The Benefits of Open-Source vs. Closed-Source LLMs
Large language models (LLMs) are some of the most influential technologies at developers’ disposal today. However, if you want to use LLMs to their full potential, you must choose the right one for your circumstances. That starts with deciding between open-source and closed-source models.
Open-source LLMs rely on open-source data, and their source code and architecture are open to anyone. Closed-source alternatives, by contrast, are proprietary solutions often only accessible through paid licensing. Which is best depends on your LLM project’s needs, as each has unique benefits and disadvantages.
Benefits of Open-Source LLMs
Open-source software currently powers up to 96% of commercial programs, and open-source LLMs can be equally as advantageous. Here’s a look at three of the most significant benefits:
1. Cost Efficiency
The most obvious benefit of open-source LLMs is that they’re relatively inexpensive. They either have no or minimal licensing fees, so their highest expenses are those pertaining to infrastructure and upkeep, which you may already be paying for your solution.
Prices for closed-source alternatives vary, but you’ll typically pay more for desired performance levels. The basic version of Google Gemini is free, but what you can do with it is limited. Obtaining the license to integrate closed LLMs into your applications will almost certainly carry high costs.
2. Flexibility
Open-source LLMs are also more flexible than closed alternatives. Because the architecture and source code are publicly available, you don’t have to worry about vendor lock-in. Being able to see and adjust all the code also makes it easier to adapt an LLM to your unique application.
Customizing a model to fit your needs may take time, but some open-source LLMs also have large user communities you can turn to for support. Others may have developed their own libraries or solutions you can use to streamline the process.
3. Rapid, Transparent Innovation
Similarly, the accessibility and community support of open-source LLMs means they often develop faster. When anyone can see the code, anyone can improve it or create novel applications based on it. Removing the barrier of licensing costs and complications further streamlines such innovation.
That same openness and visibility can also be a useful marketing tool for your solution. Surveys show that 60% of today’s consumers say transparency is a brand’s most important trait, so providing an in-depth look at your software may simultaneously boost your reputation.
Benefits of Closed-Source LLMs
Closed-source LLMs may incur higher costs and be less transparent, but they also have plenty of advantages. These three are the most noteworthy:
1. Higher Performance
The primary benefit of closed-source LLMs is that they typically offer better performance in specialized applications. While many open-source options achieve impressive results, they’re usually best as general-purpose models. Closed LLMs have significant financial and technical backing, which drives them to cutting-edge functionality for niche markets.
AI is an increasingly crowded field — there are at least 15,000 AI companies in the U.S. alone. It can be hard to make your solution stand out amid such competition, so a more specialized underlying model can be a crucial advantage as you try to distinguish your product.
2. Easy Implementation
Closed-source LLMs are also easy to implement, as the developer will have handled most of the work. Open-source solutions’ flexibility comes at the cost of requiring additional work before they’re ready to use. In comparison, proprietary alternatives often offer plug-and-play functionality.
Some closed-source projects involve you partnering with the original developer. In such cases, the provider will perform much of the technical work of getting the LLM ready for your use or adapting it to fit your solution, which is particularly helpful for less experienced teams.
3. Streamlined Security and Privacy Controls
The original developer will also retain control over cybersecurity and privacy measures in a closed-source LLM. While this does not necessarily mean the model is more secure than an open-source one, it may provide high-end security resources you’d be unable to access otherwise.
Offloading the burden of securing your model also removes workflow complications from your team. Many proprietary LLM providers will also handle regulatory compliance. Considering the rapidly evolving regulatory landscape — at least 24 states introduced over 60 security bills in 2024 alone — this can be a monumental time-saver and provide needed assurance.
Open-Source vs. Closed-Source LLMs: Which Is Best?
Whether an open-source or closed-source LLM is best depends on your goals and needs. Generally, open-source models are ideal if costs or customization are your biggest concerns, while closed-source LLMs are better for specialized performance or teams without much in-house AI expertise.
Just 12% of technologists today say they have significant experience working with AI. Dev teams falling under that umbrella may find the costs of a closed-source LLM worth it, as they’ll need to do less grunt work in deploying the model.
However, many less experienced teams are also those without as many resources to spend on cutting-edge technology. In these cases, you should look for an open-source LLM with a large, active user community so you can rely on help from other devs while capitalizing on open models’ lower costs.
Remember to consider any software and AI tools you already use when comparing LLMs. Vendor lock-in may prevent some closed-source options from being viable, while in other situations, a proprietary model may have built-in support for programs you already use.
Find the LLM That’s Right For Your Needs
Both open-source and closed-source LLMs can be beneficial. The key to making the most of either is knowing what you need and how each may help you meet those demands. Once you understand these dynamics, you can find the machine learning model that’s best for you.
