The evolving landscape of experimentation tooling: Build vs. buy

The build vs. buy decision for experimentation tools seemed to be settled. For around a decade, it just depended on the size of your company: early-stage companies built in-house, medium-stage ones would switch to a vendor solution, and later stages would revert to building. But what had been a stable answer started changing around 2022 with the introduction of warehouse metrics—and that change is still going.

I’ll cover what you need to know about how companies used to make this decision, why this decision has changed, and how it will continue to evolve in the future.

Early stages: Building in-house

In the previous decision model, small teams who needed feature flags would often opt for open-source solutions or build their own simple systems. This was a feasible solution with minimal effort, even for a single person. Free or minimal time to build was the route to go.

Upon achieving product-market fit and desiring to experiment within the product, experimenters found a similar paradigm: a simple randomizing service could be built and integrated with other tools, such as product analytics. While seemingly straightforward, though, these custom solutions would require careful setup to avoid issues like SRM (sample ratio mismatch) and to ensure proper metric tracking. They also involved stitching a plethora of disparate tools together.

As experimentation needs grew—going from few experiments and one team to many experiments and many teams—these simple services would no longer scale. This could be due to:

• Data limitations: Inability to analyze diverse metrics, such as revenue, within the existing product analytics tool. Or requiring a data scientist to pull the results for each test, requiring a large time commitment.
• Team coordination challenges: Difficulty managing concurrent experiments across multiple teams and preventing conflicts.
• Lack of advanced features or formalized processes: Absence of mechanisms to determine experiment duration, monitor all relevant metrics, or ensure experiment health.
• Process challenges: Cumbersome to manage and standardize usage across an array of tools.

At this point, the in-house tool, once sufficient for a small team, became inadequate.

Mid-stages: Buying a vendor solution

Faced with scaling challenges, startups would then turn to experimentation vendors. Given that startups typically prioritize time over cash and face high opportunity costs, building in-house solutions for non-core competency technology was often seen as inefficient.

Furthermore, developing a robust experimentation platform demands a diverse range of expertise on-staff: data science, data engineering, product management, back-end engineering, and UX. Internal tools often suffer in areas like UX due to a lack of specialized talent. Consequently, mid-sized startups found it more logical to purchase a vendor solution.

Later stages: Bringing it back in-house

Interestingly, once a company reached a significant size, they would often transition away from vendors and build their own in-house solutions again. This shift was driven by two primary factors:

1. Internal expertise: Larger companies had grown to a size where they could assemble a team with the necessary expertise to build a sophisticated experimentation platform.
2. Unique needs and culture of experimentation: Companies with a deeply ingrained culture of experimentation often developed unique requirements that vendor solutions couldn’t fully address. This included bespoke needs for metric measurement, alignment with existing BI tools, specific configuration processes, and customized result reporting for leadership.

This cycle, where a company might build in-house early on, buy from a vendor as they got serious about experimentation, and then bring it back in-house at scale, was the conventional approach. The build versus buy decision could be easily deduced based on the company stage.

The shift to warehouse data: Buying prevails longer

This build versus buy cycle was fundamentally altered with the advent of warehouse-based solutions around 2022. By being built directly on the same data residing in a company’s data warehouse, warehouse-based vendors eliminated one of the key reasons for building in-house at scale: the need for customized data infrastructure.

This innovation led to a notable trend: companies that had previously built in-house products began moving back towards vendor solutions.

Vendors, empowered by warehouse-based capabilities, could now more effectively meet the complex needs of large-scale experimentation programs. There was also a better understanding and recognition of the specific requirements of teams conducting experiments at this level.

In some cases, companies moved their infrastructure wholesale to vendors offering warehouse-native solutions. In other cases, companies used their in-house tools as front ends custom designed for their company process and built on vendor APIs.

The emerging landscape: Extending the “buy” phase further with integrated platforms

While very early-stage companies may still face an initial “build or buy” choice, the market now leans heavily toward “buy”—and stays there for much longer. This shift shows no sign of abating.

Teams are finding pure warehouse-native solutions, while powerful, often limit self-service experimentation. They typically require heavy data science involvement to define and maintain the metrics catalog, plus data engineering to optimize warehouse tables. Data delays can further cause faulty rollouts and slow analysis.

The sweet spot is a platform that automatically captures product events—without requiring data science instrumentation—while pulling north star metrics from the warehouse. This pairing delivers a truly self-service experience that scales from small teams to global enterprises.

Integrated analytics platforms make this possible. By combining experimentation, analytics, targeting, and reporting in one system, they eliminate the engineering burden of keeping disparate tools in sync. The result: less maintenance, faster onboarding, and lower operational risk.

Amplitude in particular goes further by minimizing integration work from the start. With a single line of code, teams can enable analytics, web experimentation, session replay, and personalization in one place—avoiding the tangle of separate SDKs, pipelines, and QA processes.

Another driver of the extended “buy” phase is the pace of vendor innovation. Amplitude ships new capabilities at a speed few internal teams could match, incorporating best practices from thousands of experiments into a self-serve workflow that lets teams move faster without waiting on engineering or data science backlogs.

In short: with platforms like Amplitude, scaling experimentation is easier than ever—making a return to “build” a far less compelling option for the long term.