Most product teams hit the same wall with Amplitude: you're paying thousands per month for analytics, then thousands more for experimentation, and somehow your engineering team still complains about the workflow. The pricing structure feels like death by a thousand cuts - every new capability requires another SKU, another integration, another budget conversation.
Statsig takes a fundamentally different approach. Built by ex-Facebook engineers who ran experiments at massive scale, the platform bundles experimentation, feature flags, analytics, and session replay into one unified system. No separate tools, no integration headaches, no surprise invoices when you want to test a new feature.
Statsig emerged in 2020 when ex-Facebook engineers built a developer-first experimentation platform. They rejected legacy tools' complexity and gatekeeping - their mission was creating the fastest, most powerful experimentation tools that engineers actually want to use.
Amplitude took a different path. Founded in 2012, they started as mobile analytics specialists and expanded into broader product analytics over time. Experimentation came much later as an add-on feature, not a core capability.
This origin story shapes each platform's DNA. Statsig built unified infrastructure from day one: experimentation, feature flags, analytics, and session replay share one data pipeline. Amplitude retrofitted experimentation onto their analytics foundation, creating separate tools that require integration. The architectural difference affects everything from pricing to performance.
Statsig appeals to engineering-led organizations that value technical depth and integrated workflows. Teams at OpenAI, Notion, and Figma chose Statsig because engineers drive adoption. The platform speaks their language with transparent SQL queries, warehouse-native deployment, and SDKs for every stack you can imagine.
Sumeet Marwaha, Head of Data at Brex, puts it simply: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."
Amplitude targets product teams seeking behavioral analytics first, experimentation second. Their pricing structure reflects this priority - analytics forms the core offering while A/B testing requires higher tiers. Product managers appreciate the visualization tools and pre-built reports, but engineers often feel like second-class citizens.
The philosophical divide runs deep. Statsig believes experimentation should be embedded in every release; Amplitude treats it as one analytical method among many. This explains why Statsig includes unlimited feature flags at every tier while Amplitude charges separately for feature management. One platform sees experimentation as fundamental infrastructure, the other as a premium add-on.
Statsig's experimentation platform handles over 1 trillion events daily with advanced statistical methods most platforms lack. Sequential testing lets you stop experiments early when results are clear - no more waiting weeks for inconclusive results. CUPED variance reduction makes experiments conclude 30% faster by reducing noise in your metrics.
Amplitude's A/B testing provides basic split testing with feature flags. You can run simple experiments and track conversion rates, but the platform lacks sophisticated techniques like:
Stratified sampling for balanced user groups
Automated heterogeneous effect detection
Multi-armed bandit optimization
Switchback experiments for marketplace dynamics
The infrastructure difference shows in practice. Statsig maintains 99.99% uptime while processing massive event volumes. Amplitude's experimentation features work well for basic tests but struggle with complex experimental designs that require advanced statistical methods.
Paul Ellwood from OpenAI's data engineering team explains the impact: "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."
Both platforms offer essential analytics features: funnel analysis, retention curves, and cohort segmentation. You can track user journeys and measure engagement metrics effectively. The visualization capabilities look similar on the surface - both create clear charts and dashboards.
But integration depth makes all the difference. Statsig connects analytics directly to experiments; every feature flag automatically tracks impact without manual configuration. Amplitude requires manual setup to link features with metrics, adding extra steps to your workflow. Those extra steps compound quickly when you're running dozens of experiments.
Statsig provides transparent SQL queries with one-click access. You see exactly how metrics calculate, making debugging straightforward. Amplitude abstracts queries behind proprietary interfaces, limiting visibility into calculations. This transparency matters when debugging metric discrepancies or building custom analyses - you can't fix what you can't see.
Statsig includes unlimited free feature flags at every pricing tier. Create flags, target users, and roll out gradually without cost concerns. Amplitude charges for feature flags based on monthly tracked users, with limits starting at lower tiers. The economics shift dramatically when you're managing hundreds of flags across multiple teams.
Both platforms support percentage rollouts and user targeting, but Statsig adds automated rollback capabilities. If metrics drop below thresholds, features automatically revert. This safety net prevents bad releases from impacting users - especially crucial for teams shipping multiple times per day.
Platform deployment options reveal another key difference:
Statsig: Edge SDK support, warehouse-native options, real-time flag evaluation
Amplitude: Standard SDKs only, cloud-hosted evaluation
The flexibility matters when you need sub-50ms flag checks or want to keep sensitive data in your own infrastructure.
Statsig processes 2.3 million events per second across its infrastructure. The platform scales automatically without performance degradation. Customer reviews consistently highlight this reliability: teams report zero downtime or latency issues even during traffic spikes.
Amplitude handles substantial volumes but requires careful capacity planning at scale. Reddit discussions mention performance concerns when event volumes spike unexpectedly. The platform works well for standard analytics workloads but may struggle with extreme scale or sudden growth.
Warehouse-native deployment gives Statsig users complete data control. Your events stay in Snowflake, BigQuery, or Databricks while Statsig computes results. This approach solves three critical problems:
Data governance for regulated industries
Unified analytics across all tools
Zero data duplication costs
Amplitude requires sending data to their servers, raising privacy and governance concerns. You're essentially maintaining two copies of your event data - one in your warehouse, one in Amplitude's cloud.
Statsig's free tier destroys conventional pricing models. You get unlimited feature flags, 50K session replays, and full analytics access without spending a dollar. Amplitude's free plan caps users at 50K monthly tracked users (MTUs) with severely limited features.
The real kicker: Statsig bundles all core products - experimentation, flags, analytics, and replay. Amplitude charges separately for each tool, turning every new capability into a budget discussion. A startup wanting basic A/B testing, feature flags, and session replay faces three separate purchase decisions with Amplitude versus one free tier with Statsig.
