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A unified alternative to Amplitude Experiment: Statsig
Most teams building experimentation programs hit the same wall: they've got analytics in one tool, A/B testing in another, and feature flags scattered across a third. The costs pile up, the integrations break, and suddenly you're spending more time managing tools than running experiments.
Amplitude built their experimentation platform as an add-on to existing analytics. Statsig took the opposite approach - they built everything from scratch as one unified system. This fundamental difference shapes everything from pricing to performance to how quickly teams can ship.
Company backgrounds and platform overview
Statsig launched in 2020 when a small engineering team got fed up with the status quo. They'd worked at Facebook and Uber, running thousands of experiments at scale. They knew what great experimentation infrastructure looked like - and they knew the market wasn't delivering it. So they built their own: four production-grade tools unified by a single data pipeline.
Amplitude started as a product analytics company back in 2012. They spent years perfecting behavioral analytics before adding Amplitude Experiment as a separate product. This makes sense from a business perspective - sell more products to existing customers. But it creates complexity for teams who just want to test and ship faster.
The philosophical split couldn't be clearer. Statsig bundles experimentation, feature flags, analytics, and session replay into one platform. You install one SDK, define metrics once, and everything just works. Amplitude sells separate products that you piece together. Different SDKs, different metrics definitions, different billing - it adds up fast.
Market positioning and target audiences
Statsig attracts engineering-first teams who care about technical depth. OpenAI uses them to test GPT features. Notion runs hundreds of experiments monthly. Figma relies on them for infrastructure that handles trillions of events without breaking a sweat. These aren't companies that settle for "good enough" tools.
Amplitude targets a different crowd: larger enterprises already invested in their analytics ecosystem. If you're already paying for Amplitude Analytics, adding Experiment feels natural. The Plus plan at $49/month also appeals to smaller teams just getting started - though that pricing comes with serious limitations we'll explore later.
"Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."
Paul Ellwood, Data Engineering, OpenAI
The pricing models reflect these different philosophies. Statsig gives you unlimited free feature flags at any scale - whether you're testing with 100 users or 100 million. Amplitude's pricing caps features based on monthly tracked users (MTUs). Once you hit those limits, costs spike dramatically. Engineering teams appreciate knowing exactly what they'll pay as they scale.
Feature and capability deep dive
Experimentation and statistical engines
Here's where the technical differences really show. Statsig ships with sequential testing, switchback testing, and CUPED variance reduction built in. These aren't just buzzwords - they're advanced statistical methods that help you detect smaller effects with less data. Sequential testing lets you peek at results without inflating false positive rates. Switchback testing handles time-based effects that traditional A/B tests miss. CUPED can reduce variance by 50% or more, meaning faster decisions with the same statistical power.
Amplitude Experiment offers standard A/B testing designed to complement their analytics product. It works fine for basic use cases. But if you need anything beyond simple randomized experiments, you'll hit walls quickly. No sequential analysis. No variance reduction. Just traditional fixed-horizon tests that force you to wait weeks for results.
The deployment options highlight another key difference. Statsig's warehouse-native deployment runs experiments directly in your Snowflake, BigQuery, or Databricks instance. Your data never leaves your infrastructure. This isn't just about security (though that matters) - it's about performance and cost. Why pay to move terabytes of data when you can analyze it where it lives?
Both platforms support Frequentist statistics, which most teams know and trust. But Statsig also includes Bayesian methods for teams who want probabilistic interpretations. Instead of just "significant" or "not significant," you get statements like "there's an 85% chance this feature improves retention by 2-5%." Different teams prefer different approaches - Statsig supports both.
Analytics and developer experience
SDK sprawl kills developer productivity. You know the pattern: one SDK for analytics, another for feature flags, a third for session replay. Each with its own initialization, its own config, its own bugs. Statsig solves this with 30+ open-source SDKs that handle everything through a single integration. One initialization. One set of configs. One dependency to manage.
Performance at scale isn't negotiable for teams serving millions of users. Statsig's SDKs deliver sub-1ms evaluation latency for feature flags after initialization. They support edge computing for global deployments. These optimizations matter when you're:
Serving feature flags on every page load
Running experiments that affect core user flows
Operating across multiple regions with strict latency requirements
Amplitude requires separate SDK implementations for analytics and experimentation. That's double the integration work, double the maintenance, and double the potential for bugs. Sure, they share some underlying infrastructure. But from a developer's perspective, you're still managing multiple tools.
The metrics story deserves special attention. Statsig uses one unified metrics catalog across all products. Define a conversion metric once, use it in experiments, track it in dashboards, alert on it in monitors. Amplitude maintains separate metric definitions between products. The same "checkout conversion" metric might be calculated differently in Analytics versus Experiment. These inconsistencies create confusion and erode trust in results.
"The clear distinction between different concepts like events and metrics enables teams to learn and adopt the industry-leading ways of running experiments," noted one Statsig user on G2.
Pricing models and cost analysis
Transparent pricing structures
Let's talk real numbers. Statsig charges only for analytics events and session replays. Feature flags are completely free - unlimited usage, unlimited MAU, unlimited everything. This changes the calculus for teams considering experimentation. You can roll out feature flags across your entire product without worrying about per-flag or per-user charges.
Amplitude's pricing model gets complicated fast:
Analytics charges based on MTUs
Experiment adds separate charges for "experimentation subjects"
Feature flags cost extra on top of both
Different products have different billing cycles and minimums
Consider a typical scenario: 100K monthly active users generating 20 sessions each. On Statsig's free tier, that's $0 for full experimentation capabilities. On Amplitude Plus, you're looking at $49+/month - and that's before you hit any of their usage caps. The free tier supports only 50K MTUs, so you're already into paid territory.
