Introduction

In today’s fast-paced digital world, experimentation has become indispensable for companies that wish to improve their products and services through iterative learning. Whether it’s A/B testing, multivariate testing, or feature flag testing, these programs allow businesses to validate hypotheses, optimize user experiences, and drive growth in a data-driven manner. For companies embracing a product-led iterative mindset, experimentation isn’t just a luxury—it’s a critical function for staying competitive.

However, the key question remains: how do companies implement such experimentation programs efficiently? Should they build an in-house experimentation platform or buy a third-party solution? This decision involves not only technical considerations but also financial, operational, and strategic factors.

For companies whose core focus isn’t tech product development, or those without dedicated development resources, a third-party client-side solution can be a great enabler. Many of these platforms are plug-and-play, allowing marketing and product teams to easily create and deploy tests (mostly content or light changes on web) without relying on engineering. These tools typically use JavaScript snippets or SDKs that are easy to integrate into websites and apps, making it possible to run tests without needing to modify backend systems. This flexibility can accelerate the adoption of experimentation, providing a quick way to start running A/B tests and optimizing user experiences.

However, this convenience can come at a cost. Client-side experimentation can introduce performance issues—like increased page load times or flickering effects (though ABsmartly allows you to configure for zero lag and no flickering), where users briefly see the original content before the test variation loads. Additionally, some client-side tools can be limited in scope, often focusing only on UI changes rather than deeper, backend-driven experiments. For businesses that need to test backend logic, algorithms, or complex user flows, server-side experimentation—which executes experiments before content is delivered to users—offers greater flexibility, control, and scalability.

The maturity of a company, both in general and in terms of its experimentation capability, is a critical factor in determining whether to build, buy, or adopt a hybrid approach. Companies in the early stages of experimentation maturity often opt for third-party solutions because they provide an easy-to-use interface and allow them to start testing quickly, equally those with more experience may opt for a more sophisticated third-party platform. More mature companies with a strong in-house tech presence historically preferred building custom platforms to gain full control over the experimentation process, and were mainly running server-side tests that require tight integration with internal systems and remote feature flagging, though these features are increasingly offered by emerging platforms.

Additionally, for companies which have embraced a continuous delivery culture, the integration of feature flagging and experimentation tools is essential. Feature flags enable teams to dynamically control which features are shown to users, facilitating gradual rollouts, instant test modifications, and immediate rollback of features if an experiment underperforms. This agility ensures that even the smallest updates can be deployed seamlessly, making experimentation an integral part of the product development process.

In this article, we will explore the differences between building an in-house platform, buying a third-party solution, or pursuing a hybrid approach. We’ll also discuss how companies can align this decision with their experimentation maturity, technical resources, and overall business goals, emphasizing the importance of continuous delivery and the role of remote feature flags in driving experimentation success.

Understanding Build vs Buy (or Hybrid) options for Experimentation Platforms 

When deciding on an experimentation platform, companies often face three main options: Build, Buy, or a Hybrid approach. Each has its own set of advantages, challenges, and financial implications. Before diving into a detailed analysis of each option, it’s important to first define these approaches:

• Build: Developing a custom in-house platform tailored to your specific business needs. This requires significant technical expertise, time, and resources, but allows for complete control over features, scalability, and integration with existing systems.

• Buy: Purchasing a third-party experimentation solution that’s ready-made. These solutions are easy to implement and come with built-in functionalities, often making them ideal for fast deployment. However,these come with varying degrees of sophistication - some offering quick and easy solutions whilst others offer more technical features aimed at product teams.

•  Hybrid: A combination of both approaches, where some parts of the experimentation system are built in-house (often core capabilities like server-side experimentation) and others are purchased (such as a feature toggle system or client-side solution). This provides flexibility, allowing businesses to scale experimentation efforts while balancing costs and development efforts.

Key Factors in Decision-Making

Deciding between these options involves weighing various factors:

• Technical expertise: Do you have the in-house talent required to build and maintain a custom platform?

