Open-Source vs Proprietary LLMs: Deciding What’s Right for Your Platform

A huge number of businesses are embracing AI in their workflows. There is no escape from this: Artificial intelligence is already an integral part of doing business. To be more precise, about 77% of organizations are either already using or planning to implement some form of artificial intelligence. If you want to stay afloat among your competition, you need to jump on the AI train.

The easiest way to do this is to adopt an LLM, since they are quite easy to implement, deliver good results, and may not require large investments. In this case, you have a choice: to use an open-source model or a proprietary (private) one. This article will describe the difference between the two notions and help you decide which is right for you.

What Are Open-Source LLMs?

Open-source models are LLMs whose training data, source code, and architecture are available for anyone to use and change. They usually function under an open license. Unlike private models, open-source ones give engineers and researchers full transparency and control.

What makes an open-source model?

• Accessible codebase: The source code is at your disposal.

• Community collaboration: Thanks to contributions from the global community, open-source LLMs often improve quickly.

• High level of customization: Your team can fine-tune models on specific data your tasks require.

Open-source LLMs allow smaller organizations, startups, and independent researchers to incorporate AI into their software without relying on Big Tech APIs.

Advantages of Open-Source LLMs

Open-source LLMs can bring plenty of benefits to your business. Here’s what you can expect if you decide to opt for implementing this type of model:

• Trust and clarity: You can inspect every aspect of the model, which removes ethical issues and makes risk management way easier.

• Cost control: Besides having lower prices in general, open-source models usually have no recurring API usage fees. You can also host everything on your infrastructure to avoid unexpected pricing changes.

• Customization: Open-source models can adapt to niche business areas and are more appropriate for experimenting.

• Data safety: With full control over hosting, data always stays with you, which matters for regulated environments.

• Deployment flexibility: These models can be deployed on whatever suits your needs (edge devices, private servers, or in the cloud).

Source: Klu.ai

Limitations of Open-Source LLMs

Open-source models are great for a lot of situations, but they can’t solve everything. Sometimes, they may run into some problems that can be difficult to deal with. If you know about them beforehand, you may decrease their impact on your software. Here’s what you need to know about:

• Resource requirements: Dealing with large models locally requires strong infrastructure that not all small teams can afford.

• Inferior performance (sometimes): Open-source models do change fast, but they are still not as capable as proprietary ones, especially when it comes to reasoning or multilingual features.

• Security and maintenance burden: With open-source models, you become the one responsible for security and updates.

• Fewer integrated tools: Private ecosystems often come with plug-and-play tools like plugins, agents, or retrieval-augmented generation that other models don’t have.

Popular Open-Source LLMs in 2025

If you want to implement an open-source LLM, there are plenty of solutions to choose from. Some of the top-tier examples include LLaMA 3, Mistral, and DeepSeek.

LLaMA 3 was released last year by Meta. This model is available in 8B, 70B, and even a massive 405B parameter version. It was trained on a dataset about seven times bigger than its previous version. It helps LLaMA 3 provide state-of-the-art capabilities while remaining open and customizable.

Mistral AI’s open‑source offerings include Mistral Small 3 (~24B parameters), launched January 2025 under Apache‑2.0 licensing. It provides low latency, high-level efficiency, and strong multilingual performance. And the newer Small 3.1 surpasses smaller proprietary models on latency and context handling.

DeepSeek LLM (versions V2.5, V3, and the R1 family) is a Chinese‑developed open‑source model series. Its 67B‑parameter version is trained on ~2 trillion tokens (both English and Chinese) and optimized for both chat and base models (7B/67B variants) under an open MIT license. DeepSeek V3 (and R1) supports huge 128 K token context and delivers GPT‑4 level results in coding and math benchmarks. And all of that with a training cost reportedly under $6 million.

What Are Proprietary LLMs?

Let’s continue the open-source vs proprietary LLM battle with the definition of the latter. A proprietary LLM is a model developed, owned, and controlled by a private company. And when you can read open-source models like an open book, proprietary solutions are not freely available to the public. Access is usually provided through paid APIs or commercial platforms.

The features of a proprietary LLM include:

• Closed source: Everything model-related is not publicly disclosed.

• Commercial licensing: You typically access the model through a subscription, API key, or usage-based pricing plan.

• Full management: All infrastructure, scaling, updates, and security are handled by the provider.

• Cloud-based access: You send a prompt to the provider’s servers, and they return a response, so no need to run the model yourself.

Proprietary LLMs are chosen for their smoother integration into products and enterprise-grade support. However, they come at the cost of less control and higher expenses.

Source: Market.us

Benefits of Proprietary LLMs

Open-source LLMs can help you with a lot of stuff, but proprietary ones are just better in some aspects. How do the benefits differ? Proprietary models offer some unique advantages that are not accessible to the other type for now.

• State-of-the-art performance: Models like GPT-4 or Gemini often lead benchmarks in reasoning, language fluency, and multimodal capabilities. They benefit from massive budgets and fine-tuned optimization.

• Ease of use: All privately owned LLMs have ready-to-use APIs, SDKs, and platforms that help businesses make use of every possible AI feature. 

• Integrated ecosystems: Proprietary LLMs come with tools like agents, embeddings, RAG frameworks, function calling, memory, and plugins, which make production faster.

• Scalability and maintenance: Since these models are hosted in the cloud by the provider, there’s no need to manage updates and patch vulnerabilities.

• Multimodal capabilities: Many proprietary models can work with not only text, but also code, images, audio, and video in a single model.

