Better Stats, More Flexibility: How Statsig compares to Convert

When your experimentation program hits its limits, you face a tough choice: stick with a tool that's outgrown your needs or migrate to something more powerful. Teams using Convert know this pain well - what starts as simple A/B testing quickly demands feature flags, deeper analytics, and infrastructure that scales beyond website optimization.

The fundamental question isn't whether Convert works (it does, for specific use cases), but whether its approach aligns with modern product development. As engineering teams take ownership of experimentation, they need platforms built for code-first workflows, statistical rigor, and unified tooling. That's where the Statsig vs Convert comparison gets interesting.

Company backgrounds and platform overview

Convert has been around since 2008, carving out a niche as the go-to A/B testing platform for agencies and marketing consultants. Its 15-year journey tells a consistent story: make testing accessible to non-technical teams. The platform does exactly what it promises - straightforward website experiments without the complexity.

Statsig took a different path when it launched in 2020. The founding team came from Facebook's experimentation infrastructure group, where they'd built systems processing billions of daily events. They saw how fragmented tools created bottlenecks: product teams juggling LaunchDarkly for flags, Optimizely for tests, and Amplitude for analytics. Their solution was radical simplicity through unification - one platform handling everything from feature rollouts to statistical analysis.

The customer lists reveal how these philosophies play out. Convert attracts CRO consultants and marketing teams who need reliable testing for landing pages and conversion funnels. Statsig draws engineering-first companies like OpenAI, Notion, and Brex - teams running hundreds of concurrent experiments across their entire product surface.

Don Browning, SVP at SoundCloud, captured the distinction: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration." It's not that other tools failed; they just couldn't deliver the unified experience modern product teams demand.

Feature and capability deep dive

Experimentation capabilities

Both platforms handle A/B and multivariate testing, but that's where similarities end. Convert focuses on what marketing teams need: split URL testing, visual editors, and conversion tracking. It's reliable for website optimization - exactly what it was designed for.

Statsig builds on these basics with capabilities borrowed from big tech experimentation platforms:

• Sequential testing that lets you peek at results without inflating false positive rates

• CUPED variance reduction to detect smaller effects with the same sample size

• Automated heterogeneous effect detection surfacing which user segments respond differently

The deployment architecture reveals another fundamental difference. Convert requires routing all experiment data through their cloud infrastructure - standard practice for SaaS tools. Statsig offers both cloud-hosted and warehouse-native deployment, letting privacy-conscious teams keep sensitive data in their own Snowflake, BigQuery, or Databricks instances. Financial services and healthcare companies particularly value this flexibility.

Analytics and developer experience

Convert's analytics stay focused on experimentation metrics: conversion rates, revenue impact, statistical significance. You get what you need for testing, nothing more. External tools handle broader product analytics.

Statsig takes the opposite approach by bundling comprehensive product analytics alongside experimentation. Teams track user journeys, build retention cohorts, and analyze feature adoption - all using the same data powering their experiments. This integration eliminates the data silos that plague most analytics stacks.

The developer experience gap is equally stark. Convert provides JavaScript snippets for web implementation, with limited SDK support for other platforms. Statsig maintains 30+ open-source SDKs covering every major language and framework:

• React, iOS, Android for client apps

• Python, Node, Java, Go for backend services

• Edge computing support via Cloudflare Workers and Vercel

• Real-time streaming through Kafka and Segment

A G2 reviewer noted this advantage: "The clear distinction between different concepts like events and metrics enables teams to learn and adopt the industry-leading ways of running experiments."

Modern products span multiple surfaces - web, mobile, backend APIs, edge functions. Consistent experimentation tooling across this stack matters more than any individual feature. Convert's JavaScript focus works for marketing sites but breaks down when product teams need unified testing infrastructure.

Pricing models and cost analysis

Transparent pricing structures

The pricing philosophies couldn't be more different. Convert charges $399-999/month based on tested users, with hard caps at each tier. Hit 1.2 million tested users annually? That's $399/month. Need more? Time to upgrade.

Statsig's usage-based model charges only for analytics events, with unlimited feature flags included free. A typical 100K MAU company pays around $400/month for the entire platform - experimentation, flags, analytics, and session replay combined. The same company hits Convert's Pro tier at $999/month for testing alone.

But raw numbers tell only part of the story. The real difference emerges when you examine total platform costs:

• Feature flags: Convert doesn't offer them; LaunchDarkly starts at $8,000/year

• Product analytics: Convert requires external tools; Amplitude begins at $49/month

• Session replay: Convert lacks this entirely; FullStory costs $199+/month

Suddenly that $399 Convert plan balloons to $1,500+ monthly across multiple vendors.