• Time to market: How quickly do you need to start running experiments and/or increase volume and velocity?

• Scalability: What are your long-term plans for experimentation? Will the solution scale with your growth?

• Total cost: What are the immediate and long-term costs, including ongoing maintenance, resources or licensing fees?

• Flexibility: How much control do you need over the experimentation process?

With these initial considerations in mind, we’ll explore each option in more detail, discussing the financial implications, benefits, and drawbacks of each.

1.1 Build In-House

Building a custom experimentation platform gives companies complete control over the system’s features, scalability, and integration. This is often a good option for organisations with complex needs, such as server-side experimentation, or those requiring deep integrations with other internal tools and data systems.

Advantages:

• Customization: Tailored to meet specific business needs.

• Long-term savings: Once built, there are no recurring license or usage fees, (as compared to vendors who may charge more for volume of experiments.) However, in-house expertise come at their own cost.

• Control: Full ownership and control over data, features, and system architecture: Unlike some vendor solutions, which require data sharing for advanced analysis or impose restrictions on data extraction (often with fewer options outside of enterprise plans), an in-house solution enables seamless integration with existing internal systems and offers greater flexibility. Some emerging platforms also support Warehouse Native, giving the customer full ownership of the data.

• Disadvantages:

• High upfront costs and ongoing maintenance: Developing a custom platform demands significant initial investment, covering salaries for specialized staff and infrastructure setup. 

• Extended time to market: Building from scratch takes considerable time, often 9-12 months, before a platform is operational. This delay means teams may miss critical opportunities to optimize products and gather insights quickly, as third-party solutions can often be implemented in weeks.

• Maintenance burden: Maintaining and evolving an in-house platform, requires dedicated resources for continuous evolution of the product based on the business needs, bug fixes, and performance improvements to keep up with the scalability needs. 

• Resource and Talent Constraints: Building in-house demands niche skills in data engineering, backend development, data science and UX. Talent for these roles can be costly and challenging to recruit and retain, especially for non-tech-focused companies.

1.2 Buy (Third-Party Solution)

Purchasing a third-party experimentation platform allows companies to start testing immediately, offering pre-built features and minimal setup requirements. This is an attractive option for companies looking for a fast, scalable solution which don’t always need significant in-house technical expertise.

Advantages:

• Quick time to market: Experiments can be launched within days or weeks, after integration and training for the new tools and processes.

• Lower upfront costs: No need to invest in development or maintenance.

• Simplicity: Most third-party tools are designed to be easy to use, even for non-technical teams, with client-side (and server-side) deployment options for quick wins.

• Sophistication: Some emerging third-party platforms have started offering sophisticated functionality such as feature flags with config, advanced statistical methods, quasi-experiments, multiarm bandits, and so on, rivalling or even surpassing in-house platforms.

Disadvantages:

• Ongoing costs: Besides the initial and recurrent licensing fees, with some platforms the cost scales with usage ), which can become expensive as experimentation efforts grow. 

• Limited flexibility: Some of the less sophisticated out-of-the-box solutions may not provide the level of customization required for more complex experiments, particularly server-side tests. Depending on the solution and the maturity of the company, the integration can be more challenging than expected.

• Vendor lock-in: Dependence on the vendor for new features and updates and, in some cases, having your dataset as part of the vendor solution without an easy way to get access to it or to migrate from. However, some vendors let you export the data, store the data in a dedicated environment, or even support your own data-warehouse.

1.3 Hybrid Approach

A hybrid solution is a blend of both in-house development and third-party tools, where companies leverage core components from vendors but build custom parts on top to balance the benefits of rapid deployment with long-term cost savings and customization. 

This approach is beneficial when certain functionalities—either not covered by external platforms or not central to the in-house product vision—are still relevant for the business. It allows teams to address these needs quickly, achieving faster time-to-market and accelerating time-to-value, while continuing to develop the core internal solution.

For example: 

1. A company might use a client-side solution from a third-party vendor to handle marketing optimizations business needs while focusing on developing their server-side only platform that supports their more complex product experiments. 

