Build vs. Buy: Deciding the Best Approach for Your Recommender System

Violeta Milarova & Ondrej Fiedler

Mar 12

When it comes to deciding between buying a recommender system and building one from scratch, the choice isn’t always straightforward. Both options come with their own set of pros and cons, and the right answer depends heavily on your team, resources, and long-term goals. But if you're weighing the decision, it helps to break things down into a few key factors: time, expertise, cost, and control.

The Case for Building In-House

For some businesses, the main appeal of an in-house recommender system is full control. When you build your own, you decide everything, from the algorithms to the data flow to the key metrics. This allows for deep alignment with business goals, ensuring recommendations fit your exact needs rather than relying on an off-the-shelf solution. If your company has highly specific requirements, whether it’s complex personalization logic, proprietary data models, or strict compliance needs, building in-house might seem like the best approach.

An internal system also offers more room for experimentation. While third-party platforms allow some customization, they often have limits on modifying core algorithms or integrating niche data sources. With an in-house solution, your team can test different recommendation strategies freely, optimize for business-specific KPIs, and refine models without external constraints.

That said, building a recommendation system from scratch is a massive investment, not just in development but in ongoing maintenance, infrastructure, and talent. It requires a team with specialized expertise, and hiring top-tier machine learning engineers and data scientists is both difficult and expensive. Only companies with significant resources can realistically afford to maintain a high-performing, custom-built system in the long run.

For businesses where recommendations are central to engagement, conversions, or customer retention, the ability to customize every aspect of the system can be a real advantage. But the cost and complexity mean it’s an option only for those with the budget, the talent, and the long-term commitment to make it work.

Challenges of Building In-House

That level of control and customization can be a major advantage, but it also comes with significant complexity. The reality is that maintaining and refining an in-house system is not a one-time effort but an ongoing process that demands continuous adjustments and technical oversight. Even small changes, such as boosting specific content or fine-tuning ranking criteria, often require direct involvement from data scientists or developers. If these adjustments necessitate model retraining, performance evaluations, or infrastructure updates, it can take days or even weeks before the update goes live.

In contrast, SaaS solutions often provide user-friendly web interfaces where product teams can tweak recommendations instantly without needing technical skills. This allows for more agile experimentation and faster iteration, reducing the dependency on specialized engineering resources for day-to-day adjustments.

Beyond the core recommendation model, there is a hidden layer of complexity that many companies overlook. Running a recommender system in production requires:

• Monitoring and alerting for uptime, latency, and recommendation quality
• 24/7 incident response teams to handle system failures
• A/B testing and quality assurance tools to evaluate performance
• Scalability planning and capabilities as your user base grows

While an in-house team could develop custom tools to manage these needs, it adds significant cost and development time. The reality is that maintaining a high-quality recommendation system is not a one-time build, but a continuous commitment that requires a dedicated team of data scientists, engineers, and operational support.

And then, of course, there is the cost. Infrastructure, salaries, ongoing R&D; it all adds up quickly. What initially seems like a cheaper, more flexible solution can easily become more expensive and time-consuming than expected.

Why Buying Often Makes Sense

On the other side of the coin, buying lets you skip many of the headaches that come with building. Instead of spending months (or longer) developing your own solution, you can get started almost immediately by leveraging an existing system.

Off-the-shelf systems are often built with scalability in mind. They’ve been tested across industries and user bases, meaning they’re designed to handle large amounts of data and adapt as your business grows. You also get the benefit of ongoing improvements since providers are constantly refining their technology to stay competitive.

For businesses that lack the time or resources to hire data scientists or stay up to date with the latest algorithmic trends, outsourcing is a practical option. And just because you’re outsourcing doesn’t mean you have to sacrifice control or flexibility. Many platforms today allow you to fine-tune their systems to meet your needs.

For example, Recombee’s advanced filtering options let you control which content gets recommended, whether that means showing only available items or filtering out specific content based on custom business rules.

You can boost certain kinds of content, adjust for diversity in recommendations, or bias the system toward newer content. It’s highly flexible, meaning you can adapt it to reflect your priorities without needing to reinvent the wheel.

Plus, buying unlocks advanced personalization capabilities without the burden of managing them in-house.

Dynamic Personalization Without the Hassle

A major advantage of buying a recommendation system is achieving deep personalization without the complexity of building and maintaining it yourself. Modern platforms make it easier to adjust recommendations for different business needs.

Take Scenarios, for example. These are distinct recommendation contexts designed for specific use cases. Whether it’s personalized content for a news homepage, "similar items" on an e-commerce product page, movie recommendations for a streaming platform, or suggested courses on an e-learning site, each Scenario operates with its own logic and rules, ensuring the recommendations align with the user’s intent.

Once Scenarios are defined, you can further refine recommendations using user and item data. With Recombee, boosters and filters help adjust which content is prioritized within each Scenario, whether it’s based on region, language, user preferences, or subscription tier. This way, recommendations stay relevant without constant manual updates.

The system also adapts in real-time. If a user suddenly interacts with a new category, shows interest in trending topics, or searches for specific products, the recommendations update dynamically. Instead of relying on static, pre-set suggestions, the system continuously learns and evolves, keeping recommendations relevant and engaging.

The Scalability Advantage

Another key benefit of buying is scalability. Whether you’re a small startup just starting out or a larger company dealing with millions of users, Recombee’s horizontally scalable infrastructure is capable of handling over a billion recommendations daily and processing more than 30,000 recommendations per second. It supports extensive catalogs with tens of millions of items and ensures low latency through multiple data centers strategically located across the globe. There’s no need to worry about whether your infrastructure can handle traffic spikes or growth. It’s all handled for you.

Weighing the Trade-offs

The biggest downside of an off-the-shelf solution is often the perceived lack of control. It can feel risky to hand over such an important part of your customer experience to an external provider. However, the level of customization and adaptability available in modern recommendation systems means this concern is less valid than it once was. With tools to fine-tune everything from the algorithms to the user experience, these systems ensure the recommendations still feel uniquely yours and align closely with your business goals.

On the flip side, building in-house gives you full control, but at a high cost, both in terms of money and time. Unless personalization is a core competency for your business, it’s worth asking whether the effort is truly worth it.

A Balanced Perspective

Ultimately, the build vs. buy decision comes down to your company’s strengths. If recommendations are central to your business, you have a sizable budget, and you want full control over the system, developing in-house could be a logical choice.

For most companies, however, outsourcing provides a faster, more cost-effective way to deliver high-quality recommendations.

And with platforms like Recombee, you get the best of both worlds. Expertly designed algorithms with the flexibility to adapt them to your needs. You’re not just adopting a tool, you’re gaining a customizable, scalable, and future-proof solution designed to grow with your business while ensuring the personalized experience your users expect.