AI offers immense opportunities for startups, both new and established. It’s only natural, then, for business owners to ask, “Should I explore open-source models first before tying the knot with ChatGPT, Claude or Gemini?”
Some experts claim that open-source LLMs are far cheaper than proprietary AI. Others may argue that open-source solutions offer full control over your models and data. But is that the full picture? Adopting AI for business is riddled with complexities and nuances.
With more than 20 years in custom software development, I know a thing or two about the value of open-source software, and when it’s best to use an off-the-shelf or a bespoke solution. In this article, I’ll explain practical reasons for favoring open-source LLMs.
What Are the Benefits of Open-Source LLMs?
Open-source large language models (LLMs) offer businesses superior data privacy, extensive customization options and improved reproducibility compared to proprietary solutions. While requiring significant upfront investment in infrastructure and talent, they provide full control over models and data, making them ideal for highly regulated industries like healthcare, legal and fintech.
A Wealth of Options
The open-source AI community continues to grow every day. Platforms like Hugging Face list thousands of open-source models for diverse use cases, ranging from simple text generation to text-to-image and text-to-video generation.
Of course, the most mature and well-maintained LLM projects belong to tech titans. Meta, Google, Microsoft and, most recently, OpenAI have all released open-source versions (albeit somewhat limited) of their LLMs. A few other players, such as Mistral AI, DeepSeek, Alibaba’s Qwen, and Technology Innovation Institute’s Falcon models, have also gained popularity.
Keep in mind, though, the term “open source” is very broad, and all these models are released under different licenses. This means there may be restrictions for commercial use. With proper research and analysis, however, it’s possible to select an open-source model that best aligns with your needs.
Superior Data Privacy
Businesses handling highly sensitive customer data often choose the open-source route not because it’s cheaper — quite the opposite — but because they have no other viable option. They’re willing to invest a significant amount of money in building their own infrastructure for deploying and training open-source models to guarantee their customers ironclad privacy, which is a huge competitive advantage in itself. When companies deploy open-source models on-premises, within a company’s own data centers or a private cloud environment, sensitive user data never leaves the organization’s secure network.
Additionally, open access to the model’s source code, architecture and algorithms promotes transparency and trust. It allows internal teams to inspect the model’s inner workings and conduct a software development lifecycle (SDLC) audit to identify vulnerabilities and ensure compliance with stringent industry regulations, such as HIPAA or DORA. Such audits are impossible with closed-source black box APIs, where an enterprise must simply trust the vendor’s claims about security and data handling.
Customization
Customization is possible through a process called fine-tuning. In simple terms, you take a foundation model (the one trained on the entire internet) and continue training it on a domain-specific data set to reduce hallucinations and achieve more accurate, industry-relevant outputs. Your proprietary data, whether it’s structured or trapped in cross-department silos, can provide the necessary context to transform your LLM from a generic tool into a truly intelligent and helpful asset.
Both proprietary and open-source models allow fine-tuning, but the former impose constraints on the degree of customization possible. Closed-source LLMs are delivered via an API, meaning all the fine-tuning happens on the provider’s platform with no access to underlying model weights or architecture.
Performance Parity
If we look at LLM leaderboards like LMArena, we’ll see that the latest closed-source models are leading the charge. If we compare the latest open-source models to older versions of closed-source ones, however, we’ll see that the open-source ones often outperform their proprietary rivals. At the time of writing, open models from DeepSeek (deepseek-r1-0528), Alibaba (qwen3-235b-a22b-instruct-2507), and Moonshot (kimi-k2-0711-preview) are tied for eighth place with the proprietary model of Anthropic (claude-opus-4-20250514-thinking-16k).
Reproducibility
Consistent, predictable outputs are a key goal for any AI startup. With a closed-source LLM, you don’t really know what happens behind the scenes. The provider may introduce subtle changes to the model behind the API your app is using, causing unexpected app behavior or breaking flows that had been tested and polished to perfection.
Remember when OpenAI rolled out GPT-5, deprecating older APIs without much warning to developers? This move resulted in a fair share of broken AI apps. Although the company rolled back the older models after a user backlash, this incident taught us a valuable lesson: Relying on a single AI provider may not be the best idea.
With the open-source approach, reproducibility is easier to manage. Because the model version and weights are fixed and under the organization’s control, it is possible to achieve dependable, consistent outputs over time.
Saving Computational Resources and the Planet
The more parameters the model has, the better. These are what make it smarter. But those hundreds of billions of parameters require enormous computational resources, which are inevitably reflected in your monthly API fees.
But what if you don’t need a sledgehammer to crack a nut? If your use case only requires a small, specialized model that can run locally, then open source is the way to go. Plus, the carbon footprint of deploying and using an LLM directly correlates with its size.
What About the Cost?
Let’s get this one fact straight: “open source” doesn’t equal “free.” Though you don’t have to pay licensing fees, the costs for infrastructure, talent and ongoing maintenance quickly add up. The upfront investment is substantial, but it may be reasonable for companies operating in the healthcare, legal, and fintech sectors, where user privacy is crucial to their business operations.
First things first, you need hardware to run your open-source LLM. While a smaller model with just a few billion parameters can be run on a powerful laptop, a model with a few hundred billion parameters requires an enterprise-grade setup.
You have several options here — building an on-premises data center, opting for a private cloud or using managed LLM platforms like Together AI, Replicate or Google Vertex AI.
In the first scenario, you need to factor in the cost of high-performance GPUs, CPUs, RAM, storage and networking. For a model with more than 110 billion parameters, you’ll need multiple high-end GPUs with 80 GB or more of VRAM each. To put things into perspective, a new NVIDIA H100 (80GB) GPU costs between $25,000 and $30,000. Now multiply that by eight or 10 to achieve enterprise-class performance. The larger the model, the more powerful and expensive the GPUs you need.
Additionally, you need to account for expenses related to operating a data center, including power, cooling, physical security and fire suppression systems, as well as salaries for maintenance personnel.
A private cloud, in other words, renting virtual machines with powerful GPUs from cloud providers like AWS, Microsoft Azure and Google Cloud, requires no upfront investment but can be pretty expensive in the long run. An on-demand instance with a single NVIDIA A100 GPU can cost between $3 and $5 per hour. A machine with eight H100 GPUs can cost more than $30 per hour. Costs are based on instance uptime, data transfer and storage. The good news is that you can always find cloud providers that charge a bit less for the same compute resources.
Finally, managed LLM platforms handle all the underlying infrastructure, deployment and scaling for you. In this case, you don’t need to worry about the complexity of managing servers and GPUs. You simply sign up, choose a pre-optimized model from their library or upload your own fine-tuned model weights, configure the endpoint and integrate the API into your app. This allows you to pick the perfect model for a specific task, balancing performance, speed and cost. For example, you could use a small, fast model for simple summarization and a larger, powerful model for complex reasoning, all from the same platform.
Generally, an open-source, self-hosted model may become operationally more cost-effective when monthly requests are predictable and approach millions of interactions. Remember that it may take several years to see a positive ROI.
Look Into Open-Source AI
With software, there’s no one-size-fits-all approach. Each business needs to do its homework and weigh the pros and cons of both worlds. Closed-source solutions shine when time-to-market and advanced reasoning capabilities are a priority. On the other hand, open-source models are the best choice for highly regulated industries. In these sectors, transparency into the model’s inner workings and the assurance that data never leaves company servers are key to meeting compliance requirements.