2. Or in a case where they don’t yet have a feature toggle service, but while they are building their solution they already want to take advantage of such a system, as part of their deployment approach. 

3. Or when seamless integration with proprietary systems is required, allowing partial in-house control - for example, ensuring that the tracking system is transversal and uniquely centralized in the company data warehouse.

4. Or when advanced features, like switchback allocation, are necessary, but not supported by the the third-party

Advantages:

• Flexibility: The company can prioritize internal development for high-value, complex tests while using third-party tools for experiments that can be run out of the box.

• Phased investment: Start small with external tools, reducing upfront costs, while developing in-house capabilities over time.

• Scalability: This approach offers flexibility for two different scaling paths. One path is moving operations fully in-house: as the internal platform grows stronger, the organisation can gradually reduce dependency on external tools. The other path is phasing out internal development: if third-party solutions become more capable than your internal tools, it permits scaling back internal development efforts and redirecting those developers to focus on core business projects instead.

Disadvantages:

• Integration challenges: Combining third-party tools with in-house systems requires robust data tracking and feature management to ensure everything works seamlessly. Increased complexity in managing two systems (external platform and internal components) and to ensure they are compatible and interoperable. Sensitive customer data transfer to vendor systems is needed in many solutions.

• Long-term planning: The hybrid approach requires a careful roadmap to ensure the transition from third-party to in-house tools,or vice-versa, happens smoothly without disrupting testing efforts. Potential duplication of efforts between in-house development and external systems.

1.4 Summary

Evaluating Based on Experimentation Maturity and In-House Development Capabilities

When deciding on the best approach—whether to build, buy, or adopt a hybrid model—it’s crucial to align your choice with your company's experimentation maturity and in-house development capabilities. These two factors significantly influence not only the type of platform you need but also the value you'll extract from your investment.

2.1 Experimentation Maturity Levels

Experimentation maturity refers to how advanced a company is in its ability to run, manage, and act on insights from experiments. Broadly, we can categorize companies into three levels:

• Basic (Emerging Stage): At this stage, companies have just begun their experimentation journey. They typically run a limited number of client-side A/B tests, often focusing on UI or marketing optimization. The processes are usually manual, and teams lack a systematic approach to experimentation.

• Intermediate (Scaling Stage): Here, companies have established some foundational processes for experimentation and are running a more significant number of tests. There is an appetite to run server-side experiments to influence product features, but the company may still be reliant on external platforms for most of its capabilities.

• Advanced (Mature Stage): Companies at this stage have fully embedded experimentation into their product development lifecycle. They run both client- and server-side experiments, leverage advanced statistical models, and integrate data from multiple sources. They might even be looking at real-time testing.

Which Approach Fits Best Based on Experimentation Maturity?

• Basic Stage: For companies at the beginning of their experimentation journey, buying a third-party tool is often the best choice. Plug-and-play solutions provide a low barrier to entry, allowing these companies to get up and running quickly with minimal technical resources. A client-side solution may be enough to support marketing efforts, which is often the focus at this stage. However, some third-party solutions offer both client and server side capabilities, making them more appropriate for product teams who are used to experimentation.

• Intermediate Stage: As companies grow and start scaling their efforts, a hybrid approach could be ideal. They might continue using client-side tools for front-end and marketing-related tests, or experiments that can be run right away, but start developing in-house capabilities for experiments or features that are not available out of the box. Building the ability to run advanced experiments allows for deeper product experimentation while maintaining flexibility through third-party tools. Ensuring a unified data layer is crucial here to maintain consistency in tracking and result interpretation across different systems.

• Advanced Stage: Mature companies that run hundreds of experiments, often server-side, and need full control over data, logic, and integrations, have historically benefited from a build approach. This allows for customized platforms that are designed to handle complex, high-volume experimentation workflows. It also ensures that there are no limitations imposed by external platforms, enabling continuous delivery cycles and high-scale testing across products. However, the more sophisticated, emerging platforms on the market offer capabilities that even very advanced in-house systems struggle to rival. That gap will, most likely, continue to grow over time.

