An alternative to Amplitude for feature flags: Statsig

Most product teams start with Amplitude for analytics, then bolt on separate tools for feature flags and experimentation. This fragmented approach creates data silos, doubles integration work, and inflates costs as you scale.

Teams at OpenAI and Notion discovered a different path: using Statsig's integrated platform where feature flags, experiments, and analytics share the same data pipeline. The cost savings alone - typically 50-80% less than Amplitude - make the switch compelling. But the real advantage runs deeper.

Company backgrounds and platform overview

Statsig launched in 2020 when former Facebook engineers built a developer-first experimentation platform designed for speed and scale. Today they process over 1 trillion events daily for companies like OpenAI and Notion. The team focused on one core insight: every feature flag should be testable, and every test should feed directly into analytics.

Amplitude took a different route. Starting in 2012 as a product analytics company, they built their reputation on data visualization and behavioral insights. Later acquisitions added experimentation and feature management - but these capabilities still feel bolted on rather than native. You can see this in how the products work day to day.

The architectural differences matter. Statsig built an integrated suite where experimentation, feature flags, analytics, and session replay share one data pipeline. Launch a feature flag; it's automatically instrumented for analytics. Run an experiment; results flow directly into your metrics dashboard. As Sumeet Marwaha, Head of Data at Brex, noted: "Having experimentation, feature flags, and analytics in one unified platform removes complexity and accelerates decision-making."

Amplitude keeps these systems separate. Teams start with behavioral analysis, then layer on testing and feature management through different modules. Some organizations prefer this separation - it matches how they've structured their teams and workflows. But the cost is constant context switching and data reconciliation between tools.

Feature and capability deep dive

Experimentation and feature management

Here's where the platforms diverge sharply. Statsig provides unlimited free feature flags with advanced targeting. No caps, no tiers, no surprise bills when you cross usage thresholds. Amplitude restricts feature flags based on pricing tier - a constraint that forces teams to ration their rollouts.

The technical capabilities tell an even clearer story. Statsig includes:

• Sequential testing for faster statistical significance

• CUPED variance reduction to detect smaller effects

• Warehouse-native deployment for complete data control

• Automated metric guardrails to catch regressions

Amplitude's experimentation features feel basic by comparison. No sequential testing. No variance reduction. Limited statistical methods. Paul Ellwood from OpenAI's data engineering team explained why this mattered: "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."

The warehouse-native option deserves special attention. You can deploy Statsig directly in Snowflake, BigQuery, or Databricks. Your data never leaves your infrastructure. Amplitude requires exporting data to your warehouse - adding latency, complexity, and potential security issues.

Analytics and data infrastructure

Amplitude built its reputation on behavioral analytics and user journey mapping. The platform excels at cohort analysis, retention curves, and funnel visualization. Product teams love these tools for understanding user navigation patterns. That's Amplitude's sweet spot.

But here's what teams find frustrating: analytics disconnected from experimentation means constant tool switching. You run a test in one system, analyze results in another, then manually reconcile the data. Statsig solves this by integrating analytics directly with feature flags and experiments. Every flag automatically tracks metric impact. No manual instrumentation needed.

The infrastructure numbers reveal the scale difference:

• Statsig: 6 trillion monthly events with sub-millisecond latency

• Amplitude: Event caps based on plan (Plus limits you to 300,000 MTUs)

That's not just a capacity difference - it's a fundamental constraint on how you can use the platform.

Pricing models and cost analysis

Transparent pricing structures

Statsig charges $0.05 per 1,000 events. That's it. Unlimited seats, flags, and experiments included. No complex tiers or feature gates. Just pay for what you use.

Amplitude's pricing structure involves multiple variables that compound quickly:

• Plus plan: $49/month for 300K MTUs

• Growth plan: Starting around $995/month

• Separate charges for session replay

• Additional costs for advanced analytics

• Per-seat pricing that scales with team size

The complexity catches teams off guard. What looks affordable at first balloons as you add features and users.

