A huge number of SaaS businesses use ready-made machine learning solutions. Large language models, computer vision, smart assistants, recommendation systems, image recognition, fraud detection. And these are only the most popular examples of technologies that help millions of companies work faster and more efficiently (in fact, there are many more).
However, ready-made ML solutions are not always suitable for achieving your business goals. They can be too general, too feature-heavy, and sometimes, too expensive. That is why many SaaS businesses are switching to custom solutions (often built by an artificial intelligence software development company that understands their specific needs). But how much more profitable are ML systems developed from scratch than ready-made software programs? What is the SaaS industry really guided by? We will discuss this in this article.
AI/ML in SaaS: How things are going
Before we dive deeper into why custom ML solutions are better for SaaS businesses, let’s take a look at what is going on in the AI-based SaaS market in general:
- The AI SaaS market is expected to reach around $775 billion by 2031 with a CAGR of 38.28%.
- 34% of SaaS companies say their productivity improved after they introduced artificial intelligence.
- 67% of SaaS businesses leverage AI to improve their offering for the clients.
- In 2022, investments in AI SaaS startups reached $43.0 billion.
- 82% of cloud companies power their SaaS products with AI functionality.
These numbers show that AI definitely has its place in the SaaS industry. Now, let’s see what the difference is between using an out-of-the-box AI and training your own.
Off-the-shelf AI vs. Custom ML: What’s the difference?
When it comes to adding AI to SaaS products, a lot of leaders jump into plug-and-play solutions. And it’s fine to use image recognition or chatbot APIs that are fast, reliable, and easy to integrate. But these solutions are built for everyone, not just for you. Off-the-shelf models are made to deal with a broad pool of use cases for many industries. They’re perfect for getting started, but they come with trade-offs like a lack of context and limited customization. Besides, if anyone can use it, it can hardly be considered an advantage.
Custom ML apps, on the other hand, are built for your specific data, processes, and business goals. They do require more investment upfront, but they enable your SaaS to automate your business precisely the way it needs to be automated.
5 Reasons Custom ML Gives SaaS A Competitive Edge
Custom machine learning solutions can make your business stand out and offer something unique to your customers.
Here’s exactly how it helps you reach your business goals:
Hyper-Personalization At Scale
Off-the-shelf personalization often doesn’t go further than “users who liked this also liked that.” Custom ML models can make personalization truly smart. The key here is your data. A tailored recommendation engine trained on your unique engagement data is not only able to recommend users similar services, but it can also change feature layout, onboarding flows, and even pricing tiers based on how users behave. If you are looking for better conversion rates and reduced churn, custom ML is your best choice.
Proactive User Retention
User churn is a SaaS killer. If nobody uses your platform, there’s no need for your product to exist. Thus, you’re leaving the market. Nobody wants that. Many companies wait until users cancel to act. Not the best approach. Custom machine learning helps you intervene proactively: You train a model on your own historical usage data to predict which users are close to leaving (sometimes weeks before they cancel the subscription). If gives enough time for your team to step in with offers, education, or outreach before it’s too late.
Smarter Automation
Automation is not a foreign concept for SaaS business tasks. Sometimes, ready-made ML solutions are exactly what these tasks need, nothing special and nothing custom. However, only businesses with custom ML solutions can optimize how and when those tasks happen, based on patterns specific to their user base. That’s where partnering with a machine learning app development company makes a real difference. Instead of simple rule-based triggers, they can make ML adapt to changing behaviors, time of day, and other nuances. As a result, you will get a more accurate and less annoying automation that will make your platform feel truly intelligent.
Proprietary Intelligence
Custom ML allows you to build proprietary Intelligence that will become a strategic asset that competitors can’t copy. Once again, your model is trained on your data. No one else has that exact combination of user behaviors, integrations, and engagement history. Even if a competitor tries to mimic your feature set, they can’t reinvent your intelligence layer without your data. And if they somehow get their hands on your data, well, you have something going on with your security measures, which is a whole other problem.
Operational Efficiency
Not all machine learning features are facing your customers. Sometimes, tailored models can help your internal teams work smarter, not harder. For example, they can forecast usage surges or route leads to the right reps. These internal-facing ML models will reduce the time your employees spend on day-to-day operations, which will lead to higher focus and transparency between departments.
Challenges SaaS Leaders Should Be Aware Of
Not everything in the AI integration process always goes smoothly. SaaS leaders can face plenty of challenges along the way, so if you know about them beforehand, you can minimize their impact on your operation and get the most out of your custom ML project. Here’s what you need to pay attention to:
- Data quality: Custom ML is only as good as the data it learns from. Incomplete, inconsistent, or biased data leads to poor predictions.
- Longer time to value: Unlike off-the-shelf tools, custom ML takes time (weeks or even months) to design, train, and integrate. Be ready to wait.
- Talent gaps: Building and maintaining your own ML model requires specialized skills. Hiring or partnering with a suitable team is extremely important.
- Never-ending maintenance: ML models degrade over time as user behavior and business conditions change. You must plan for retraining and versioning from day one.
- Cost of experimentation: ML models built from scratch are an R&D investment. Not every model will work out. You should be comfortable with possible sunk costs.
Bottom Line
Custom ML development may not be for everyone. But it’s the solution that can bring new loyal customers to your SaaS business and set you apart from your competitors. Your task now is to understand whether you are ready for such investments, choose the tasks for which a custom ML solution is suitable, and find the right team to complete the project.