Feature availability tells the story:
Statsig free tier: All experimentation features, unlimited flags, full analytics
Amplitude free tier: Basic analytics only, no experimentation, limited cohorts
Teams can validate entire product strategies on Statsig's free tier. With Amplitude, you're upgrading before running your first meaningful experiment.
Amplitude's pricing escalates quickly with usage. Their Growth plan starts at $995/month, while Enterprise plans begin at $2,000/month. The MTU-based model creates unpredictable costs - especially painful for consumer apps with fluctuating user counts.
Statsig charges only for analytics events and session replays, not feature flag checks or user seats. This approach keeps costs predictable: you pay for what you actually use. A company with 1M monthly active users pays roughly $500/month on Statsig versus $2,000+ on Amplitude's Growth tier.
Reddit users share horror stories about Amplitude's "high cost and lack of transparency regarding event budgets." One noted how costs spike unexpectedly when Amplitude's session replay "maxes out at 10K sessions" - forcing immediate upgrades mid-month.
The pricing gap widens at scale. Consider a typical growth-stage company:
5M MAUs
100 experiments per month
50K session replays
20 team members
Amplitude total: $5,000-10,000/month across multiple SKUs Statsig total: $1,000-2,000/month for everything
One G2 reviewer captured it perfectly: "Customers could use a generous allowance of non-analytic gate checks for free, forever."
Getting started quickly matters when your team needs results. Statsig users report launching first experiments within days using 30+ SDKs and comprehensive documentation. The unified platform means one integration unlocks everything - no separate setups for analytics versus experimentation.
Amplitude's pricing structure forces you to implement two different systems with distinct data models. Your engineering team spends weeks reconciling metric definitions between platforms. The same conversion event might calculate differently in analytics versus experiments, creating endless confusion.
Statsig's unified data model eliminates these conflicts entirely. One metric definition works everywhere. Your data scientists and product managers finally speak the same language, using the same numbers to make decisions.
Direct access to experts changes everything. Statsig provides Slack channels where engineers and data scientists respond to all customers - including free tier users. You get answers from the people who built the platform, not tier-one support reading from scripts.
Amplitude reserves dedicated support for Enterprise customers only. Lower-tier users navigate community forums and documentation. Response times vary dramatically based on subscription level, creating friction exactly when teams need help most.
Both platforms handle enterprise scale, but infrastructure quality differs. Statsig processes 200B+ events daily without performance degradation. Your experiments run smoothly whether you have 1,000 or 100 million users. The same infrastructure powering OpenAI's experiments powers yours.
Modern teams need flexibility in their data architecture. Statsig offers both cloud-hosted and warehouse-native deployment options. You maintain complete control over sensitive data while leveraging advanced experimentation capabilities. Healthcare companies can keep PII in their warehouse while still running sophisticated experiments.
Amplitude primarily operates as a cloud-hosted solution. Reddit discussions highlight how teams struggle to consolidate Amplitude with other BI tools. Your data lives in multiple places, creating governance nightmares and compliance risks.
The warehouse-native approach transforms team velocity. Notion reduced deployment time by 75% using Statsig with Vercel Edge Config. When your experimentation platform integrates deeply with existing infrastructure, everything moves faster.
Statsig delivers enterprise-grade experimentation, analytics, and feature management at 50-80% lower cost than Amplitude. While Amplitude's pricing starts at $49/month for basic analytics alone, Statsig includes experimentation and feature flags free. The math becomes obvious quickly.
Engineering teams gravitate toward Statsig's developer-first approach. Transparent SQL, warehouse-native options, and unified platform architecture solve real problems. Every query shows its underlying SQL with one click. Teams maintain full control over their data infrastructure while accessing advanced analytics that actually work.
Sumeet Marwaha from Brex summarizes the experience: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."
Companies like OpenAI, Notion, and Brex chose Statsig for advanced statistical capabilities unavailable in Amplitude. Sequential testing, CUPED variance reduction, and stratified sampling come standard. These techniques typically require custom-built platforms or expensive enterprise contracts elsewhere.
Turn any feature release into an experiment without switching tools or reconciling conflicting metrics. Statsig's unified platform means your analytics events automatically power experimentation. No duplicate implementations, no metric discrepancies between systems, no confusion about which number to trust.
Access sophisticated experimentation techniques typically found only in custom platforms:
CUPED variance reduction for 30% faster experiments
Sequential testing for early stopping
Stratified sampling for balanced groups
Multi-armed bandits for continuous optimization
Statsig processes over 1 trillion events daily with the same statistical rigor used by OpenAI and Microsoft. Your experiments get enterprise-grade analysis from day one, not watered-down approximations.
Scale from startup to enterprise without platform migrations. Notion scaled from single-digit to 300+ experiments quarterly on the same platform. Start free and grow without switching tools or losing historical data. The infrastructure handling billions of users today handles your first thousand tomorrow.
Choosing between Statsig and Amplitude ultimately comes down to your team's philosophy about experimentation. If you view A/B testing as one analytics method among many, Amplitude's approach might work. But if you believe every feature should be tested, every release measured, and every decision data-driven, Statsig's unified platform delivers more value at lower cost.
The technical advantages - warehouse-native deployment, advanced statistics, transparent SQL - matter less than the cultural shift Statsig enables. When experimentation becomes as easy as deploying code, teams naturally test more ideas. When feature flags cost nothing, teams naturally adopt progressive rollouts. The tools shape the culture.
For teams ready to dive deeper, check out Statsig's technical documentation or join their community Slack where engineers share implementation patterns and best practices. The same team that built Facebook's experimentation platform now helps yours scale.
Hope you find this useful!