DeepSeek R1, the latest Chinese AI model, may never see mass adoption by U.S. companies due to regulatory concerns and security risks. But its launch has reignited a crucial conversation: should enterprises rely on proprietary AI models or invest in open-source alternatives?
If you’re planning to integrate AI into your business, this decision will impact costs, security, flexibility, and long-term scalability. Choosing the wrong approach could mean overpaying for AI services, struggling with vendor lock-in, or failing to comply with industry regulations.
This guide is for executives and tech leaders who want to understand the key differences between open-source and proprietary AI models before making their first AI investment. It covers:
- The approximate costs of running AI (with estimates for small and large businesses)
- The level of control, customization, and compliance each model offers
- Which AI strategy makes the most sense based on your company’s goals
Whether you need a quick AI solution with minimal overhead or a long-term AI strategy that gives you full control, understanding these factors will help you make the right decision.
What Does “Open-Source LLM” Really Mean?
The Open Source Initiative (OSI) defines an open-source LLM model as one that grants users four essential freedoms:
- Use the system for any purpose, without needing permission.
- Study how it works and inspect all components.
- Modify it for any purpose, including changing its outputs.
- Share the system with or without modifications, without restrictions.
However, many AI models marketed as “open-source” don’t fully meet these criteria.
Meta’s LLaMA models, for example, share their model weights but restrict commercial use and do not provide full transparency into their training datasets—disqualifying them from being fully open-source under OSI’s definition.
Another example is DeepSeek R1 itself. The company released r1’s pre-trained weights publicly, but did not release the full training data or original training code, so no one can independently reproduce the model
This blurring of open vs. closed makes it vital for business leaders to dig into an AI model’s transparency.
Don’t take an “open-source” label at face value — verify what you can actually see and do with the model before adopting it.
Rent a House or Buy It? Understanding AI Model Ownership
Comparing AI models to real estate can clarify the trade-offs.
Using a proprietary AI service is more similar to renting an apartment – you get a ready-to-use solution, but you’re subject to the landlord’s (vendor’s) rules and pricing.
In contrast, adopting an open-source AI model is akin to buying a house – you invest in hardware and upkeep, but you gain ownership: full control to customize the model and no sudden rent hikes. The convenience of renting comes with dependency, whereas ownership brings freedom at the cost of responsibility. In deciding between the two, the key consideration is how much control you need versus how much hassle you’re willing to offload.
Many enterprises begin their AI journey with proprietary models for convenience, but as AI becomes central to their business, they transition to open-source models for greater autonomy. ANZ Bank, for example, initially leveraged OpenAI’s API for experimentation but later shifted to fine-tuning LLaMA models internally for improved stability, cost control, and regulatory compliance.
How Much Vendor Lock-In Can You Accept?
Locking into a single AI provider can become a serious issue. If your AI strategy depends on a proprietary model like GPT-4 or Claude 3, what happens if:
- The vendor increases API costs significantly?
- They change terms of service that limit your use case?
- The model stops being supported or is discontinued?
AWS, Salesforce, Oracle, and SAP have started integrating open-source models for this reason. Enterprises want the freedom to switch AI providers and avoid being trapped in long-term dependencies.
Hybrid Strategies Are Gaining Traction
Also, hybrid strategies can mitigate lock-in risks. Instead of choosing one model, many enterprises combine proprietary and open-source AI.
- Salesforce introduced Agentforce, allowing users to switch between proprietary and open models within Salesforce applications.
A hybrid approach reduces vendor lock-in while keeping AI options flexible for future needs.
What are the risks with vendor lock-in in proprietary models?
Those are the biggest concerns regarding vendor lock-in for companies:
- Unpredictable pricing: API costs can fluctuate unexpectedly. OpenAI, Google, and Anthropic have all revised pricing structures multiple times, impacting enterprise budgets.
- Usage restrictions: Some vendors limit how businesses can fine-tune or deploy their models. OpenAI, for instance, currently does not allow full fine-tuning of GPT-4 Turbo.
- Service disruptions: For example, Google’s retirement of Bard in favor of Gemini forced businesses to transition to a new model, illustrating the unpredictability of proprietary AI offerings.
How Much Does It Cost to Run an Open-Source vs. Proprietary AI Model?
Before choosing an AI model, you need to understand the total cost of ownership (TCO). AI isn’t just about buying a license or running a model—it requires computing power, storage, fine-tuning, and security investments.
