Why I Prefer Open Source AI

AI agents, automation, LLM integration... every project raises the same question: proprietary or open source model? My default answer is open source. Not out of dogmatism. Depending on the need and the budget, a proprietary model can make sense. But when I have the choice, I lean open source.

You keep control over the model

An open source model that you host is a model whose behavior you control.

With a proprietary provider, you have no guarantee of stability. This is true across the board: OpenAI, Google, Anthropic, Mistral's API... Any of them can modify, replace, or degrade their model without consulting you.

In August 2025, OpenAI replaced GPT-4o with GPT-5 without warning, removing the model from the selector. The backlash was so severe that Sam Altman reversed course within 24 hours. Users found GPT-5 colder, less creative, and less reliable for their existing workflows.

A month later, reports emerged that OpenAI was silently routing requests to cheaper models (like 5-nano, estimated 20 times cheaper than GPT-4o). Up to 80-90% of requests may have been affected, including for paying subscribers. This kind of practice isn't unique to OpenAI: every proprietary provider has the technical ability (and the economic incentive) to do the same.

With open source, you decide which version runs, when you update, and how the model is configured. No Monday morning surprises.

Important note: "open source" for an AI model means the architecture and model weights are public. The training dataset, however, is rarely available. The transparency is real but partial: you can see how the model works, not necessarily what it learned from.

Your data is no longer yours

All proprietary providers retain your prompts and conversations. Their privacy policies look alike: user content can be used to improve models, with an opt-out that's more or less accessible depending on the provider. OpenAI, Google, Anthropic: same logic, same risks.

And this retention isn't harmless. In May 2025, a US federal judge ordered OpenAI to retain all ChatGPT logs and hand over 20 million anonymized conversations as part of the New York Times lawsuit. What exists can be seized.

When you use a proprietary model, your data is exposed to several risks:

Every question your team asks via ChatGPT can end up in the next training cycle. Your internal data, business processes, confidential documents: all of it feeds a model your competitors also use.

If the provider doesn't like how you use their product, they can suspend your account without notice. This happens across all providers, and false positives are documented: developers get cut off for perfectly legitimate usage.

All providers have law enforcement cooperation policies (here's OpenAI's). US courts now treat AI prompts as digital evidence on par with emails.

And providers analyze how you use their products to develop their own features. The most popular GPTs built by OpenAI's users? Their functions end up integrated directly into ChatGPT. Your usage feeds their product roadmap, and this is true across the entire proprietary ecosystem.

With a self-hosted open source model, none of this applies:

• Your data never leaves your infrastructure
• No one retrains a model on your conversations
• No judge can seize logs that don't exist
• GDPR compliance is simpler: data stays in your jurisdiction

Security through open review

There's a saying in cybersecurity: "Given enough eyeballs, all bugs are shallow."

Open source models like Llama (Meta), Mistral, or those published on Hugging Face are reviewed by thousands of security researchers, academics, and developers. Vulnerabilities are spotted and fixed faster than any single provider could manage.

Proprietary systems rely on keeping their methods secret. If that secrecy is breached, the entire security model collapses. Open source assumes everything is visible: security must be genuinely robust, not just hidden.

And when an issue is found, you don't depend on a single provider's priorities to fix it.

No vendor lock-in

Proprietary AI creates dependencies that compound. Prices change (always upward), features disappear, services can be shut down or degraded. You depend on a roadmap you don't control, and migrating to alternatives is costly and slow.

With open source, community-driven development continues even if a company pulls out. Multiple hosting providers compete on the same models. You can switch infrastructure without switching models.

Costs at scale

For small volumes, a proprietary API is often simpler and cheaper. No infrastructure to manage, pay-as-you-go billing.

But costs pile up fast. At 1 million calls per month, the monthly bill for a proprietary model can exceed $2,000. A self-hosted open source model, after the initial hardware investment, costs a fraction of that.

The break-even point often comes within a few months. After that, the gap widens every year.

The trade-offs

Let's be honest: open source takes more effort upfront.

You need infrastructure, know-how to deploy and maintain models, and responsibility for security updates. For small-volume projects or quick prototypes, a proprietary API is sometimes the pragmatic choice.

My approach: I start by evaluating whether open source is viable for the project. Often it is. When the budget, volume, or privacy constraints justify it, it's my first choice. When it's not the right fit, I say so.

Not a religion

Tux the Linux penguin

Open source is a tool that gives you more control when you need it. To understand the sovereignty stakes behind all of this, read Data Sovereignty: What It Means for You. And if you want to run your own models, the Self-Hosting AI guide is a good starting point.