Choosing the Right Model Library: Open Source vs. Commercial Solutions

Choosing the Right Model Library: Open Source vs. Commercial Solutions

Key Takeaways

• Building your own analytics library with open-source tools can seem cost-effective - but often leads to higher long-term costs, technical debt, and slower time to market.
• Proprietary solutions offer faster deployment, expert support, and models that are continually updated to reflect evolving market standards.
• Choosing the right partner can future-proof your analytics infrastructure and give your team a major competitive edge.

Figuring out your requirements.

You need a powerful analytics library - one that's fast, accurate, and flexible enough to support complex portfolios. You have two options: build it yourself, with the support of free open-source tools, or buy a commercial solution.

At first glance, open-source sounds appealing. It's "free," after all. But hidden costs quickly pile up: the time your team spends building, documenting, maintaining, and constantly updating the models. Additionally, staying aligned with evolving market standards and new regulations, becomes a never-ending chore.

Meanwhile, proprietary libraries offer a different path.

When you choose a proprietary solution, you're not just getting models - you're getting access to thousands of pages of expert documentation, direct support from quants and financial technologists, and models that are used by leading financial institutions for pricing, XVA, risk, and P&L.

And because these platforms are continually updated, you're always aligned with the latest industry standards - without the heavy lifting.

In other words, "free" often comes at the cost of time, transparency, and reliability. Choosing a trusted partner means you can focus on what matters most: delivering results.

Why open-source often costs more than you think.

While open-source libraries provide flexibility, the reality is that they require significant internal investment. Building an enterprise-grade platform demands deep quant expertise, continuous testing, and a major commitment to keeping up with market changes.

Teams can easily find themselves stuck in a cycle of endless updates and technical debt, leaving less time for innovation and competitive advantage.

Once you decide to buy, not all libraries are created equal.

When a leading investment manager recently switched to Quantifi, they described it as "the closest I've seen to a bank-level analytics platform." That's because Quantifi supports a broad range of asset classes and our model performance is trusted by tier one banks, capable of running billions of valuations daily for complex XVA calculations.

Unlike others that treat their models as black boxes, Quantifi believes in transparency. Clients have visibility into model workings - building trust and control.

We also deliver an exceptional developer experience. Our intuitive Python API makes integration seamless, and our low-code Data Science Platform (DSP) empowers teams to combine proprietary models, open-source tech, and all their data in one place - boosting productivity and speeding innovation.

Our key advantages:

• High-performance analytics optimised for large-scale computation
• Designed to be truly cross-asset with a unified volatility framework
• Superior fixed income and credit modelling, matching tier-one bank standards
• Direct access to expert support, not just generic help desks
• We offer detailed documentation and insight into our models

Numbers you can trust.

Expertise, speed, transparency - built into every model.

Choosing the right library impacts how quickly you can respond to market changes, how confidently you can explain your numbers, and how efficiently your team can scale.

With Quantifi, you get more than a model library - you get a trusted partner that helps you deliver results, stay compliant, and gain a competitive edge.

Get in touch with us today to see why the world's leading financial institutions trust Quantifi.