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Why buying an experimentation platform makes more sense than building one
For most organizations, buying an experimentation platform today is the smarter choice.
“Building your own experimentation platform made a lot of sense… five years ago,” says our customer Jared Bauman, Engineering Manager - Core ML at Whatnot, who previously worked on DoorDash’s in-house platform.
Over the past few years, several companies have moved away from in-house experimentation tooling and turned to Statsig to scale their experimentation cultures. Notion increased their experimentation velocity by 30x within a year. Lime went from limited testing to experimenting on every change before rolling it out.
If you're weighing the build vs. buy decision, here are some key factors to consider:
1. Time to market and launch velocity
It can take years just to build the table stakes for an experimentation platform. That means you're slowing down innovation on your core product while trying to recreate a system that already exists.
The basic foundation for a reliable experimentation platform has several key components such as:
Server & client-side SDKs for seamless experimentation across all surfaces without impacting performance
Data logging, ingestion and processing services and/or integration with your data warehouse
Randomization functions for accurate user allocations in different scenarios
Experiment result computations including metric lifts, p-values, confidence intervals, and other statistical calculations
Automated checks (like power analysis, balanced exposures) to ensure experiments are properly set up and issues are identified proactively
Statistical corrections to account for outliers, pre-experimental bias, and differential impact, etc. to provide trustworthy results
Variance reduction (CUPED, winsorization)
Experimentation techniques like sequential testing, switchback testing, geo-based tests etc.
Support for running concurrent, mutually exclusive experiments and holdout groups to measure long-term impact
UI that helps stakeholders visualize results easily and explore data with custom queries
There's also the issue of overfitting. Internal platforms are often built for today's needs but struggle to scale as new product areas emerge. A purpose-built platform like Statsig removes these bottlenecks, allowing you to focus on shipping features faster without the burden of innovating on the experimentation platform.
2. Total cost of ownership (TCO)
Teams often underestimate the investment required to build and maintain a state-of-the-art experimentation platform. These costs can add up. You need to hire specialized talent to build and maintain the platform, and constantly optimize infrastructure costs. Below is a summary of key cost pillars to consider:
Engineering development costs: Hiring a team of engineers, including specialized infra and SDK engineers to build and scale the platform.
Data team: Data Scientists and Data Engineers to develop the core stats methodologies and metric computation frameworks.
Maintenance & support: It’s not just a one-time build cost. You’ll also need the team to maintain the platform, ensure uptime, assist experimenters and address issues as they arise.
Infra costs: As event volumes scale, cloud and warehouse costs can rise steeply. Vendors like Statsig process trillions of events per day and continuously optimize data pipelines. These efficiencies translate into lower compute and warehouse costs for customers.
For small to midsized companies, it can take a couple of years with a team of five engineers and a couple of data scientists just to build the barebones of an acceptable experimentation platform.
Then, consider larger companies like Bing (Microsoft). Ron Kohavi (former Microsoft CVP and Technical Fellow) noted that while Bing now runs thousands of experiments per month, it actually took six years (from 2008 to 2014) to scale its experimentation program.
Companies like Facebook invested hundreds of thousands of engineering hours to build systems that give their engineers the autonomy to move fast and test every change. Of course, tools like Statsig didn’t exist back then and these companies invested heavily to build their own systems. But today, buying a third-party platform is the faster, smarter choice—saving you years of effort and enabling teams to experiment at scale from day one.
3. Advanced capabilities and access to world-class R&D
As you scale to hundreds of experiments per month and expand to new use cases, you need more than just table stakes features. Keeping up with what’s “best-in-class” is much harder if you're building in-house.
Vendors like Statsig partner with top experimentation-led companies. We tackle the broadest range of experimentation challenges across industries and co-create features with our customers. This means our platform continuously improves, and you get access to this R&D today and always—without spending years trying to catch up.
For example, over the past year we’ve shipped several advanced capabilities that you won’t find in a typical experimentation platform:
Stats methodologies such as stratified sampling, differential impact detection, interaction detection, Benjamini Hochberg procedure etc.
Meta-analysis to gain aggregated insights from all the experiments you’ve run
AI prompt experiments to test different prompts and optimize your AI applications
In addition to this innovation, you also get access to Statsig’s world-class team of Data Scientists and Engineers, who serve as thought partners and help you scale your experimentation culture.
4. Driving adoption and scaling the culture of experimentation
You can’t scale an experimentation culture unless the entire organization follows a standardized process and works with the same data. Everyone should be aligned on the goals and trust experiment results.
There should be no barriers to adoption. That’s why we’ve focused on usability and building a well-documented platform that enables developers to quickly ramp up and start shipping every change as an experiment.
A platform like Statsig brings legitimacy with a verified metrics catalog and full transparency, so you’re not questioning results every time. We’ve also built various features directly into our core product workflows to make it easier to scale experimentation:
Templates, reviews and team-based settings to enforce best practices
Knowledge base to document learnings and create an institutional memory
Experiment quality score to quantify trustworthiness of experiments
Inline discussions & comments to foster collaboration
Take the case of HelloFresh. They now run hundreds of trustworthy experiments with Statsig. Instead of spending time building the above capabilities in house, their experimentation team can focus on people-centric initiatives, like a “bar raiser” program to strengthen their experimentation culture.
What should experimentation teams invest in?
Jeff Bezos used the analogy "focus on what makes your beer taste better" to illustrate the benefits of outsourcing IT infrastructure—allowing companies to concentrate on what truly differentiates them.
A similar principle applies to experimentation. Companies should focus on what only they can do—where they have unique context. This includes building trustworthy metrics that reflect business impact, prioritizing the right experiments to run, and diving deeper into the insights. Focus on business problems and strategic impact rather than spending time on tooling that can be abstracted away.
When SoundCloud moved from their in-house experimentation tooling to Statsig, here’s how their Director of Product summed up the impact within a year:
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