Open-Source LLMs vs Closed-Source LLMs: Key Differences in 2025

It’s hard to imagine the modern world without LLMs. Although they have become popular relatively recently, their impact on business is undeniable. Companies turn to LLMs for a variety of requests, from writing an email for a client to developing a business plan for a new startup. Thanks to these AI tools, a huge part of the tasks has become almost completely automated. That’s why organizations are increasingly integrating these models into their work processes.

When it comes to business, one of the first questions to consider when choosing a suitable LLM is whether to opt for an open-source or closed-source model. How to choose which one is right for you, and how exactly do they differ? We will tell you in today's article.

Understanding Open-Source LLMs

Before comparing the two notions, we need to learn what both of them mean. We are going to start with open-source LLMs.

Definition and Core Characteristics

Open-source large language models, or LLMs, are models whose structure, training data, and code can be accessed and changed by basically anyone. Such an approach emphasizes transparency, accessibility, and community innovation. These models don’t have to be completely open, but they generally provide developers with more visibility and control.

Source: Hostinger

Their key features include:

• Transparency: Model architecture, training processes, and evaluation benchmarks are usually published.

• Accessibility: Available for engineers and businesses to download and run locally.

• Customizability: Users can fine-tune/retrain models on whatever data they seem fit.

• Community-driven development: Improvements like bug fixes and safety guardrails are often made by users themselves.

• Cost efficiency: Since the models are available for free, businesses can avoid licensing fees and vendor lock-in.

• Flexibility: It can be hosted on private servers, edge devices, or cloud environments.

Open-source LLMs do a very important job: They democratize AI by making top-tier models accessible and transparent.

Advantages of Open-Source LLMs

Open-source LLMs can bring your business plenty of benefits, especially if you care about clarity and independence. These perks include:

• Community-driven development

Open-source LLMs get upgraded by the contributions of a global network that involves a lot of researchers, engineers, developers, businesses, and organizations. This means bugs are fixed faster, features are added more quickly, and performance benchmarks improve through collective effort. Besides, best practices for optimization, deployment, and safety spread way faster.

• Transparency and auditability

Open-source models make it possible to look under the hood of all their processes. Your team can understand how the model was built and trained, so it becomes easier to find biases, security vulnerabilities, and limitations. You can audit the model to make sure it follows all the privacy regulations and industry standards.

• Cost-effectiveness

Unlike proprietary APIs that charge per request, open models are free to use. This makes it possible to run models in-house, so they become even more economical at scale compared to ongoing API costs. And if you are not satisfied with the results, you can adapt or migrate your systems without being tied to a single vendor’s pricing.

Limitations of Open-Source LLMs

Unfortunately, open-source LLMs are not all-powerful. They still have their own drawbacks that you need to take into account if you want to start working with them. Here’s what you need to know:

• Security risks

While openness promotes transparency and honesty, it also creates plenty of risks. Since the model’s parts are freely available, hackers can repurpose them for harmful use cases like automated scams or malware generation. Also, running models in-house does give you control, but it also puts the burden of security entirely on you.

• Lack of dedicated support

Since open-source projects typically rely on community help, there may be no guaranteed SLAs. If you face problems in production, there’s no support team you can count on. Documentation may also be incomplete or split up across forums, GitHub repos, and research papers.

Popular Open-Source LLM Examples

There are a lot of open-source solutions that you can choose from, depending on what you want to do and what resources you have. The most popular and proven LLMs here are:

• LLaMA (Meta): A strong foundation model series from Meta. LLaMA-13B can beat GPT-3, and the larger 65B model goes toe-to-toe with private state-of-the-art models 

• BLOOM (BigScience): A 176B-parameter multilingual model with the knowledge of 46 natural languages and 13 programming languages.

• Falcon Series (TII, Abu Dhabi): Includes Falcon 7B, 40B, and the flagship Falcon-180B

• Mistral Models: The French startup offers productive models like Mistral 7B, which outperforms many models in English and coding.

