A.I. offers immense opportunities for both start-ups and established companies. So it's natural for business owners to ask, “Should I consider an open source model first before partnering?” Chat GPT, Claude or gemini? ”
Some experts claim that open source LLM Much cheaper than proprietary AI. Some may argue that open source solutions give you complete control over your models and data. But is that the whole picture? Deploying AI in business is full of complexities and nuances.
We have over 20 years of custom experience software developmentI know a thing or two about the value of open source software and when it's best to use off-the-shelf versus bespoke solutions. This article provides practical reasons to support open source. LLM.
What are the benefits of an open source LLM?
Open-source large-scale language models (LLMs) offer enterprises greater data privacy, extensive customization options, and improved reproducibility compared to proprietary solutions. It requires a large upfront investment in infrastructure and talent, but gives you complete control over your model and data, making it ideal for highly regulated industries such as healthcare, law, and fintech.
Abundant options
The open source AI community continues to grow every day. Platforms like Hugging Face list thousands of open source models for simple to a variety of use cases. Text generation to text to image and Text to video generation.
Of course, the most mature and well-managed LLM projects belong to large technology companies. Meta, Google, Microsoft, and Recently, OpenAI All companies have released (albeit somewhat limited) open source versions of LLM. Several other players, e.g. Mistral AI, deep seekAlibaba's Qwen, and Technology Innovation Institute's Falcon models are also gaining popularity.
However, keep in mind that the term “open source” is very broad and all of these models are released under different licenses. This means that commercial use may be limited. However, with proper research and analysis, you can choose the open source model that best suits your needs.
Superior data privacy
Companies with sensitive customer data choose the open source route not because it's cheaper, but rather because there are no other viable options. They are willing to invest significant amounts of money in building their own infrastructure for deploying and training open source models in order to guarantee ironclad privacy to their customers, which is a huge competitive advantage in itself. When a company deploys an open source model on-premises, in its own data center, or within a private cloud environment, Sensitive user data Never leave your organization's secure network.
Additionally, open access to the model source code; architecture and algorithm promote transparency and trust. This allows internal teams to inspect the inner workings of models and enforce the software development lifecycle. (SDLC) Audit Identify vulnerabilities and ensure compliance with stringent industry regulations, including: Hypaa Like Dora. Such auditing is not possible with closed source black box API. Businesses simply need to trust the vendor's claims regarding security and data processing.
customization
Customization is possible through a process called Fine adjustment. Simply put, basic model (trained across the Internet) on domain-specific datasets. hallucination Achieve more accurate industry-relevant output. Proprietary data (whether it is structured or locked away across departments) siloprovides the context you need to transform your LLM from a general-purpose tool to a truly intelligent and useful asset.
Both proprietary and open-source models allow fine-tuning, but the former imposes limits on the degree of customization possible. Closed Source LLM is delivered via API. This means that all fine-tuning is done on the provider's platform without access to the underlying model weights or architecture.
Performance parity
Looking at the LLM leaderboard: LM Arenawe can see that modern closed-source models are leading the way. But when we compare modern open source models to older versions of closed source models, we often see that open source models outperform their proprietary rivals. As of this writing, open models from DeepSeek (deepseek-r1-0528), Alibaba (qwen3-235b-a22b-instruct-2507), and Moonshot (kimi-k2-0711-preview) are tied for 8th place with their own models. human (claude-opus-4-20250514- Thinking-16k).
reproducibility
Consistent and predictable output is an important goal for any AI startup. With a closed-source LLM, you don't know what's going on behind the scenes. Providers can introduce subtle changes to the model behind the APIs your app is using, causing unexpected behavior in your app or breaking your perfectly tested and polished flows.
Remember when OpenAI expanded GPT-5deprecate old APIs without much warning to developers? This move resulted in a significant proportion of broken AI apps. nevertheless the company rolled back Older models after User backlashthis incident taught us a valuable lesson that relying on a single AI provider may not be the best idea.
The open source approach makes it easy to manage reproducibility. Model versions and weights are fixed and under your organization's control, allowing you to achieve reliable and consistent output over time.
Save computing resources and the planet
The more parameters a model has, the better it is. These make you smarter. However, these hundreds of billions of parameters require huge computational resources, which are inevitably reflected in monthly API charges.
But what if you don't need a sledgehammer to crack nuts? If your use case only requires a small, specialized model that you can run locally, open source is the way to go. moreover, carbon footprint The adoption and use of an LLM is directly correlated to its size.
What about cost?
Let's get this one fact right. “Open source” is not the same as “free”. You don't have to pay licensing fees, but the costs of infrastructure, staffing, and ongoing maintenance add up quickly. Although the upfront investment is large, health carelegal, and fintech An area where user privacy is critical to business operations.
First of all, you need hardware to run open source LLM. On the other hand, for small models with only a few billion parameters, Run on a powerful laptopmodels with hundreds of billions of parameters require an enterprise-grade setup.
There are several options here – Build on-premises data centerselect private cloud or using a managed LLM platform such as Together AI, Replicate, or Google Vertex AI.
In the first scenario, you need to consider the cost of high-performance GPUs, CPUs, RAM, storage, and networking. Models with more than 110 billion parameters require multiple high-end GPUs, each with at least 80 GB of VRAM. To put it in perspective, a new NVIDIA H100 (80GB) GPU costs between $25,000 and $30,000. Multiply this by 8x or 10x for enterprise-class performance. Larger models require more powerful and expensive GPUs.
Additionally, you must consider the costs associated with running the data center, such as power, cooling, physical security, and fire suppression systems, as well as the salaries of maintenance personnel.
Private clouds, that is, renting virtual machines with powerful GPUs from cloud providers such as AWS, Microsoft Azure, and Google Cloud, require no upfront investment, but can be quite expensive in the long run. On-demand instances with a single NVIDIA A100 GPU cost between $3 and $5 per hour. A machine with eight H100 GPUs can cost more than $30 per hour. Costs are calculated based on instance uptime, data transfer, and storage. The good news is that you can always find a cloud provider that charges a little less for the same computing resources.
Finally, a managed LLM platform takes care of all the underlying infrastructure, deployment, and scaling. In this case, you don't have to worry about the complexity of managing servers and GPUs. Just sign up, choose a pre-optimized model from our library or upload your own fine-tuned model weights, and configure the endpoints to integrate the API into your app. This allows you to choose the best model for your specific task, balancing performance, speed, and cost. For example, you can use a small, fast model for simple summarization, and a large, powerful model for complex inference, all from the same platform.
In general, open source, self-hosted models have predictable monthly requests and can be operationally cost effective as interactions approach the millions. Keep in mind that it may take several years to see a positive ROI.
Consider open source AI
There is no one-size-fits-all approach when it comes to software. Each company should do its homework and weigh the pros and cons of both. Closed-source solutions shine when time to market and advanced inference capabilities are priorities. On the other hand, an open source model is best suited for highly regulated industries. In these areas, transparency into the inner workings of the model and assurance that data never leaves corporate servers is key to meeting compliance requirements.