Real-world cost scenarios

Let's get specific with actual usage patterns. A 100K MAU SaaS company typically generates about 10 million events monthly. Here's how costs break down:

On Statsig:

• Events: ~$500/month

• Feature flags: Free (unlimited)

• Session replay: Free (up to 50K/month)

• Total: ~$500/month

On Amplitude Growth plan:

• Base platform: $2,000-5,000/month

• Session replay: Additional charges

• Feature flags: Limited by tier

• Total: $3,000-7,000/month

The gap widens at scale. Processing 50 million events monthly? That's $2,500 on Statsig versus $10,000+ on Amplitude. As Andy Glover from OpenAI shared: "Statsig has helped accelerate the speed at which we release new features. It enables us to launch new features quickly & turn every release into an A/B test."

Decision factors and implementation considerations

Developer experience and time-to-value

Getting to production quickly matters. Statsig's 30+ open-source SDKs get you running in hours, not weeks. One SDK handles analytics, feature flags, and experiments. According to customer reviews: "Implementing on our CDN edge and in our nextjs app was straight-forward and seamless."

Amplitude splits this across multiple SDKs. You integrate analytics first, then add experimentation separately. Double the integration work. Double the maintenance overhead. And constant struggles with data consistency between systems.

The unified approach pays dividends during daily development. Launch a feature behind a flag; it's automatically tracked. Roll out to 10% of users; see the impact on your metrics instantly. No switching between tools or waiting for data pipelines to sync.

Enterprise readiness and support

Both platforms check the enterprise boxes: SOC2 compliance, SSO, audit logs. But the similarities end there.

Statsig adds warehouse-native deployment for complete data sovereignty. Your data stays in your Snowflake, BigQuery, or Databricks instance. Critical for regulated industries where data residency matters. Amplitude's cloud-only model forces your data onto their servers - creating additional security reviews and compliance headaches.

Support models differ dramatically too:

• Statsig: Direct Slack access to engineering team

• Amplitude: Traditional ticketing with multi-day response times

When you're debugging a critical experiment at 3 AM, that difference matters.

Cost predictability at scale

Feature flag usage grows exponentially as teams adopt them. With Statsig, flags stay free at any volume. You only pay for analytics events and session replays. Simple, predictable, linear.

Amplitude charges separately for each module. Costs spike as you cross usage tiers. Reddit discussions are filled with teams shocked by unexpected bills. One month you're under the MTU limit; the next month you're paying 3x more.

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

Statsig costs 50-80% less than Amplitude while delivering more advanced capabilities. Feature flags remain free forever. Experimentation includes cutting-edge statistical methods. And everything integrates seamlessly - no data silos or tool fragmentation.

The platform's statistical engine sets it apart. CUPED variance reduction detects 20% smaller effects with the same sample size. Sequential testing reaches conclusions 30% faster. Automated guardrails catch metric regressions before they impact users. Paul Ellwood from OpenAI emphasized this advantage: "Statsig's experimentation capabilities stand apart from other platforms we've evaluated. Statsig's infrastructure has been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

While Amplitude focuses on behavioral analytics, Statsig integrates the entire product development lifecycle. Track metrics, launch experiments, control features - all without switching tools. This unified approach solves the tool fragmentation issues plaguing modern product teams.

The warehouse-native option seals the deal for enterprise teams. Complete data control. No vendor lock-in. Deploy in your own infrastructure while accessing Statsig's statistical engine. This flexibility helped Statsig grow from zero to $40M+ ARR in under four years - faster than most B2B SaaS companies.

For teams using Amplitude primarily for analytics, the switch might not make sense. But if you're paying for Amplitude's experimentation and feature flag modules? You're leaving money on the table. The same capabilities cost a fraction on Statsig - with better statistical methods and true platform integration.

Closing thoughts

Choosing between Statsig and Amplitude ultimately comes down to your team's priorities. If you need world-class behavioral analytics and don't mind managing separate tools, Amplitude serves that niche well. But for teams wanting integrated feature flags, experimentation, and analytics at a reasonable price, Statsig delivers a compelling alternative.

The cost savings alone justify evaluation - most teams cut their bill by 50-80%. Add in the advanced statistical capabilities and unified workflow, and the decision becomes clearer. Start with Statsig's generous free tier to test the platform yourself. Run a few experiments, launch some feature flags, and see how the integrated approach changes your development velocity.

Want to dive deeper? Check out Statsig's documentation for implementation guides, or explore their customer case studies to see how teams like OpenAI and Notion use the platform at scale.

Hope you find this useful!