Open source vs proprietary AI models - why free costs more

Everyone thinks open source AI models are free.

They’re not. They just move your costs from the vendor invoice to your ops team. I’ve seen companies spend months building what they thought would be a budget-friendly AI implementation, only to discover they’ve created an expensive engineering project that needs constant feeding.

The open source vs proprietary llm debate isn’t about licensing costs. It’s about whether you want to pay for AI capability or pay to build AI infrastructure.

The operations tax nobody mentions

Here’s what happened to a company I know. They picked Llama 2 because it was “free” compared to GPT-3.5 Turbo. Their actual costs ended up 50% to 100% higher. They are not alone. 84% of enterprises report AI costs eroding gross margins by more than 6%, and a big chunk of that erosion comes from underestimating operational overhead.

Why? Infrastructure.

Running production AI on open source means you are buying and managing GPU servers. A basic deployment needs dedicated hardware. Most enterprise budgets underestimate true AI TCO by 40-60%, and the infrastructure piece alone annualizes to significant sums. And that is before you factor in the engineering time.

You need people. Not contractors you call occasionally. Actual staff. And data preparation alone eats 60-75% of total project effort in AI initiatives - the most underestimated component across the board.

Software engineers to maintain integrations and APIs consume about a third of someone’s time. MLOps specialists to handle deployment and monitoring take another fifth. These fractional needs sound efficient until you realize most teams end up hiring full-time employees to cover what should be part-time work. The math doesn’t work in your favor.

Then there’s maintenance. Updates, security patches, performance monitoring, compliance tracking. Proprietary platforms handle this. With open source, it’s your problem. compliance and integration maintenance alone add 20-30% to baseline budgets. And 65% of total software costs occur after the original deployment.

When open source makes sense

I’m not saying open source is always wrong. It works in specific scenarios.

High-volume production use cases that can amortize infrastructure costs across millions of requests make sense. If you’re processing enough API calls that token-based pricing from proprietary vendors would exceed your infrastructure costs, open source wins.

Companies with existing ML teams and GPU infrastructure already have the capability gap covered. You’re not starting from zero. The marginal cost of adding another model to your existing setup is reasonable.

Highly regulated industries with data residency requirements sometimes have no choice. When your data legally cannot leave specific geographic boundaries, open source models you can deploy anywhere become necessary rather than optional.

Custom fine-tuning for specialized domains works when you have both the data and the expertise. Generic models won’t cut it, and vendors with domain-specific models might not exist yet for your niche. Though that gap is closing fast - Gartner predicts 40% of enterprise apps will embed AI agents by end of 2026, up from under 5% in 2025.

But here’s what I’ve seen. These scenarios describe maybe 10% of mid-size companies. The other 90% would be better served by proprietary solutions.

Why proprietary wins for most companies

The numbers tell a story most people ignore. McKinsey’s 2025 State of AI report found only 7% of organizations have fully scaled AI across their businesses, and the average enterprise scrapped 46% of AI pilots before they ever reached production. That’s already a disaster.

Adding operational complexity doesn’t fix this problem.

Proprietary platforms give you something crucial: support with accountability. 99.9% uptime SLAs, 24/7 technical assistance, dedicated account managers. When your AI goes down at 2 AM, someone whose job depends on fixing it is on the call.

Open source gives you community forums. Maybe someone had your problem before. Maybe they documented the solution. Maybe that documentation is current. These are all maybes your business can’t afford when AI is in your critical path.

Security and compliance work differently too. Proprietary vendors provide certifications like ISO 27001 and SOC 2 that your compliance team can check off their list. Reputable vendors are increasingly aligning to NIST AI RMF, HIPAA, and ISO/IEC 42001. Open source means your team owns security patching, vulnerability management, and compliance documentation. All of it.

The open source vs proprietary llm choice often comes down to this: do you want to spend your limited technical resources building AI infrastructure, or building AI applications that differentiate your business?

Common decision traps

The biggest mistake I see is underestimating the fractional talent problem. Companies think “we just need 20% of an engineer’s time” and discover that hiring 0.2 of a person doesn’t work. You hire a full person or you don’t get the coverage you need.

The vendor lock-in fear gets overplayed. Yes, proprietary platforms create dependencies. But so does open source. You’re locked into your infrastructure choices, your deployment patterns, your operational processes. OSS stack lock-in is real, just different. And the market is moving toward consolidation anyway. Enterprises are spending more through fewer vendors in 2026, not more.

Technical teams often push for open source because they want to work on interesting infrastructure problems. That’s fine if infrastructure is your business. But if you’re trying to build customer-facing AI features, consider that 76% of enterprise AI use cases in 2025 were deployed via third-party or off-the-shelf solutions rather than custom builds. There’s a reason for that.

The “we can customize it” argument assumes you have both the expertise and the time to do meaningful customization. Most teams don’t. They end up running the model exactly as released, just with more operational overhead.

A practical selection framework

Start with your actual technical capability. Do you have ML engineers on staff? Do you run GPU infrastructure for other workloads? If no to both, proprietary makes more sense.

Calculate your expected API volume honestly. Token-based pricing from vendors is public, and multi-model routing can cut inference costs by up to 85% by directing simple tasks to cheaper models. Compare that to the all-in cost of infrastructure, staffing, and maintenance. Include the cost of downtime.

Look at your compliance requirements. If you need SOC 2, HIPAA, or similar certifications, proprietary vendors have already done that work. 71% of tech teams choose off-the-shelf solutions to accelerate time-to-value, and compliance is a big reason why.

Evaluate your risk tolerance. With only 5-20% of AI pilots resulting in high-impact enterprise deployments, do you want to bet on a complex implementation? Or do you want to reduce variables?

Consider timeline. Proprietary platforms deploy in days. Open source deployments take weeks to months. If you’re trying to prove value quickly, speed matters.

For most 50-500 person companies, the math points to proprietary solutions for initial deployments. You can always move to open source later if volume justifies it. The reverse migration is harder.

Start simple. Get AI working. Prove value. Then optimize costs if needed.

The debate between open source vs proprietary llm platforms matters less than whether your AI actually works. Focus on that first.