Non-Technical Guide To Open-Source LLMs

In early 2025, when everyone thought that leading Western AI labs like OpenAI and Anthropic were unbeatable, the Chinese DeepSeek models shook everything up. 

They were cheaper, they were open, you could run them locally, and the performance was eerily close to models from OpenAI.

But excitement with DeepSeek, as well as open-source LLMs in general, has somewhat fizzled out. As of mid-2025, enterprise companies are spending nearly $8.4 billion (up from $3.5 billion in 2024) for API access to Large Language Models, and DeepSeek models make up just 1% of that amount. 

Closed-source models prevailed. Only 13% of AI workloads today use open-source models, with Meta’s Llama models leading the pack. 

Open-source models have their advantages and work great in specific use cases, even if they aren't the best fit when bleeding-edge performance is necessary.

Plus, the open-source LLM ecosystem keeps evolving, so it’s good to stay up to date and understand when they make business sense, what challenges they bring, and how to get started with them.

What Are Open-Source LLMs? Core Concepts Explained

If you've used ChatGPT, you've already experienced what a large language model can do. But understanding what makes an LLM "open-source" requires going beneath the surface.

When we talk about open-source LLMs, we mean models whose weights are publicly available for anyone to use, modify, and distribute, though models may require approval or restrict certain commercial uses. 

This is fundamentally different from traditional open-source software, where you get access to the source code itself. With LLMs, you're getting access to the finished, trained model - the result of millions of dollars and massive computational resources spent on training.

It’s important to always check the license attached to the model, as not all of them come with permissive licenses like Apache 2.0. Understanding these nuances affects what you can legally do with a model and how much vendor dependency you're actually avoiding.

Key Concepts To Know About Open-Source LLMs

Let's break down a few technical terms.

First, model weights, training data, and code. The weights are the trained model itself. The training data is what was used to teach it (often kept private). The code represents the model’s architecture and inner workings. Most open-source LLMs give you the weights and code, some also provide training data. For business purposes, access to weights is the key thing.

What are parameters? You'll see models described as "7B," "70B," or "405B" - that's billions of parameters. Roughly speaking, more parameters often mean more capability, but also more computational power needed to run them. A 7 billion parameter model might handle customer support queries efficiently, while a 70 billion parameter model could tackle complex legal analysis.

What’s the difference between inference and training? Training is creating the model from scratch - extremely expensive and unnecessary for almost all businesses. Inference is using an already-trained model to generate outputs, which is what you'll be doing. Understanding this distinction is critical because the infrastructure and cost discussions around open-source LLMs are almost entirely about inference, not training.

An exception here is fine-tuning, which means using a high-quality, specialized dataset to train an existing model to help it handle a niche use case. It’s still an investment, but exponentially smaller than training a model from scratch.

Finally, do you need to set up your own infrastructure, either on-premise or cloud, to be able to use open-source LLMs? The answer is no. Platforms like Together.ai or OpenRouter can do it for you, and all leading cloud providers (AWS, Google Cloud, Azure) offer managed services that simplify development and deployment of open models. 

The Best Open-Source LLMs and Top Open LLM Leaderboards

Finding the right open-source LLM for your use case might be difficult considering how many of them are out there.

Leaderboards might come in handy. According to the LLM Stats leaderboard, which seems to be the most comprehensive, these are the top open LLMs as of September 2025:

• Qwen3-235B-A22B-Thinking-2507

• DeepSeek-R1-0528

• GPT OSS 120B

• DeepSeek-V3.2-Exp

• GLM-4.5

• Llama 3.1 Nemotron Ultra 253B v1

• Kimi K2 Instruct

These models consistently rank high in other leaderboards. Different leaderboards offer different benchmarks and characteristics of models, so you might find more information relevant to your case on:

• The Dubesor LLM Benchmark table

• The LMArena leaderboard

• The Open LLM Leaderboard by Vellum (particularly useful for evaluating models by different tasks or performance metrics)

The open-source LLM landscape changes rapidly. There was a time when the French Mistral models were leading the open LLM category, currently Chinese models like Qwen and DeepSeek dominate. Few people expected OpenAI to ever release an open model, but their GPT OSS is now competing with the best of them, alongside powerful open models from Meta and Google.