Drawbacks of Proprietary LLMs

Well, just like open-source LMMs, proprietary solutions also have their own drawbacks. There are plenty of things you need to take into account when choosing this type of model.

• Closed training: Unfortunately, you will never know what data the models were trained on. You also won’t be able to find out whether the initial data was biased or compromised. It can be a concern for sensitive/high-compliance use cases.

• Vendor lock-in: Switching providers or migrating to open models can be difficult once your product is built around one API.

• Cost: Yes, these models are expensive. Especially usage-based pricing and especially at scale. If you have a high-traffic application or a solution that needs long context windows, you will have to spend a lot of money on AI.

• Limited customization: These models offer some level of fine-tuning/prompt-tuning, but you’re still going to be constrained by the provider’s rules.

• Data privacy concerns: Even though vendors assure their clients that their security is top-tier, not all companies are ready to send their data to a third-party server.

Leading Proprietary LLM Providers

When it comes to examples, you have definitely heard a word or two about them. These models are on everybody’s lips, and you are probably already using one in more day-to-day circumstances. The most prominent of them are GPT, Claude, and Gemini.

We are going to start with OpenAI’s GPT‑4 series (including the multimodal GPT‑4o). It’s the engine behind ChatGPT and many enterprise integrations. It delivers industry‑leading benchmarks in reasoning, multilingual generation, and human‑like conversation. And besides that, GPT‑4o can work with visual and audio data with low latency and broad ecosystem support.

Claude 3 (launched March 2024) includes three tiers: Haiku, Sonnet, and Opus. Claude 3 Opus is the higher‑capability model, Sonnet is balanced, and Haiku is optimized for cost. All of them feature vision capability and show great results in long-context support. 

Gemini 1.5 represents Google DeepMind’s multimodal family. The models support long context (millions of tokens, hours of audio/video), multilingual processing, and extremely high efficiency thanks to a Mixture‑of‑Experts architecture.

Open Source vs. Proprietary LLMs: Key Comparisons

Now, we can compare these two options. We will discuss the most common parameters by which they are compared. Knowing the strengths and weaknesses of each type will help you choose the perfect solution for your software, depending on what you want your LLM to do.

Performance and Accuracy

Proprietary LLMs are the undoubted benchmark leaders in things like creativity and coding. They are trained with huge datasets, so they can be considered more "knowledgeable" and powerful.

However, open-source LLMs are definitely catching up to them. Even though their results may be as advanced, open-source models are progressing every day and now can easily compete with GPT-3.5-level performance. But they still tend to lag slightly behind when it comes to advanced reasoning and long-context coherence. They depend a lot on fine-tuning, hardware, and deployment quality.

Cost and Licensing

Proprietary LLMs are relatively expensive. Their pricing is usually usage-based, where usage is measured and billed (for example, per 1,000 tokens). APIs are also hosted by providers, and you may pay for them separately. Finally, licensing is restricted, with commercial terms, limited usage, and possible vendor lock-in.

Open-source models are free to use thanks to permissive licenses. They have no proprietary APIs, which means no API fees and free hosting wherever you want. Costs will depend on your infrastructure, but if you want to go long-term, it will scale better.

Customization and Flexibility

Unfortunately, proprietary LLMs don’t offer countless customization options. Some of them allow some sort of prompt engineering/fine-tuning, but you will never have full control over architecture. You also won’t be able to access training data, and guardrails will narrow the list of use cases.

Open-source solutions provide you with unmatched flexibility and full control over almost everything: fine-tuning on your own data, changing the architecture, hosting, and inference logic. These models are perfect for building domain-specific applications in medical, legal, and scientific fields.

Security and Compliance

Mature proprietary vendors offer enterprise-grade security, SOC 2, ISO certifications, and data encryption. It’s great and all, but there’s a catch: Your data is sent to third-party servers, which may be a compliance issue for some industries.

With open-source LLMs, you can self-host them, so your data stays within your own infrastructure and network. However, you become responsible for implementing your own security controls, which can be burdensome for smaller companies.

Here’s a short table to compare all the parameters:

When to Use Open-Source vs. Proprietary LLMs

Both types have something to offer and something to be aware of, but that doesn’t mean that one is worse than the other. They just come in handy for different situations.

Best Use Cases for Open-Source LLMs

The most common situations for using open-source models include:

• Internal tools in regulated industries (to keep private data in-house)

• Custom domain-specific assistants (with your niche data)

• Edge/on-prem deployments (if you want to deploy AI locally)

• Low-cost high-volume use (to scale faster)

• Academic research and innovation (to modify architectures, analyze behaviors, or benchmark against new assignments)

If your business goals fit any of these, an open-source LLM will suit your purpose well.

Best Use Cases for Proprietary LLMs

Proprietary LLMs have their own things that they can do more efficiently than the previous type. They include:

• Customer-facing chatbots (thanks to their best-in-class conversational ability)

• Creative and multimodal apps (that need to generate images, interpret charts, or respond to voice)

• Enterprise productivity tools (assistants in software like Microsoft Copilot)

• Rapid prototyping (test ideas without infrastructure concerns)

• High-stakes reasoning tasks (those that need precision, logic, and long-context understanding)

With these use cases, proprietary solutions will work smoothly because of their performance and ease of use.

Bottom line

With everything that we discussed above, you can now make your own choice when it comes to the open-source LLM vs proprietary LLM discussion. It doesn’t matter what model you ultimately end up with. Adding some AI into the project is the right way to go.

If you need any assistance with such integration, Yellow is here to help! Contact us, and we will transform your ideas into a solution that pleases users and makes a profit.