Proprietary vs Open-Source AI Trap That's Bankrupting Startups

Companies are shutting down because of the AI trap. Today’s newsletter breaks down how I choose AI models. Open-Source vs Proprietary.

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In today’s edition:

• AI Roundup— learning resources, news on RAG, Edge devices, and Gemma 3n

• Dive Deep Drill— How to Choose AI Models? [Open-Source vs Proprietary]

• Build Together— Here’s How I Can Help You

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Dive Deep Drill

How I Choose AI Models? [Open-Source vs Proprietary]

At first glance, it looks like a technical choice.

But in reality, it’s about control, cost, risk, and speed, and how much you’re willing to trade between them.

I’ve made this decision in many roles

• as an engineer fine-tuning models

• as a consultant building for clients

• as a mentor guiding AI leaders

Here’s how I think through it, and what you should know before making the call.

What’s the real trade-off?

At its core, it comes down to this:

• In open-source models, you download the weights, fine-tune or modify them, run them on your infra (or cloud), and have full control

• In proprietary models you send an API request to something like OpenAI, Anthropic, or Google, and they handle everything under the hood

It sounds simple.

But each path carries trade-offs that will shape your product, your team, and your costs over time.

What Happens When You Get It Wrong?

Too many startups and teams don’t think long-term.

They scale up on APIs without a plan, then hit a wall like this:

• builderAI scaled with a fake AI stack, raised $1.5B, then imploded

• startups relying only on GPT APIs saw their infra costs exceed revenue

• autonomous vehicle players like Argo AI burned billions before shutting down

These aren’t just stories.

They’re reminders that the cost structure of AI matters just as much as the technology.

Just strategic miscalculations.

Why I Choose Open-Source [When I Can]

• once deployed, inference costs are much lower, great for high-volume tasks

• I can fine-tune the model, add business-specific logic, or run it entirely offline

• I don’t need to send sensitive data to third-party servers

• I can inspect the model and understand how it behaves

But here’s what people underestimate:

• you need MLOps, GPU access, version control, monitoring, and people who can handle them

• not all open-source models are equal: Some are great, others are unreliable

• you’ll need to test and evaluate

• from setup to optimization, it’s slower than an API call

So if the team isn’t ready, this can become more of a bottleneck than a benefit.

Why I Sometimes Choose Proprietary APIs

• I can test a use case in hours, not weeks

• GPT-4, Claude 3, and Gemini, these models are extremely powerful out of the box

• I don’t worry about scaling, retraining, or maintenance

• great for prototypes and non-sensitive tasks

But I also keep in mind that:

• every call adds up, and usage-based pricing can break your margins

• no fine-tuning (or very limited)

• If pricing changes or APIs go down, you’re stuck

• sensitive data goes through someone else’s servers

What I Recommend [A Hybrid Strategy]

In most real-world projects, I end up doing both.

• start with proprietary models to test ideas and move fast

• shift to open-source when you need control, privacy, or scale

This way, you don’t overcommit too early.

You get speed and long-term rapo.

How I Make the Call [With Clients or My Team]

• what’s the use case? Prototyping or production? Sensitive or generic?

• what’s the budget? Can we afford API costs, or do we need to own the infra?

• what are the privacy and compliance needs?

• what’s our internal technical capacity?

• are we okay with being locked into a vendor?

If you need speed, go proprietary.

If you need control, go open.

If you want both, start with APIs, but design with open-source in mind from day one.

Final thoughts

Choosing between open-source and proprietary models isn’t just a technical call.

It’s a strategic decision tied to your budget, your data policy, your customer needs, and your long-term vision for AI inside your organization.

Choose your model based on your future, not just your present.

This is the kind of thinking I bring into every project, every cohort, and every leadership call.

The best teams aren’t locked into one tool.

They learn fast, adapt, and build what works.

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Paper Unfold

A series in which you will get a breakdown of complex research papers into easy-to-understand pointers. If you missed the previous ones:

• Apple's Bomb on Reasoning Models

• Agents are Not Enough [Paper Unfold]

• Attention Is All You Need [Paper Unfold]

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