• Qwen (Alibaba): A broad and powerful model family licensed under Apache 2.0 that covers more than a hundred languages.

Exploring Closed-Source LLMs

We now know more about open-source LLMs, so we can move on to their closed-source counterparts.

Definition and Key Characteristics

Closed-source LLMs keep their insides hidden from the general public. You can access them via SDKs, APIs, or platforms. Usage is also controlled by the vendor’s terms and pricing. These models show top performance in the industry but remain under the strict control of their creators.

Their most prominent features include:

• Restricted access: Users can’t look through the model’s insides.

• Optimized performance: Vendors usually deploy closed models at a massive scale with enormous infrastructure, so response times are short.

• Commercial licensing: Use is based on commercial agreements.

• Limited customization: Fine-tuning is not as extensive as in the previous type.

• Vendor support: These solutions have professional support teams, standardized documentation, and SLAs.

• Safety guardrails: Vendors have many top-tier safety precautions in place.

Source: CBinsights

Advantages of Closed-Source LLMs

If you choose to stick to this option, you will definitely be happy with the following perks.

• Enterprise-grade support

Closed-source LLMs are backed by large companies that provide professional services. You can get access to things like customer success managers, onboarding assistance, or top-tier technical support. Vendors must comply with a lot of industry standards, so your adoption becomes safer.

• Optimized performance

Proprietary LLMs usually show better results in reasoning and real-world benchmarks. And since vendors never stop updating and enhancing everything, these results are constantly improving. Thanks to the huge infrastructure, you can stop worrying about usage spikes.

• Stronger security controls

Closed-source providers care a lot about the safe usage of their models. Constant content moderation, red-teaming, and safety measures reduce biased outputs. If a vulnerability arises, the vendor is responsible for patching and supporting security, not you.

Limitations of Closed-Source LLMs

Closed-source LLMs provide first-class services to their clients, but they're still not immune to limitations and restraints.

• Vendor lock-in

This approach can closely tie businesses to a specific provider. These companies become extremely reliant on the provider’s infrastructure, roadmap, and policies. If the vendor changes any of it, customers won’t have a lot of options for their next step.

• Higher costs

Since most closed-source LLMs typically have usage-based pricing, costs will grow with each request or token. That’s why proprietary systems can make scaling more expensive. Vendors can also put a high price tag on premium features like new integrations or enterprise-level support.

Leading Closed-Source LLM Examples

The most prominent closed-source LLMs are:

• GPT-4 Turbo (OpenAI): Widely used in many use cases and known for its coding skills, deep thinking, and scalability.

• Claude 3 (Anthropic): Built with safety and enterprise readiness in mind. Can deal with long-context tasks.

• Gemini 1.5 (Google DeepMind): Google's multimodal model with exceptional long-memory capabilities.

• PaLM 2 (Google): A prominent language model for multiple tasks.

• Jurassic-1/Jurassic-2 (AI21 Labs): High-parameter LLMs designed for next-level language processing.

Key Comparison: Open-Source vs Closed-Source LLMs

So, let's line up LLM open-source vs closed-source solutions across the most important parameters for a business. We’re going to look at the budget requirements, security, flexibility, update speed, and intellectual property issues.

Cost and Budget Considerations

Open-source LLMs are free to access and use with no fees. If you need to run the model on-premises, you’ll get more long-term savings. However, you still need to consider that infrastructure and in-house expertise can still be pretty expensive.

Closed-source LLMs are mostly usage- or subscription-based, and costs are calculated per API call/token. They have predictable short-term costs, but with time, they can bulk up and become expensive. In exchange for that, you will have no infrastructure burden.

Customization and Flexibility

Open-source models are the undisputed kings in customization. They give users complete and utter flexibility, so you can tailor them to almost any task you need. You will also have more control over deployment options.

