Every AI tool you see today, from chatbots, summarizers, to voice assistants, is powered by one thing: a large language model (LLM).

LLMs are the real engine behind modern AI. They understand language, generate ideas, and automate complex workflows.

Today, as more businesses start building AI into their workflows, one critical question keeps coming up:

Do we use an open-source LLM or go with a commercial model like GPT-4?

On the surface, this appears to be a technical choice. But, in reality, this choice has a direct impact on your budget, roadmap, and autonomy.

One gives you control. The other gives you convenience.

The real question is: what matters more for your business?

This guide helps you think through that decision clearly. We break down what open-source LLMs are, how they compare to closed models, where they excel, and the trade-offs to consider so that you can evaluate them with clarity.

Open-source LLM is an AI model whose source code and training data are publicly available for use. This means anyone can download, modify, fine-tune, or deploy the model wherever they choose. Unlike closed-source models such as GPT-4 or Claude, there are no licensing fees or gated APIs.

Open-source LLMs have gained popularity in the developer and research communities, as they can be used, modified, and improved to meet specific objectives.

Both open-source and closed-source LLMs offer advanced text generation and reasoning capabilities. But the way you access, use, and manage them differs significantly. 

Let’s understand this through a head-to-head comparison. 

If you’re looking to employ open-source LLM in your business, here are 6 enterprise use cases you can start with: 

Create conversational AI agents

Open-source LLMs allow you to build AI agents that go beyond simple chat responses. 

For example, an e-commerce company can deploy an AI agent for customer support, trained on its order system, return policies, and FAQs. 

The agent can check order statuses, initiate returns, and answer common queries in real time. Automating customer support reduces the load on human agents, improves response times, and provides consistent support. 

Generate marketing content

Open-source LLMs can streamline your marketing efforts by generating blog posts, crafting ad copy, and analyzing market trends. 

For example, a marketing team can use an LLM to draft initial versions of campaign content, which can then be refined by human editors, accelerating the content creation process.

Sentiment analysis

When fine-tuned on your domain-specific data, open-source LLMs can power AI agents that recognize customer emotions, like frustration or urgency, while resolving customer queries.

For example, if a customer writes, “This is the third time my package is delayed. What’s going on?” the AI agent can detect the frustration in the message and automatically flag it for priority handling or escalate it to a human agent.

Language translation

Open-source LLMs can be fine-tuned to handle accurate, context-aware translations tailored to your industry. 

For example, a healthcare provider operating across regions can train a model on its medical records and consent forms to ensure that patient communication remains accurate across languages. 

Unlike off-the-shelf translation tools, this approach preserves domain-specific terminology and tone while keeping all sensitive data within your infrastructure.

Code generation

You can use the LLM to assist developers in generating code snippets, suggesting fixes, or improving the existing code base. 

With this, your dev team can review and implement code changes faster, reduce errors, and focus on solving complex problems. 

Summarize internal docs

LLMs can process dense internal documentation and generate concise, role-specific summaries. 

For example, customer success teams can utilize the LLM to build custom AI agents that summarize 30-minute onboarding calls into concise, CRM-ready notes, including key concerns and actionable next steps. 

This saves hours of manual work, ensures context isn’t lost, and makes knowledge more accessible across teams, such as sales, product, and support.

The open-source LLM ecosystem is evolving fast. New models are being released every few months, and some are quickly closing the gap with their commercial counterparts.

Here are 6 popular open-source LLMs available on the market today:

Llama 3

LLaMA 3 is Meta's most advanced open-source LLM to date, trained on approximately 15 trillion tokens. The 70B model outperforms comparable models, such as the Gemini Pro 1.5 and Claude 3 Sonnet, on various benchmarks. The 405B variant, released in July 2024, is designed for high-level research and offers enhanced capabilities in language and mathematical problem-solving.

