Why Leaders Are Embracing Open-Source AI for Their Tech Stacks

How Open-Source Models Are Reshaping Enterprise AI Strategies

Like most tech leaders, I’ve stayed up to date on every AI development that’s cycled through the media over the years. And while open-source software has been around for decades, due to the huge amounts of resources it requires, open-source AI has only started to gain momentum in recent years.

Open-source AI got a lot more buzz once China’s DeepSeek released its open-source AI model that was reportedly built for $5.6 million—just 10% of the cost of Meta’s Llama (CNBC).

The use of open-source AI has risen among businesses. A report by McKinsey in partnership with the Mozilla Foundation and Patrick J. McGovern—which surveyed more than 700 technology leaders and senior developers across 41 countries—found that three-quarters of leaders expect to increase their use of open-source AI technologies over the next few years.

So, let’s explore why leaders are expressing more interest in open-source alternatives to leveraging AI for business, including how to mitigate certain risks that are inevitable with any technological movement.

Who Uses Open-Source AI Tools the Most?

In a nutshell, those who are most experienced in technology and its business value are the most likely to use open-source AI tools.

McKinsey reports, organizations that view AI as important to their competitive advantage are more than 40% more likely to use open-source AI—with the tech industry (unsurprisingly) making up 72% of these organizations. These findings suggest that the organizations most likely to use open-source AI are those who already place the most value in AI in general, and who know the most about it.

With tech leaders leading the charge, more than three-quarters of respondents across industries expect to increase open-source AI use in the coming years.

The Biggest Open-Source AI Benefits

Cost-Efficiency

Perhaps the biggest advantage of open-source AI is its cost-efficiency, with McKinsey respondents reporting lower implementation costs (60%) and lower maintenance costs (46%).

Despite reports of a faster time-to-value (48%), using commercial AI tools requires additional, often costly hurdles, like obtaining a commercial license or purchasing subscriptions, perhaps with restricted access to its core technology.

Meanwhile, open-source tools are available to the public, not just to use but to adapt and distribute with far fewer obstacles—not only saving upfront costs but potentially creating more sustainable tech solutions that save time and costs in the long run.

Control & Innovation

Up there with cost-efficiency is the benefit of control. When using open-source tools, organizations have control over their tech stacks—preventing them from relying on specific vendors, their ecosystems, and their pricing.

This also enables innovation and a sense of ownership, with developers being able to use their company’s data to fine-tune open-source models to their advantage. Using open-source tools also opens users up to the community of innovation that keeps projects at the cutting edge.

Performance and Ease of Use

The McKinsey survey found that more than ten times the respondents are satisfied than dissatisfied with their open-source AI tools. The top reasons for satisfaction were performance and ease of use. What does this mean?

In my experience, open-source models work well when you need outputs that cut out unnecessary fluff that dilutes the quality of responses. If you ask ChatGPT how to adopt an exotic bird, for example, it may lecture you on the rules and ethics of owning exotic animals, when sometimes you just need the facts.

Likely for these reasons, developers also increasingly view experience with open-source AI as an important part of their overall job satisfaction.

My Open-Source AI Advice

Source from Key Players

Going open source doesn’t have to mean straying from trusted developers. As of January 2025, the most used open-source tools used by enterprises are developed by big tech players, like Meta’s Llama and Google’s Gemma (Deloitte). Sourcing from trusted names in tech will likely mitigate some concerns regarding regulation and security. Still, users will need to carefully review the license terms to avoid violations, keeping up with compliance updates on an ongoing basis.

Robust Security Is Everything

AI implementation has already required a massive shift in how we go about cybersecurity, so making adjustments for open-source alternatives shouldn’t be too big a task. That said, tech leaders will need to strengthen security frameworks and software supply chain controls, in addition to recreating third-party evaluation models and setting guardrails for model behavior. When it comes to building security teams for open-source AI, security professionals who are already on top of AI’s impact on security should have easily updated their knowledge to handle open-source models.

Conclusion

Unless laws are created to stop it, open-source tech is not going anywhere. It benefits corporations, too, as they get crowd-sourced QA and bug fixes from diverse expertise.

Still, despite open-source AI’s clear benefits, and the community-driven philosophy of innovation it enables, leaders will still need to manage risks—researching, monitoring, and adjusting accordingly.

To tackle the unpredictability of AI with confidence, consider partnering with my organization PTP to equip your business with future-ready AI and ML engineers.