsoft. This isn't just about big numbers; it's about reliability when your business depends on real-time decisions.

The bundling strategy reveals each platform's philosophy:

• Statsig includes: Unlimited feature flags, 50,000 free session replays monthly, and full analytics in every plan

• Amplitude requires: Separate pricing for each capability, often through expensive add-ons

Data ownership matters more as you scale. Statsig offers warehouse-native analytics on Snowflake, BigQuery, and Databricks - your data stays in your infrastructure while you get full analytics capabilities. Amplitude focuses on behavioral cohorts within their hosted environment, which works until you need that data elsewhere.

Pricing models and cost analysis

Transparent pricing structures

Let's talk real numbers. Statsig charges only for analytics events and session replays - feature flags remain free at any scale. No MTU calculations, no surprise overages, no complex SKUs to decode.

Amplitude's pricing tells a different story:

• Plus plan: $49/month (limited to 100K MTUs)

• Growth plan: $995+/month

• Enterprise: Starting at $2,000+/month

The math gets painful at scale. A company with 10M events monthly (roughly 200K MTUs) faces dramatically different costs. Statsig maintains linear, predictable pricing tied to actual usage. Amplitude's user-based model creates cost cliffs that force urgent upgrades.

Real-world cost scenarios

Consider a 100K MAU startup. With Statsig's free tier, they get full analytics, experimentation, and feature flagging without paying a cent. That same startup pays $600+ annually for Amplitude's Plus plan - and they'll hit the 300K MTU ceiling fast.

Enterprise savings multiply. Brex cut costs by over 20% after switching platforms. When you factor in hidden costs - Amplitude charges extra for predictive audiences, real-time streaming, and advanced features that Statsig bundles - the total difference often exceeds 50%.

Don Browning, SVP at SoundCloud, evaluated the entire market: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration." The unified platform eliminated multiple vendor contracts and simplified their stack.

Decision factors and implementation considerations

Onboarding and time-to-value

Speed matters when you're shipping features. G2 reviews consistently praise Statsig's "quick setup" and "streamlined integration" - teams launch their first experiment within days, not weeks.

The contrast with Amplitude is stark. Multiple enterprise reviewers report steep learning curves requiring extensive training. Reddit discussions reveal teams struggling to understand how Amplitude differs from traditional BI tools, let alone integrate it with their experimentation workflow.

Real implementations prove the point. Secret Sales launched 30 features in six months after adopting Statsig, praising the "developer-friendly experience" and sub-10-second config propagation. That velocity comes from having everything in one place: no API keys to manage across tools, no data syncing delays, no conflicting metric definitions.

Support and scalability

Support structures reveal company priorities. Statsig provides direct Slack access to engineers - sometimes the CEO jumps in to help. This isn't just good service; it's recognition that blocked teams cost money. Amplitude's support requires Enterprise plans for dedicated assistance, leaving growing teams to figure things out alone.

Both platforms handle scale, but architecture matters. Statsig processes trillions of events daily for OpenAI and Microsoft without breaking a sweat. But raw scale isn't the whole story - Brex's data team chose Statsig specifically for transparency and reliability after validation issues with their previous platform.

Their engineers put it simply: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."

The infrastructure choices have downstream effects. Statsig's warehouse-native deployment lets teams keep data control while achieving enterprise performance. Amplitude's cloud-only architecture often forces additional tools - one Reddit user noted their company runs both Amplitude and Looker because neither handles all their analytics needs.

Bottom line: why is Statsig a viable alternative to Amplitude?

Statsig solves a fundamental problem: experimentation, analytics, and feature flags belong in one platform. While Amplitude's pricing splits these capabilities across products and price tiers, Statsig bundles everything together. Teams typically save 50% compared to assembling multiple tools.

The technical foundation backs up the promise. Processing over 1 trillion events daily with 99.99% uptime isn't just a stat - it's what enables Brex to reduce experimentation time by 50%. Unlike Amplitude's cloud-only model, warehouse-native deployment gives teams data control without sacrificing performance.

Sumeet Marwaha, Head of Data at Brex, captures the core benefit: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."

Results validate the approach:

• Notion scaled from single-digit to 300+ experiments quarterly

• SoundCloud reached profitability for the first time in 16 years

• Teams report 50% faster experiment velocity

These outcomes stem from removing friction between testing and measurement. When analytics and experimentation share the same data pipeline, the same metric definitions, and the same interface, velocity follows naturally.

Cost transparency seals the deal. Unlike Amplitude's complex pricing with hidden add-ons and usage cliffs, Statsig charges only for analytics events and session replays. Feature flags remain free at any scale - a model that actually encourages experimentation instead of punishing it.

Closing thoughts

Choosing between Statsig and Amplitude isn't really about features - both platforms can track events and run tests. The real question is whether you want separate tools that require integration and maintenance, or a unified platform built for modern product development.

If you're ready to explore how a unified approach could accelerate your team's velocity, check out:

• Statsig's interactive demo to see the platform in action

• Customer case studies from companies who made the switch

• Technical documentation for implementation details

Hope you find this useful!