• Developer: Meta

• Model sizes: 8B, 70B, and 405B parameters

• Context window: Up to 128,000 tokens

• License: Open-source with community license

Grok 1

Grok-1 is xAI’s first open-source LLM, designed to handle a broad range of language and reasoning tasks. Released under the Apache 2.0 license, it marked a significant move toward transparency and community-driven development. While not as advanced as Grok 1.5, it demonstrates strong general-purpose performance and provides a solid foundation for experimentation and fine-tuning.

• Developer: xAI

• Model size: 314B parameters

• Context window: 8,192 tokens

• License: Apache-2.0​

Falcon 2

Falcon 2 is an open-source, multilingual, and multimodal LLM with vision-to-language capabilities. The 11B model outperforms Meta’s LLaMA 3 8B and performs on par with leading models like Google's Gemma 7B. Plans are underway to enhance Falcon 2's capabilities further by incorporating a mixture-of-experts architecture.

• Developer: Technology Innovation Institute (TII)

• Model sizes: 11B parameters

• Context window: 8,000 tokens

• Variants: Falcon 2 11B (Text-only, multilingual LLM) and Falcon 2 11B VLM (Multimodal LLM with vision-to-language capability)

• License: TII Falcon License 2.0

Mixtral 8x22B

Mixtral 8x22B is Mistral AI’s most advanced open-source large language model, released under the Apache 2.0 license. Utilizing a sparse Mixture-of-Experts (MoE) architecture, it activates only 2 of its 8 expert pathways per token, enabling high performance with reduced computational cost. The model supports multilingual tasks, advanced reasoning, and code generation. 

Its architecture allows for efficient processing, making it suitable for large-scale applications without the infrastructure demands of dense models.

• Developer: Mistral AI

• Model size: 141B total 

• Context window: 65,536 tokens

• License: Apache 2.0

GPT-NeoX-20B

GPT-NeoX-20B is a 20-billion-parameter dense autoregressive language model developed by EleutherAI. Trained on the Pile dataset and released with open weights under a permissive license, it delivers strong performance in language comprehension, math reasoning, and knowledge-driven tasks.

• Developer: EleutherAI

• Model size: 20 billion parameters

• Context Window: 8,192 tokens

• License: Apache 2.0

BLOOM

BLOOM is a 176 B-parameter multilingual LLM developed by the BigScience collaboration, designed to support inclusive language research across 46 natural and 13 programming languages. Unlike many large models, BLOOM was built with transparency and global accessibility in mind, using publicly documented datasets. It remains one of the largest open-weight models available, with strong performance in translation, summarization, and multilingual generation tasks.

• Developer: BigScience (Hugging Face-led initiative)

• Model size: 176B parameters

• Context window: 2,048 tokens

• License: BigScience RAIL license

Now that we've covered use cases and top models, let's delve deeper into the specific advantages of adopting open-source large language models (LLMs):  

Cost savings

One of the major benefits of using open-source LLMs is the ability to access models under a free license. This eliminates API or subscription costs, making LLM adoption more feasible. 

Collaboration and Innovation

Open-source LLMs foster a strong culture of collaboration. Because their design, data, and pretrained versions are publicly available, developers and researchers from different industries can contribute improvements. For example, if you're building a support bot and struggling with multilingual accuracy, someone in the community might have already released a fine-tuned version for that language, saving you weeks of development and making your deployment more effective.

Transparent architecture

With open-source LLMs, you're not working with a black box. You can review the model's architecture, examine the training data, and understand how it generates outputs. This level of access enables you to identify issues such as bias or misinformation and provides you with the tools to correct them. 

For businesses operating in regulated or high-risk environments, this transparency is crucial for establishing trust, maintaining accountability, and ensuring control.

While open-source LLMs offer transparency, flexibility, and cost savings, their deployment presents significant operational and technical challenges. 

Here are the 5 major challenges you’ll face while implementing open-source LLM in your enterprise:

Hallucinations

Open-source LLMs are more prone to hallucinations, where the model generates responses that sound accurate but are not based on facts. Many models also struggle with longer conversations or documents, losing track of earlier context. 