Here’s a comparison of the approximate costs for medium and large businesses using AI:
| Scenario | Open-Source Model (e.g., LLaMA, Mistral) | Proprietary Model (e.g., GPT-4, Claude, Gemini) |
| Medium Business (Testing Phase) | $2,000 – $10,000 (server costs, deployment setup) | $0 upfront, but $100 – $500/month for API calls |
| Medium Business (Production Use) | $15,000 – $50,000 per year (cloud hosting + maintenance) | $50,000+ per year (scaling API usage) |
| Enterprise (Custom Fine-Tuned AI) | $100,000+ (GPUs, storage, security, MLOps team) | $500,000+ per year (Enterprise API plans) |
| Enterprise (Hybrid Model) | $200,000+ (fine-tuning, hosting, integration) | $1M+ per year (API fees, dedicated support, compliance) |
Key Cost Considerations
- Proprietary models eliminate infrastructure costs but become expensive as API usage grows.
- Open-source models have higher upfront costs (servers, in-house expertise) but are more cost-effective in the long run.
- Compliance & security costs vary—open models allow for self-hosted deployment, while proprietary models come with built-in compliance but less transparency.
- Fine-tuning an open-source model requires technical resources, while proprietary models offer out-of-the-box solutions with limited customization.
What Resources Are Required for AI Deployment?
AI implementation goes beyond just selecting a model. Organizations must invest in infrastructure, talent, and governance policies to ensure a seamless deployment and a capable team that facilitates top-down enterprise adoption.
Let’s break down the key aspects you need to consider—whether you’re deploying a proprietary model or an open-source alternative.
Proprietary AI Deployment: What’s Required?
With proprietary AI models, the major cloud providers—OpenAI (Azure), Google Cloud (Gemini), AWS (Anthropic’s Claude), and IBM Watson AI—handle the infrastructure, giving businesses a plug-and-play experience. This significantly reduces the need for in-house AI architecture but introduces long-term dependencies and potential cost escalations.
To deploy proprietary models at scale, companies need:
- Cloud AI Infrastructure: Enterprise-grade AI workloads run on Azure AI, Google Vertex AI, or AWS Bedrock, which provide API access, model hosting, and fine-tuning services. These platforms handle compute, storage, and security but limit customization options.
- Enterprise AI Integration: Proprietary AI models often include pre-built API connectors and SDKs for direct integration into platforms like Salesforce, Oracle, and SAP, minimizing middleware dependencies. However, for companies managing multi-cloud or hybrid IT environments, middleware like MuleSoft, Workato, or Boomi can help orchestrate AI-powered workflows across disparate systems.
- AI Governance & Compliance Teams: Proprietary models function as black boxes, making AI governance specialists, compliance officers, and data privacy experts critical for monitoring AI outputs, ensuring fairness, and addressing ethical concerns.
- Data Science & ML Specialists: While proprietary models reduce the need for deep ML expertise, businesses still require prompt engineers, AI product managers, and domain-specific analysts to optimize API calls and fine-tune results.
- AI Strategy & ROI Optimization: Business intelligence teams work with AI strategy consultants to evaluate cost efficiency, API usage, and vendor lock-in risks.
Licensing Considerations for Proprietary Models
Proprietary AI operates under subscription-based or pay-per-use licensing models, meaning businesses rely on vendor-provided infrastructure and must adhere to specific usage terms. Before committing, enterprises should carefully assess the implications of licensing agreements, particularly in areas like data ownership, fine-tuning restrictions, pricing structures, and compliance requirements.
One of the most critical factors is data ownership and usage. Does the vendor store, analyze, or use your data for model improvement? Many proprietary AI providers claim not to train on customer data, but the fine print in licensing agreements may indicate otherwise. Companies must review these terms carefully to understand how their data is handled, whether it is retained, and if it could be leveraged to enhance future iterations of the model.
Another key limitation is fine-tuning restrictions. Unlike open-source models, proprietary AI typically restricts full model fine-tuning. Some vendors, such as Google Gemini and Claude Sonnet Enterprise, offer fine-tuning services, but the level of customization is often constrained. Others, like OpenAI, currently do not allow fine-tuning of GPT-4 Turbo, limiting enterprises to prompt engineering and API-based optimizations instead of direct model modifications.