Hidden costs and scalability

Convert's tiered pricing creates artificial friction. Multivariate testing? Only available on higher plans. Need more integrations? Another upgrade. These feature gates force awkward conversations about whether advanced testing methods justify the cost jump.

Statsig includes every capability in its free tier - sequential testing, variance reduction, warehouse-native deployment. The 2 million free events monthly support real production usage, not just trials. Convert's 15-day trial barely allows meaningful evaluation before requiring payment.

Scale amplifies these differences. A growing startup might start with Convert's $399 plan but quickly need:

• Feature flags for gradual rollouts ($600+/month)

• Product analytics for user behavior ($500+/month)

• Session replay for debugging ($200+/month)

That's $1,700 monthly for capabilities Statsig bundles at a fraction of the cost. Plus you're managing contracts, integrations, and data pipelines across four vendors instead of one.

Decision factors and implementation considerations

Time-to-value and learning curve

Convert optimizes for immediate results. Marketing teams launch their first A/B test within hours using visual editors and pre-built templates. No code required - just point, click, and start testing. This approach works brilliantly for its intended audience.

Statsig demands more upfront investment. Engineers implement SDKs, instrument events, and design feature flags before running experiments. But this code-first approach paradoxically accelerates long-term velocity. Once integrated, product managers create and analyze experiments independently without engineering bottlenecks.

Notion's experimentation journey illustrates this perfectly. They went from single-digit to 300+ quarterly experiments after adopting Statsig. The initial implementation took weeks, but it unlocked exponential growth in testing velocity. Their product teams now ship experiments as easily as features.

The learning curve reflects each platform's audience:

• Convert trains marketers in testing methodology and statistical concepts

• Statsig assumes familiarity with experimentation but simplifies the infrastructure

Teams with strong engineering support typically see faster time-to-value with Statsig despite the technical implementation. The unified platform eliminates integration complexity that slows down multi-tool setups.

Support and enterprise readiness

Both companies provide dedicated support, but their approaches differ significantly. Convert offers traditional email and phone support across paid plans, focusing on helping marketers troubleshoot tests and optimize conversions.

Statsig connects customers directly with engineers via Slack. Questions about experimental design, statistical methods, or infrastructure scaling get answered by people who built the platform - sometimes the CEO jumps in personally. This technical depth matters when you're pushing platform limits or designing complex experiments.

Sumeet Marwaha from Brex highlighted this difference: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."

Enterprise readiness separates the platforms completely. Statsig processes over 1 trillion events daily with 99.99% uptime SLA for companies like OpenAI and Microsoft. The infrastructure scales automatically - no capacity planning or upgrade negotiations. Convert handles website optimization reliably but lacks the foundation for product-scale experimentation.

Your infrastructure requirements should drive the decision:

• Optimizing marketing websites with limited traffic? Convert handles this well

• Running product experiments across millions of users? You need Statsig's scale

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

The math is straightforward. Convert costs $399+ monthly for A/B testing. Add LaunchDarkly for feature flags ($8,000+ annually), Amplitude for analytics ($600+ monthly), and FullStory for session replay ($200+ monthly). You're spending $1,500-2,000 monthly for capabilities Statsig provides at $400.

But cost is just the entry point. The real advantages compound over time:

Brex reduced data science workload by 50% using Statsig's automated analysis. Their data team stopped building custom statistical pipelines and focused on strategic insights instead. Convert's manual reporting would have required hiring additional analysts.

Notion scaled from single-digit to 300+ experiments quarterly without changing platforms. They run sophisticated experiments across web, mobile, and backend services using consistent tooling. Convert would have required separate solutions for each surface.

The infrastructure gap becomes insurmountable at scale. Statsig processes trillions of events with 99.99% uptime while offering warehouse-native deployment for data sovereignty. Convert works well for its niche but can't support modern product experimentation demands.

Platform unification creates unexpected benefits. Engineers write feature flags that automatically become experiment variants. Product analysts access the same metrics definitions across flags, experiments, and analytics. This shared context eliminates the translation errors plaguing multi-tool setups.

Closing thoughts

Choosing between Statsig and Convert isn't really about comparing feature lists - it's about matching tools to your experimentation ambitions. Convert serves marketing teams who need reliable website testing without technical complexity. It does that job well.

Statsig targets a different problem: how do you run experimentation at the speed of modern product development? The answer requires unified tooling, statistical sophistication, and infrastructure that scales automatically. For engineering-led teams building data-driven products, that combination proves transformative.

Want to dig deeper into experimentation platforms? Check out the Statsig blog for detailed comparisons with other tools, or explore their customer case studies to see how companies like OpenAI and Notion structure their testing programs.

Hope you find this useful!