This becomes risky in enterprise scenarios where consistency and correctness are critical. Without advanced guardrails or fine-tuning, these models can cause errors, misinformation, and confusion in production environments.

Hidden infrastructure costs

Although there are no licensing fees, operating an open-source LLM still incurs significant expenses. You need skilled engineers to deploy and manage the model, as well as GPU resources for inference, and ongoing maintenance to keep the systems stable. 

These costs grow with usage, especially as you scale to support multiple teams or products. What looks inexpensive at the start can turn into a major operational and financial investment over time.

Lower performance compared to commercial LLMs

Most open-source LLMs still fall short of commercial models in areas like accuracy, reasoning, and reliability. 

They often struggle with following instructions consistently or handling edge cases out of the box. While fine-tuning can help, it takes time and resources to match the quality of models like GPT-4 or Claude. 

For teams that require production-ready results from the outset, this performance gap can be a serious limitation.

Intellectual property issues

Open-source LLMs are often trained on massive datasets scraped from the web, including books, articles, and code, much of which may be protected by copyright. 

If the training data includes proprietary content, any outputs derived from it could expose your business to legal risk. 

Without clear documentation or proven data, it is challenging to verify whether a model is safe for commercial use. This creates uncertainty around ownership, compliance, and monetization rights.

No Support or SLAs

With open-source models, you’re entirely on your own. There’s no official support team to escalate issues to, no uptime guarantees, and no one accountable if things break in production. 

Any fixes, updates, or optimizations must come from your internal team or the community, and timelines are unpredictable. For businesses that rely on stability, this lack of support can delay launches, increase risk, and make it harder to scale reliably.

Deciding whether to use an open-source LLM depends on your company's goals, technical resources, and long-term plans.

If your team includes skilled machine learning engineers and you can manage infrastructure and ongoing maintenance, open-source LLMs are likely a strong choice. You’ll gain transparency, flexibility, and complete control over the model’s data and functionality.

Closed-source LLMs work well if you prioritize rapid deployment, scalability, predictable costs, and dedicated provider support. However, these benefits come at the expense of limited customization, restricted visibility into the model’s workings, and dependency on a vendor’s roadmap.

But if you’re looking to skip the complexity of managing open-source LLMs while still building powerful AI experiences, pagergpt is designed for you.

With pagergpt, you don’t need to manage infrastructure, fine-tune models, or worry about uptime. You get access to advanced LLMs like GPT-4, along with full customization of workflows, branding, and user experience. Everything is ready to use from day one.

Whether you're automating support, qualifying leads, or embedding AI chat on your website, pagergpt gives you the tools to move fast and scale confidently. You get features such as lead capture, a shared live inbox, knowledge integration, advanced training, and in-depth analytics.

What’s more? You can try pagergpt firsthand with no upfront commitment and pay as you scale. 

Want to see pagergpt in action? Book a demo today.

What is a LLM token limit?

A token limit defines how much text an LLM can process at once, including inputs and responses. One token can be a word or part of a word. Limits vary by model, from 2,000 to 128,000 tokens.

What are open-source LLMs?

Open-source LLMs are AI models trained on large text datasets, with their code and weights publicly available. This openness allows developers to use, modify, and deploy them freely, facilitating customization and transparency in various applications.

Is BERT an LLM?

BERT is a large language model developed by Google. Unlike autoregressive models like GPT, BERT employs bidirectional encoding to comprehend context, making it particularly effective for tasks such as question answering and language understanding.

Are open-source LLMs secure?

Open-source LLMs offer transparency but may lack dedicated security measures. Without proper oversight, they can be vulnerable to misuse or data leaks, emphasizing the need for careful implementation and monitoring.

Is ChatGPT an LLM or generative AI?

ChatGPT is a generative AI application built upon large language models like GPT-4. It generates human-like text responses, making it both an LLM and a generative AI tool.