2.2 In-House Development Capabilities

The technical capabilities of your organization are equally important as your experimentation maturity when choosing between build, buy, or hybrid approaches. The level of in-house talent, the complexity of the experiments you want to run, and the resources available will all factor into your decision.

Evaluating Based on In-House Development Resources

• Minimal Tech Resources: If your company has limited tech capabilities or lacks dedicated resources for product experimentation, a buy approach is more practical. Even though the long-term costs may be higher, the low upfront investment and immediate deployment of a third-party solution can be a key enabler for less tech-savvy organizations. Additionally, some of the simpler third-party solutions that focus on client-side testing often cater well to teams with limited technical knowledge, such as marketing or business teams.

• Growing Tech Resources: Companies with some in-house technical resources might consider a hybrid approach. As their teams develop and become more capable, they can take over more core functionalities—such as building a server-side experimentation platform or building on top of their external platform—while continuing to use external platforms for less critical or more niche requirements (e.g., client-side tests or feature flag management). The hybrid model allows for gradual investment in development, spreading costs over time and scaling as internal talent grows.

• Strong Tech Resources: For companies with a robust development team that can handle ongoing platform maintenance, and where experimentation is a core part of the product development process, building in-house used to be the best solution. It offers full control over custom features and allows deep integrations with existing systems. Nowadays, these capabilities are rivalled by more sophisticated third-party platforms and a hybrid approach may be the way to go.

2.3 Balance Between Technical Capabilities, Maturity and Experimentation Goals

It's also essential to consider whether your business model and long-term goals align with developing technical capabilities in-house. For companies where technology is a core differentiator (e.g., tech or SaaS companies), having a sophisticated (in-house or third-party) experimentation platform may be critical to building competitive advantage. However, for businesses where technology is not a primary focus—such as retail or marketing-led organizations—the focus may be more on agility and speed to market, making some of the simpler external solutions more appealing.

Advantages Based on Development Capabilities and Maturity

• Basic Stage & Minimal Resources: Buying external tools ensures quick setup and low technical overheads, enabling companies to start running experiments quickly and effectively, whether they chose simple tools for marketing-driven experiments, or more sophisticated tools for product teams. 

• Intermediate Stage & Growing Resources: A hybrid model enables companies to scale while balancing development costs and external dependencies. This is a transition phase where companies gain control over their more complex tests while still relying on external platforms for most of the workflow.

• Advanced Stage & Strong Resources: Building an in-house platform used to be the go-to solution for mature companies with a dedicated tech team, and it can still be very effective. They can build exactly what they need and have the resources to support ongoing enhancements, allowing for more sophisticated experimentation strategies at scale. However, with newer platforms rivalling, sometimes even out-performing in-house tools, some organisations who would previously have built in-house are looking to these third-party platforms now.

Choosing the right experimentation platform is a balancing act between where your company stands in terms of experimentation maturity and its in-house technical capabilities. Companies in the early stages with limited resources may find significant value in buying a simple third-party solution, while companies with more developed testing programs and technical expertise can reap the benefits of picking a sophisticated third-party platform that fosters experimentation culture and build extra features on top that are core to their product. Hybrid solutions provide a middle ground for companies in transition, allowing for both quick wins and long-term development.

By aligning your choice with your company’s current needs and capabilities, you can ensure that your investment in experimentation tools is effective, scalable, and conducive to achieving your long-term business goals.

Key Factors in the Build vs Buy (or Hybrid) Decision

When deciding between building, buying, or adopting a hybrid approach for an experimentation platform, businesses must consider a wide range of factors beyond just ROI and all the aspects explored above to be able to produce a proper business case. Below, we'll explore these key factors in more detail to help guide companies through the decision-making process.

3.1 TTV (Time to Value)

Time to Value (TTV) is the period between making an investment and seeing its first meaningful benefits. For experimentation platforms, TTV refers to how quickly you can start running tests and gaining actionable insights.