Enterprise cost implications
The pricing gap becomes a chasm at enterprise scale. Third-party analysis confirms that Amplitude's costs can spiral out of control. Users report bills jumping from hundreds to thousands of dollars monthly as they cross certain thresholds. The 10 million event mark seems particularly painful - that's when Amplitude pushes you to enterprise pricing.
Statsig takes a different approach: 50-80% cost savings compared to equivalent Amplitude setups, with volume discounts kicking in at 200K MAU. No platform fees. No implementation charges. No surprise bills when you have a viral moment.
"We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion."
Don Browning, SVP, Data & Platform Engineering, SoundCloud
The total cost extends beyond subscription fees. Think about:
Engineering time: Managing multiple vendors versus one
Data costs: Moving data between systems versus analyzing in place
Opportunity cost: Waiting for budget approval versus starting immediately
Teams using Amplitude often need additional tools to match Statsig's capabilities. Add LaunchDarkly for feature flags. Mix in FullStory for session replay. Suddenly you're managing three or four vendors to get what Statsig provides in one platform.
Decision factors and implementation considerations
Time-to-value and onboarding
Speed matters when your competition ships daily. Statsig gets teams running experiments within hours, not weeks. The platform automatically discovers your existing events and suggests relevant metrics. No manual taxonomy setup. No complex configuration. Just connect your data and start testing.
Here's what typical onboarding looks like with Statsig:
Install SDK (30 minutes)
Send a few events (1 hour)
Create first feature flag (5 minutes)
Launch first experiment (30 minutes)
Amplitude's implementation path requires more patience. First, you set up analytics and wait for data to flow. Then you define your taxonomy and train your team. Only after analytics is humming can you add experimentation. Many teams report spending weeks in setup before running their first test.
"It has allowed my team to start experimenting within a month," noted one Statsig user in their G2 review.
Support makes a huge difference during implementation. Statsig provides dedicated customer data scientists who actually understand statistics. They'll help design your experiments, review your metrics, and explain surprising results. These aren't just customer success managers reading from scripts - they're practitioners who've run thousands of experiments themselves.
Scalability and enterprise readiness
Both platforms handle scale, but their approaches differ fundamentally. Statsig processes over 1 trillion events daily across customers like OpenAI and Microsoft. The infrastructure scales automatically - no capacity planning, no performance degradation, no emergency calls to increase limits.
Amplitude's pricing structure creates artificial scaling challenges. Those MTU limits force uncomfortable decisions: limit who can access the platform or pay significantly more. The 10 million event threshold particularly frustrates growing companies. You're succeeding, your product is growing, and suddenly your analytics bill explodes.
Data sovereignty represents another critical difference. Financial services, healthcare, and government clients often can't send data to third-party clouds. Statsig's warehouse-native option solves this - run everything within your own Snowflake or BigQuery instance. Amplitude lacks this capability entirely, limiting adoption in regulated industries.
The seat limit question affects adoption patterns too. Statsig offers unlimited seats across all plans. Everyone from engineers to designers to customer success can access experiments and results. Amplitude's user-based pricing often restricts access to a small group, reducing the platform's impact across the organization.
Bottom line: why is Statsig a viable alternative to Amplitude?
Statsig delivers more functionality at 50-80% less cost than comparable Amplitude setups. You get a complete experimentation platform - not just basic A/B testing bolted onto analytics. The unified approach means faster implementation, cleaner data, and less vendor management overhead.
Real companies see real results. Brex cut experimentation overhead by 50% after consolidating tools. SoundCloud achieved profitability for the first time in 16 years:
"Leveraging experimentation with Statsig helped us reach profitability for the first time in our 16-year history."
The warehouse-native deployment option opens doors that Amplitude can't. Keep your data in your infrastructure. Maintain complete control. Meet the strictest security requirements. This isn't just a nice-to-have for enterprises - it's often the difference between adopting a platform or building internally.
Key differentiators for decision makers
The free tier comparison tells the whole story:
Statsig's free tier includes:
Unlimited feature flags (any scale)
50,000 session replays monthly
Complete experimentation suite
No seat limits
No MAU restrictions
Amplitude's approach:
Feature flags require paid plans
Strict MTU caps (50K on Plus)
Experimentation costs extra
Limited seats based on tier
Technical validation from industry leaders speaks volumes. OpenAI trusts Statsig for GPT experimentation. Notion runs their growth experiments on the platform. Figma relies on it for product development. These teams have the resources to build internally or buy any tool - they chose Statsig for a reason.
The cost comparison at scale reveals the full picture. At 10 million monthly events, Amplitude pushes you to enterprise contracts with opaque pricing. Statsig maintains transparent, linear pricing that scales predictably. No surprises. No sudden jumps. Just clear costs that finance teams can actually budget for.
Closing thoughts
Choosing between Statsig and Amplitude comes down to your philosophy on experimentation. If you want a unified platform that handles everything from feature flags to advanced statistics - and does it at half the cost - Statsig makes sense. If you're already deep in the Amplitude ecosystem and just need basic A/B testing, their add-on approach might work.
The market is moving toward unified platforms. Teams are tired of juggling multiple tools, multiple vendors, and multiple bills. They want to ship faster with less overhead. That's exactly what Statsig delivers.
Want to dig deeper? Check out Statsig's interactive demo or read how OpenAI uses the platform to test AI features at scale. You can also explore the detailed pricing calculator to see exactly what you'd pay based on your usage.
Hope you find this useful!