Does a high score on the leaderboard automatically mean that a model is good for your use case? If only it were so simple. Sadly, leaderboards and benchmarks can only go so far in informing your decision. 

You’re going to have to test and evaluate different models to find the right one. A good starting point is to chat with various models on a platform like LMArena or OpenRouter.

To help you make the right choice, here are a few bonus recommendations from GitHub’s AI experts:

• Test how good a model is at the particular task you care about the most,

• Consider how much the model costs to run and its overall latency rates.

Benefits of Open-Source LLMs

There are several reasons why companies might opt for open-source LLMs:

• Cost control,

• Data privacy and security,

• Customization, 

• Transparency and risk management,

• No vendor lock-in,

• Community support.

Cost Control and Predictability

Proprietary models typically charge per token, and those costs multiply quickly at scale. For companies running high-volume operations, this can create two problems: escalating costs and unpredictable budgets.

Simply put, with open-source LLMs you might be able to achieve similar performance at a lower budget.

Data Privacy and Security

When you send data to a third-party API, you're trusting an external provider with potentially sensitive information - customer records, proprietary strategies, confidential communications, or regulated data.

Open-source LLMs can be deployed on private infrastructure, ensuring sensitive information never leaves your environment. This can be an essential requirement in highly regulated industries.

For example, one pharmaceutical company used closed LLMs for internal chatbots, but deployed a secure Llama model specifically for flagging personally identifiable information.

Customization and Competitive Differentiation

With proprietary APIs for leading models, you usually get the same tools everyone else uses with limited customization options. With open-source models, you can fine-tune them on your own data to create genuine competitive differentiation.

Fine-tuning with domain-specific data enhances relevance and accuracy, improving contextual understanding for your specific business. A law firm can train models on their case precedents, a manufacturer can teach models their technical specifications, or a retailer can embed their brand voice and product knowledge.

Transparency and Risk Management

Unlike black-box proprietary models, open-source LLMs provide more visibility into model architecture, training data, and algorithms. This transparency enables detailed audits to ensure model integrity and performance, which may be important for governance and risk management.

When something goes wrong with a proprietary API, you're dependent on the vendor to diagnose and fix it. With open-source models, your team can investigate issues, find root causes, and implement fixes. This level of control can come in handy.

No Vendor Lock-In

Strategic flexibility matters. With proprietary services, you're exposed to pricing changes, feature deprecation, service discontinuation, or shifts in vendor priorities. Prominent AI labs alter their pricing, change API behaviors, or sunset older models usually without warning, leaving customers scrambling to adapt.

Open-source LLMs eliminate vendor lock-in scenarios, giving you flexibility to switch between models or providers as technology evolves. If a better model emerges, you can migrate. If one approach isn't working, you can pivot. The agency is yours.

Access to Innovation and Community

The open-source AI community moves fast. Since early 2023, new open-source model releases have nearly doubled compared to closed-source counterparts. This means rapid innovation, with new techniques, optimizations, and capabilities emerging constantly.

The community provides shared knowledge, best practices, third-party tools and integrations, and peer benchmarking. You're not alone in solving problems - thousands of developers and organizations are working on similar challenges and sharing solutions.

Open-Source LLM Challenges

Open-source LLMs are not a plug&play solution, and anyone who tells you they're "free" is misleading you. The models are free to download, but deployment, infrastructure, maintenance, and expertise all have real costs. Understanding these challenges upfront is essential for making sound decisions.

For example, take a look at a quick breakdown of cost considerations for several leading open-source models from the Latitude blog:

• LLaMA 3: Custom license with restrictions; high GPU and memory needs; strong community but no official support.

• Falcon 180B: Apache 2.0 license; requires enterprise-grade GPUs; smaller community, higher scaling costs.

• Mixtral 8x22B: Apache 2.0 license; uses a mixture-of-experts design to lower costs; growing community support.

• Yi-34B-Chat: Apache 2.0 license; moderate costs; limited community and support options.