• Build: Developing an in-house platform has a long TTV due to the time required to design, build, and implement the system. It can take 6 to 12 months (or longer) to get the platform fully functional, depending on complexity. This extended timeline can delay the ROI for companies needing quick experimentation.

• Buy: With pre-built third-party solutions, TTV is usually much shorter, often in the range of weeks or even days. These platforms come with pre-configured features and out-of-the-box integrations, allowing businesses to start experimenting quickly.

• Hybrid: The hybrid approach balances these two extremes. You can start using a third-party tool for immediate testing needs (with a short TTV) while developing extra features in-house in parallel. This allows you to see value faster for out of the box experiments while gradually building out other needed capabilities.

3.2 TCO (Total Cost of Ownership)

Total Cost of Ownership (TCO) includes all the costs associated with the platform over its lifetime, not just upfront development or licensing costs. This factor takes into account ongoing maintenance, support, updates, and scalability as your experimentation needs grow.

• Build: While in-house development requires a high upfront investment, the long-term costs (maintenance, upgrades, and scaling) will depend on the complexity and volume of experiments. For companies running high-frequency server-side tests, in-house systems can be (but aren’t always) more cost-effective in the long run, despite ongoing operational costs.

• Buy: The initial investment is significantly lower. With companies that tie cost to the number of users, tests, and impressions, costs may rise. Some subscription models can get expensive as your experimentation efforts scale, and costs are typically on an annual basis. However, not all third-party platforms scale costs in the same way.

• Hybrid: In a hybrid model, you have moderate upfront costs combined with ongoing subscription fees. Long-term TCO can be optimized by finding the right mix of real-time events or even going with a warehouse native third-party that only ingest exactly the data that is needed for each experiment.

3.3 Flexibility and Customization

The ability to customize the platform to meet your business’s unique needs is crucial, especially as your experimentation strategy evolves.

• Build: In-house solutions offer maximum flexibility. You can tailor every feature, experiment type, and integration according to your specific requirements. This can be important for companies needing complex experiments, like switchback testing, quasi-experiments or have a preference for a statistical method that is not supported by a third-party.

• Buy: Simple third-party platforms often have some customization options, but they are generally designed to serve a broad range of use cases. Some legacy off-the-shelf tools are optimised for client-side experiments, which can be restrictive. However, the landscape of third-party tools has changed significantly in recent years, with several going beyond the capabilities, both client and server-side, that many in-house platforms offer.

• Hybrid: A hybrid approach offers a balanced level of flexibility. You can use the pre-configured capabilities of a third-party tool for simpler, fast-deployment tests, while customizing an in-house solution to meet your more specific needs.

3.4 Integration with Existing Systems

Your experimentation platform needs to work seamlessly with your existing infrastructure, including your data analytics, feature flag systems, and deployment pipelines.

• Build: Building integrations in-house means you can fully integrate your platform with all other systems, ensuring tight coupling with your data layer, continuous delivery pipeline, and internal tools. This is ideal for companies that want full control over the experimentation lifecycle.

• Buy: Third-party platforms often provide integrations with popular tools and systems, but some may lack the depth of integration. If your organisation prioritises  server-side experimentation, where precise control over backend systems is crucial, it is important to choose one of the more sophisticated platforms. When looking to buy, the choice of tool will be driven by which team is the user eg. Marketing or Product.

• Hybrid: A hybrid approach allows you to use external tools for quick wins in areas where deep integration is less critical (e.g., client-side or marketing tests), while custom-building integrations for more complex or backend-focused experiments.

3.5 Scalability

As experimentation grows within your company, the platform’s ability to handle increased testing volume and complexity becomes critical.

• Build: In-house platforms can be built to scale as needed, provided you have the development resources to support and maintain them. The ability to control scaling infrastructure ensures you can run a high volume of tests without performance degradation, but it requires continuous investment in resources and scaling technologies.