API rate limits and pricing tiers are another crucial consideration. Costs in proprietary AI models scale with API call volume, meaning enterprises may face exponentially increasing expenses as AI adoption grows. While API-based AI offers convenience, companies must evaluate whether long-term API costs justify the ease of use compared to investing in self-hosted or open-source alternatives.
Finally, compliance and security play a significant role in licensing considerations. While proprietary AI vendors handle data security and regulatory compliance, enterprises operating in highly regulated industries must assess whether third-party AI processing aligns with U.S. compliance frameworks, such as SOC 2 for cloud security, HIPAA for healthcare data protection, and FISMA for federal information security standards. Even though these AI providers offer enterprise-grade security, sensitive corporate data must still pass through external infrastructure, which may pose additional risks depending on the use case.
Understanding these licensing factors is essential for businesses to make informed AI adoption decisions, balancing vendor convenience with cost control, customization flexibility, and long-term security considerations.
Open-Source AI Deployment: What’s Required?
Open-source AI models offer greater control and customization but require significant investment in AI architecture, security, and technical expertise.
Unlike proprietary models, open-source deployments demand a full-stack AI infrastructure, from GPUs to model fine-tuning and compliance frameworks.
To deploy open-source models, businesses need:
- AI Compute Infrastructure: Open-source AI requires high-performance GPUs (e.g., NVIDIA H100, AMD Instinct, or TPU v4 on Google Cloud). Enterprises must decide between on-premises GPU clusters (NVIDIA DGX, Lambda Labs) or cloud-hosted AI infrastructure (AWS EC2, Google Cloud TPU, or Azure ML).
- MLOps & AI Engineering Teams: Open-source demands a dedicated MLOps team to manage model training, fine-tuning, versioning, and deployment. This includes machine learning engineers, AI architects, data scientists, prompt engineers, and DevOps specialists who ensure model performance and scalability.
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- Enterprise Data Pipelines: Unlike proprietary AI, open-source models require ETL pipelines to ingest and preprocess data. Tools like Apache Spark, Databricks, Snowflake, and Airflow help manage large-scale AI data flows.
- Security & Compliance Frameworks: Open-source AI requires self-hosted security solutions, including encryption, access controls, and compliance auditing. Enterprises must establish Responsible AI guidelines for monitoring bias, hallucinations, and data privacy risks.
- Cross-Functional AI Pods: Unlike traditional IT teams, modern AI teams operate in AI pods, integrating data engineers, ML researchers, AI product managers, and domain experts. These agile AI development teams continuously refine models and optimize deployment.
Licensing Considerations for Open-Source Models
While open-source AI offers greater flexibility, licensing terms vary, affecting model modification, fine-tuning, and redistribution. Apache 2.0-licensed models, like Mistral and Falcon, allow full commercial use, fine-tuning, and redistribution, making them ideal for enterprise deployment. In contrast, Meta’s LLaMA models provide pre-trained weights but restrict commercial redistribution, requiring businesses to navigate licensing agreements carefully.
Some models, like DeepSeek R1, share model weights but withhold full training data and original code, limiting transparency and reproducibility. Additionally, custom licenses may impose restrictions on modification, training, or resale, making legal review essential before enterprise adoption. Understanding these distinctions helps businesses ensure compliance and avoid unintended limitations when deploying open-source AI.
Why Understanding Licensing Is So Critical
Before committing to an AI model provider, organizations must conduct a full license analysis to determine:
- Who owns the data used in AI training and inference?
- What are the model’s modification rights—can you fine-tune it for your use case?
- Are there restrictions on deploying AI on-premises, or does the vendor require cloud-based usage?
- Does the license allow embedding AI into commercial products?
- Are there clauses that allow vendors to change terms, pricing, or availability in the future?
Inclusion Cloud helps enterprises navigate AI licensing complexities by providing expert guidance on technical implications, security risks, and the right AI model for your business. With our AI-powered talent engine, we also source top-tier AI specialists—ensuring you have the expertise to build, deploy, and govern AI effectively.
We deliver pre-vetted experts in just 72 hours to help you get started with AI or scale your current projects.
Final Takeaways: Which One Should You Choose?
Use proprietary AI if you need fast deployment, vendor support, and lower initial complexity.
Use open-source AI if you need full control, cost predictability, and customization.
Consider a hybrid approach if you need a balance of compliance, flexibility, and scalability.
If you’re unsure which route is best for your business, book a call with Inclusion Cloud today—our experts can help you evaluate your options, optimize your AI strategy, and build the right team to execute it successfully.