• Mistral: Dual licensing (open-source and commercial); efficient on standard GPUs; strong community and optional enterprise support.

Infrastructure and Technical Requirements

Running LLMs requires serious computational power. You can get some personal usage out of a local LLM hosted on your laptop, but for business usage you’ll need GPU resources, either in the cloud or on-premise, and enterprise-grade hardware for larger models.

The initial setup complexity is real. Unlike making an API call, you need to configure serving infrastructure, set up model deployment pipelines, implement monitoring systems, and establish maintenance procedures. This infrastructure management introduces both complexity and ongoing operational expenses.

The True Total Cost of Ownership

Here's where many organizations get surprised. You can download a model for free, but expenses like infrastructure, licensing, scaling, and support are not free.

The hidden costs include ongoing model evaluation and testing, debugging edge cases, handling model updates and versioning, scaling infrastructure as usage grows, and security and compliance implementation. These operational costs may exceed initial hardware investments. According to one expert’s analysis from June 2025:

• Even a minimal internal deployment can cost $125K–$190K/year.

• Moderate-scale, customer-facing features? $500K–$820K/year, conservatively.

• Core product engine at enterprise scale? Expect $6M–$12M+ annually, with multi-region infra, high-end GPUs, and a specialized team just to stay afloat.

• Hidden taxes include: glue code rot, talent fragility, OSS stack lock-in, evaluation paralysis, and mounting compliance complexity.

• Most teams underestimate the human capital cost and the rate of model and infra decay. OSS gives you flexibility   until you’re too deep to pivot.

That’s why due diligence is important - you have to crunch the numbers to understand whether an open-source model or a proprietary API makes more sense in your case. 

Performance and Quality Trade-offs

Smaller open-source models will underperform compared to flagship proprietary models from OpenAI and Anthropic on complex reasoning tasks. The gap has narrowed somewhat, open-source models are catching up in some benchmarks, but trade-offs remain.

The key is matching model size and capability to your specific use case. Performance depends heavily on the specific use case and requirements, there's no universal "best" model.

You need robust evaluation processes, validation workflows before production deployment, continuous monitoring of output quality, and contingency plans for when models fail.

Support and Responsibility

When something breaks with a proprietary API, you can contact support. With open-source models, you own the problem. There's no vendor support line, you're dependent on community resources or your own expertise.

This means you need internal capability to diagnose issues, debug problems, and implement solutions. Community documentation varies in quality, and for less common models or edge cases, you might be pioneering solutions.

The flip side? You're not waiting on a vendor's timeline for fixes, and you can implement workarounds at your own pace. But the responsibility is entirely yours.

Compliance and Legal Considerations

License terms matter. Some models have custom licenses with usage restrictions or commercial limitations. Meta's Llama family, for instance, has restrictions for organizations above certain revenue thresholds. Understanding license requirements for your specific use case, particularly around commercial use and redistribution, is essential.

Beyond licensing, regulatory compliance is your responsibility. Companies must ensure compliance with data privacy laws and regulations like GDPR, and industry-specific requirements like HIPAA. This involves data minimization practices, content filtering systems, privacy-centric fine-tuning, and ongoing monitoring and audit processes.

Output liability is another consideration. You're responsible for what your model generates, including potential issues with accuracy, bias, inappropriate content, or incorrect advice. Having clear terms of service, disclaimers, and human review processes becomes necessary.

Security Challenges

Open-source LLMs present certain security challenges due to publicly available model weights and architecture. This transparency attracts both collaborators and potential attackers who can study the system for vulnerabilities.

Implementing rigorous security measures is key, including system isolation to prevent unauthorized access, real-time monitoring for suspicious activities, established security protocols aligned with enterprise standards, and regular security audits and updates.

The security responsibility extends beyond the model itself to the entire infrastructure stack - from data storage to API endpoints to monitoring systems.