• Buy: Most third-party tools offer scalable infrastructure as part of their services, but with some the cost increases as your usage grows. This may be a consideration for companies running large-scale experimentation programs with high traffic volumes.

• Hybrid: The hybrid approach provides a scalable solution in phases. You can start with the scalability offered by third-party tools and transition to in-house scaling as your needs and capabilities expand.

3.6 Data Ownership and Security

Data security and ownership are critical concerns, especially for companies dealing with sensitive customer data or operating in regulated industries.

• Build: An in-house solution provides complete control over data, ensuring that all experimentation data remains within the company. This can be crucial for companies with strict compliance or data privacy requirements.

• Buy: Third-party solutions often host your experimentation data on external servers. While most vendors provide strong security measures, some companies may find this lack of control problematic, particularly in highly regulated industries. Some third parties allow you to choose the region where the data is stored, and now that warehouse native is a thing, the data can even live on your own servers.

• Hybrid: A hybrid model can offer flexibility in this regard, allowing companies to keep sensitive data in-house for core experiments while using third-party tools for less sensitive tests, such as marketing experiments.

3.7 Experimentation Culture and Organizational Readiness

Finally, the organizational mindset and culture surrounding experimentation will heavily influence which option makes the most sense for your company.

• Build: Companies with a mature, ingrained experimentation culture—where testing and learning are core components of product development—are more likely to benefit from building an in-house solution. These organizations typically have the support systems, leadership buy-in, and development resources required for such a significant investment.

• Buy: For companies just starting out with experimentation, buying an external tool can help jump-start their efforts, particularly if they lack a deeply ingrained testing culture. Equally, for businesses who value experimentation culture but rather not build in-house, or struggle to build a platform as capable as some on the market, they may choose one of the emerging platforms to build on and elevate existing experimentation programs.

• Hybrid: The hybrid approach works well for organizations in transition, where some teams are more advanced in their experimentation efforts while others are just beginning. A hybrid solution allows experimentation to grow with the organization, balancing speed and complexity.

3.8 Summary

Conclusion

Deciding between building, buying, or adopting a hybrid approach for an experimentation platform is a multifaceted decision that requires careful evaluation of your company’s needs, technical capabilities, and strategic goals. While it may be tempting to focus solely on the financial implications such as ROI, TCO, and TTV, the decision must be aligned with the company's long-term vision for experimentation and its role within the organization.

For tech-savvy companies where experimentation is a strategic priority, building an in-house platform used to be the go to solution to offer flexibility, data control, and scalability. However, this path demands a high level of technical expertise, a mature experimentation culture, and significant upfront investment. Some companies also find that their in-house teams struggle to rival some of the newer, technically sophisticated third-party platforms that have been designed specifically with product teams in mind.  And for companies looking for a simpler, marketing-driven platform, there are plenty of third-party solutions that can provide quick wins, especially for businesses just starting out or those that don’t have experimentation as a core competency. Both the solutions for marketing teams, and the more sophisticated platforms for product teams allow for rapid deployment and immediate value but some may introduce long-term constraints in terms of flexibility, scalability, and data ownership.

A hybrid approach can offer the best of both worlds, allowing companies to build internal capabilities for mission-critical experimentation while leveraging third-party solutions for less complex use cases like client-side testing or marketing optimizations. This strategy can be especially useful for companies that are growing their experimentation culture and gradually increasing their technical capabilities.

However, no matter the choice of platform, a company's success in experimentation will ultimately depend on its cultural adoption of testing and the integration of experimentation into its product development processes. Even the most advanced platform will fail to deliver value if it’s not supported by the right mindset, practices, and cross-functional collaboration. Creating a center of excellence, fostering an iterative approach to product development, and continuously improving how experiments are conducted are just as important as the platform itself.

In the end, the right decision should be made on a case-by-case basis, factoring in the real-world numbers for your specific company. The goal is to not only pick the best platform but also to embed experimentation deeply into the DNA of your organization—turning data-driven decisions into a cornerstone of your company's growth and innovation.