Comparing Total Cost of Ownership: Open Source vs Proprietary LLMs for Canadian Enterprises 

Is the price tag really the price? 

On the surface, comparing open-source vs proprietary large language models (LLMs) might seem like a simple licensing decision. One is “free,” the other comes with a monthly invoice. But when Canadian enterprises start deploying LLMs at scale, the real costs emerge, across infrastructure, talent, compliance, security, and long-term vendor dependence. 

What looks affordable today might create expensive roadblocks tomorrow. And what seems costly up front could actually be the smarter investment over time. 

This post breaks down the total cost of ownership (TCO) between open source and proprietary LLMs so you can make an informed, strategic decision. Whether you’re leading IT, compliance, or strategy, by the end, you’ll know how to align your LLM investment with your business priorities. 

What Is Total Cost of Ownership in the Context of LLMs? 

Total cost of ownership (TCO) isn’t just about what you pay upfront It helps organizations avoid surprise expenses and choose models that align with strategic priorities.  

Total cost of ownership is often misunderstood. It goes well beyond what you pay upfront and includes the full scope of hidden and ongoing costs. We’ll break down what TCO really includes, why it’s more than just licensing fees, and how operational and compliance obligations can quietly inflate your AI investment. 

When evaluating an LLM deployment, cost isn’t just about the sticker price. 

TCO considers everything: 

• Licensing or usage fees
• Hardware and compute infrastructure
• Customization and integration
• Security, compliance, and risk mitigation
• Support and maintenance
• Talent and training

For Canadian enterprises especially, these costs are compounded by requirements around data residency, privacy regulations, and multilingual support. 

Curious about deploying secure AI in Canada? Explore our enterprise AI solutions. 

Open Source LLMs: Cost Benefits and Trade-Offs 

 
Open source models offer unmatched flexibility, but they also demand a serious look at internal capabilities, resources, and risk exposure. 

Open source models can unlock major cost advantages and customizability, but only if you’re ready for the workload they bring. From infrastructure to security and internal expertise, there are serious demands to consider. Here’s what you need to know about the trade-offs involved. 

Lower Licensing, Higher Control 

Open source LLMs like LLaMA, Mistral, or Falcon don’t come with licensing fees. That’s their biggest draw. You’re not locked into a vendor’s roadmap or data policies. 

For businesses with strong internal AI teams, this can be a major advantage. You own the model, the deployment, and the data. But “free” gets complicated: 

• Engineering time to fine-tune and deploy models 

• Infrastructure costs for GPU clusters or cloud compute 

• Time spent maintaining, updating, and securing models 

Customization and Data Residency 

Open source LLMs are extremely flexible. Want to tailor a model to Canadian French? Fine-tune it for legal, healthcare, or finance workflows? Go for it. 

They’re ideal for sectors dealing with: 

• Strict compliance rules 

• Data privacy mandates 

• Canadian data residency requirements 

Security and Compliance Responsibility 

With control comes responsibility. Open source models lack built-in safety layers. You’re accountable for: 

• Monitoring for hallucinations and harmful output 

• Applying ethical guardrails 

• Rapidly patching security issues 

Need help managing AI risk and compliance? Talk to our AI governance experts. 

Proprietary LLMs: Ease, Support, and Predictability 

 
Proprietary models deliver immediate value through support and simplicity, but they come at a premium and with less control. 

Proprietary LLMs promise speed, reliability, and less hassle, making them attractive for companies that want fast results and dependable support. But that convenience can come with long-term costs and constraints. Here’s what you’re really getting when you go proprietary. 

Faster Time to Value 

With OpenAI, Claude, or Gemini, you’re buying polish and convenience. These models are battle-tested, stable, and ready to use. 

Advantages include: 

• API-ready integration 

• No in-house ML team required 

• Prebuilt features like summarization and translation 

This means faster deployment and less internal friction. 

Support and Predictability 

Enterprise contracts come with: 

• Technical support and implementation guidance 

• Compliance documentation 

• Uptime SLAs and latency guarantees 

Proprietary vendors handle the backend, so you don’t have to. For resource-constrained teams, that’s worth the investment. 

Vendor Lock-In and Privacy Limitations 

You’re locked into someone else’s roadmap. Some vendors log your queries. Others train on your data unless told otherwise. 

For privacy-focused sectors like law, finance, or healthcare, this is risky. And switching later? Painful. 

Evaluate vendor risks with our AI deployment audit. Schedule a free consultation. 

Open Source vs Proprietary LLMs: Cost Comparison 

This is where the numbers speak for themselves, a clear, apples-to-apples look at what each model type really costs over time. 

Cost comparisons between open source and proprietary models often reveal surprising long-term implications. With real-world examples and apples-to-apples breakdowns, you’ll see how each choice stacks up, and which one better fits your business model. 

Cost Category	Open Source LLM	Proprietary LLM
Licensing	$0	Monthly or usage-based fees
Infrastructure	In-house/cloud (self-managed)	Included or outsourced
Customization	Full flexibility	Limited to vendor constraints
Security/Compliance	Fully internal responsibility	Shared with vendor
Support	Community-based or self-built	Vendor SLAs and enterprise support
Talent Needs	High – ML engineers, DevOps, MLOps	Low – general devs or business ops
Vendor Lock-in	None	High

When Open Source Saves Money 

Use open source when: 

• You have a strong internal team 

• You need deep customization 

• You must keep data in Canada 

• You plan to scale over years, not months 

Hybrid LLM Strategy: The Best of Both Worlds? 

 
For many Canadian businesses, the answer isn’t open source or proprietary. it’s both. Hybrid strategies offer strategic flexibility. 

Blending open source and proprietary models can help you optimize for both flexibility and performance. It’s an increasingly popular approach that avoids vendor lock-in while still leveraging best-in-class features. Let’s look at how Canadian enterprises are making this model work. 

How Canadian Enterprises Combine Both 

Hybrid strategies are gaining traction. Common examples include: 

• Open source for internal tools and sensitive data workflows 

• Proprietary APIs for low-risk functions like summarization 

• Orchestration layers to switch models based on context 

This lets you balance performance, cost, and control. 

Technical Considerations 

Hybrid strategies are powerful, but they require: 

• Solid integration planning 

• Model monitoring infrastructure 

• Legal reviews for both open and closed environments 

Still, for many, the flexibility and efficiency are worth it. 

Build your hybrid AI roadmap. Let us show you how. 

What Should C-Level Executives Do Next? 

Understanding total cost of ownership is only useful if it leads to action. It’s time to turn insight into execution. 

Now that you understand the cost variables, the next move is strategic. Executive teams need to weigh internal capacity, risk tolerance, and business needs to make the right call. Here’s how to turn analysis into action. 

Ask the Right Questions 

Before choosing a model type, executives should ask: 

• Do we require full data sovereignty? 

• Are we subject to Canadian compliance laws? 

• Can we support open source infrastructure? 

• What’s our AI maturity and talent capacity? 

• How fast do we need to launch? 

Decision Matrix (Simplified) 

Priority	Best Fit
Speed to deploy	Proprietary
Long-term cost control	Open Source
Data privacy	Open Source
Enterprise support	Proprietary
Custom features	Open Source
Balanced needs	Hybrid

You don’t need to choose immediately. Pilot both. Compare outcomes. 

Start small. Test fast. Scale what works. Talk to our AI advisors. 

Ready to Take Action on Your LLM Strategy? 

You wouldn’t buy a car without checking the maintenance schedule, right? Same goes for LLMs. What looks cheaper now could cost you later. 

Ready to build smarter with AI? Let’s get started.