Closed-source LLMs, unfortunately, don’t have a lot of customization features. They are usually restricted to fine-tuning APIs made by the vendor. And their deployment model (cloud-first, API-based) doesn’t allow for enough adaptability.

Innovation Speed and Updates

For open-source solutions, innovation is community-driven. It implies fast-paced experimentation, frequent forks, and specialized variants. Unfortunately, such updates may be fragmented and not have a clear direction.

Closed-source LLMs are controlled entirely by their vendors. Updates and enterprise features arrive when the vendor wants to, and it makes the innovation progress depend not on what users need, but on the vendor’s roadmap.

Security and Compliance

Open-source transparency grants you the right to fully check the model. Whatever vulnerabilities you find, you can easily check and fix them. However, don’t forget that it makes you responsible for security-related stuff.

As you have probably already guessed, closed-source models don’t have this level of sincerity. You have to just trust the vendor’s claims without full auditability. But these companies are required to follow a lot of rules and have strong moderation, so they can provide a decent level of security.

Intellectual Property Concerns

An open model won’t have many restrictions for training data, but you may encounter some licensing/copyright ambiguities, and there’s always a risk of inheriting biases or IP issues.

In the case of private solutions, companies take care of what’s going on under the hood. Users are indeed protected from most liabilities, but must work within the vendor's terms of service.

Which LLM Type is Best for Your Needs?

Now, this is where decision-making gets practical. Here’s a quick rundown of when you should choose an open-source LLM vs closed-source LLM:

When to Choose Open-Source LLMs

Since the main advantages of open-source solutions are transparency and freedom, they suit organizations that need long-term cost efficiency and utmost control. If you want to get more deployment and modification flexibility, closed-source solutions won’t be able to give it to you.

Security is another reason you may choose this type of model. If you must keep your data in-house and/or fully control what the model does, open-source solutions are your way to go.

When to Choose Closed-Source LLMs

If your priorities lie with faster time-to-market and better support, closed-source LLMs are exactly what you need. Many industries can't afford downtime or delays due to infrastructure or model issues. Plug-and-play solutions allow you to lessen the training and hosting load and launch your AI features quickly.

Regulated industries will also make the most of closed-source AI. Companies there must follow strict requirements like, so AI vendors provide compliance frameworks to reduce risk and liability.

Future Trends in LLM Development

Finally, let’s talk about what LLM development should expect in the future as an industry. There are several main trends that are forming now and will continue to change the way LLMs function. Here’s what you should know about.

Source: Snorkel AI

The Growing Role of Hybrid Models

The next wave of LLMs won’t simply fall into either “open-source” or “closed-source.” Instead, we’re already seeing hybrid approaches emerge. For example, AI companies may use open-source models for the base architecture, then add proprietary fine-tuning pipelines to boost their solutions. Vendors can also offer a mix of an open-source core available for experimentation and premium closed-source features.

AI Ethics and Regulation Impact

Countries and international organizations are shaping how you build, deploy, and audit LLMs. They apply new laws to protect the users and their data from being abused by the providers. That’s why vendors may be required to disclose their data sources and model limitations. This could push closed-source vendors to explain their “black box” systems in more detail.

Why Choose Yellow For AI Development?

Yellow is your trusted AI software development partner. We provide top-tier services for a variety of industries. With more than 10 years of experience, we know exactly how to make your software solution stand out.

What makes us your best choice?

• Product lab: We develop our own software, so we know everything about the product lifecycle and apply this knowledge to your solutions.

• Business-first approach: Your goals are our top priority. Every single feature will be based on what you want to achieve.

• Availability: Whatever your time zone is, we will always be ready to respond to your questions and concerns.

• Secure process: Before our collaboration starts, we sign an NDA with you to keep your data safe.

Conclusion

The final choice between a closed-source vs open-source LLM depends on your business goals and priorities. With hybrid models and changing regulations, you should carefully combine experimentation with responsibility. And if you want to integrate such a solution into your software, our team is here to help!