Open-Source LLM Planning Considerations

These challenges aren't reasons to avoid open-source LLMs, but they’re factors to plan for. The organizations succeeding with open-source approaches are those who:

• Start with clear use cases and success metrics

• Honestly assess in-house technical capacity and budget for partners

• Calculate true ownership cost including hidden costs

• Implement rigorous testing and quality processes

• Plan for iteration and continuous improvement

• Build or acquire the necessary expertise

Open-source LLMs come with baggage, so you have to establish whether your organization has the use case, scale, and capability to turn those challenges into manageable implementation details - and whether the benefits justify the effort.

As of mid-2025, the answer is “no” for most organizations, and accessing models from OpenAI, Anthropic or Google through proprietary APIs is the popular choice. But thousands of brilliant engineers are constantly tinkering with open-source LLMs and building new ones, so you shouldn’t ignore this space. Trends in the AI industry change rapidly.

How To Choose Open-Source vs Proprietary LLM

After understanding what open-source LLMs offer, the practical question remains: when should you choose them over proprietary alternatives like GPT, Claude, or Gemini? Here's a framework for making that decision.

When To Choose Proprietary Models

You need cutting-edge performance immediately. Proprietary models like GPT and Claude lead in raw performance and capabilities, particularly on complex reasoning tasks. If your application demands the absolute best performance and you can't wait for open-source models to catch up, proprietary options deliver.

You have limited technical resources. Proprietary LLMs offer basically plug&play solutions with simple API and platform-based integrations. If you lack ML engineering expertise and don't have a budget for specialized consultants, the ease of proprietary services is attractive.

Rapid deployment is critical. For creating Proof of Concepts or MVPs, proprietary models enable faster integration and deployment. When time-to-market is the priority and usage is limited initially, accessing these models through APIs is easier.

When To Choose Open-Source Models

Data cannot leave your environment. If you're in a regulated industry handling sensitive data (healthcare, finance, legal), open-source models deployed on-premises keep data under your control. No third-party data sharing required.

You need deep customization. Open-source models allow fine-tuning on domain-specific data and modification of model behavior in ways proprietary APIs simply don't permit. If general capabilities aren’t enough, customization comes in handy.

Predictable cost structure is essential. Open-source models shift costs from usage-based pricing to infrastructure expenses. For budgeting and financial planning, this offers predictability that may be valuable.

You have technical capacity. If you already have AI engineers, MLOps expertise, or budget for specialized partners, you can capture the benefits of open-source without being overwhelmed by the complexity.

The Hybrid Approach To LLMs

In the complex reality of many companies, hybrid strategies might be the best choice. Use proprietary models for certain aspects where security, reliability, and support are paramount, and open-source models for parts where customization and cost control matter most.

Common hybrid LLM usage patterns:

• Proprietary for customer-facing, open-source for internal - use a closed model for customer interactions where quality is paramount, while deploying an open model for internal tools and high-volume processing.

• Proprietary for prototyping, open-source for production - rapidly develop with easy-to-use proprietary APIs, then migrate to self-hosted open-source models once usage scales.

• Proprietary for general tasks, open-source for specialized - use proprietary models for broad capabilities, while fine-tuning open-source models for domain-specific expertise.

Open-Source LLMs as a Strategic Tool

There are so many different open-source LLMs to choose from that it can make your head spin. Definitely a lot of risk for falling into analysis paralysis here, so proceed with caution.

For most organizations, the rational choice right now is to just go with the leading closed models from OpenAI or Anthropic. But if you’ve made up your mind about wanting to use open LLMs, here’s what to do:

• Identify your specific use case - what problem are you trying to solve, and what are your requirements?

• Run the numbers thoroughly - calculate closed model token usage VS open model infrastructure, optimization and maintenance costs.

• Assess your technical capacity - do you have the necessary expertise on board, is it viable for you to experiment with open LLMs?

• Start small and iterate - start with a pilot project, test with platforms like Together.ai or OpenRouter, and validate your model choice and approach before going all-in.

• Stay informed - keep following developments in open models, as the community continues to innovate at an impressive pace.

Open LLMs aren't for everyone, and they're certainly not "free." They're one of the many options you can choose from when you want to integrate LLM capabilities into your digital products, internal systems, or daily workflows.