The Core Question Every Organisation Faces

As AI becomes central to business strategy, one decision keeps coming up: should we build on open-source AI systems that anyone can inspect and modify — or use closed-source platforms built and maintained by tech giants?

There's no single right answer. But understanding the trade-offs will help you make an informed decision.

Open vs. Closed: The Essential Difference

💡 Analogy: Open source is like getting a full recipe — you can modify every ingredient and share your version. Closed source is like ordering a dish at a restaurant — you get the result but not the method.

The Rise of Open-Source AI

Something significant happened in 2024–2025: open-source AI models caught up — and in some areas surpassed — their proprietary competitors.

Models like Meta's Llama family, Google's Gemma, and China's DeepSeek-R1 demonstrated performance on industry benchmarks that rivalled ChatGPT and Claude — at a fraction of the cost.

🌍 Notable Moment — DeepSeek-R1 (Early 2025): A Chinese developer released DeepSeek-R1, an open-source reasoning model that allegedly matched or outperformed OpenAI's state-of-the-art models on several benchmarks — and claimed it was built for a fraction of the cost. While some claims were later disputed, the release sent shockwaves through the industry and energised the open-source AI debate.

This convergence has made open-source a genuinely viable alternative for organisations — not just a budget option.

Five Factors to Evaluate

1. 🔧 Development, Innovation, and Customisation

Open source:

• Global community of researchers and developers can contribute improvements
• Highly customisable — you can fine-tune the model for your specific industry, language, or task
• Faster innovation cycles driven by collective effort

Closed source:

• Development is managed internally by the vendor — polished and consistent
• Customisation is more limited; you're working within the vendor's framework
• Vendor controls the update schedule — you depend on their roadmap

⚠️ Why This Matters: If your use case is highly specialised (e.g., medical diagnosis, legal document analysis in a specific jurisdiction), open-source models that you can fine-tune on your own data often outperform generic closed-source alternatives.

2. 💰 Cost and Accessibility

The cost comparison is more nuanced than "open source = free."

Real numbers (approximate):

• Meta's Llama: ~$0.60 per million input tokens / ~$0.70 per million output tokens
• OpenAI GPT-4: ~$10 per million input tokens / ~$30 per million output tokens

💡 What This Means: If you're running millions of AI queries per month, open-source can save tens of thousands of dollars in API costs. But factor in the cost of engineering time to set it up and maintain it.

3. 🔐 Security, Trust, and Compliance

Open source:

• Transparent code allows community security audits — vulnerabilities are found and fixed publicly
• When run on your own servers, gives you complete control over where data goes
• No risk of a vendor seeing your sensitive queries

Closed source:

• Vendors maintain their own robust security protocols
• Data passes through vendor servers — important to review their data handling policies
• In regulated sectors (healthcare, finance), vendor certifications (SOC 2, HIPAA compliance) can provide extra assurance

🌍 Example: A hospital processing patient records with AI has two choices:

• Use Ollama (open-source) on local servers → complete data privacy, no external transmission
• Use a closed-source API → relies on vendor's HIPAA compliance guarantees

Both can be compliant; the question is whether you trust the vendor's protocols or prefer to control them yourself.

4. 🛠️ Technical Expertise Required

Open source:

• Requires significant in-house technical expertise or external consultants
• Support comes from community forums and documentation — no help desk
• You're responsible for maintenance, updates, and security patches

Closed source:

• Designed to be "plug and play" — usable without deep technical knowledge
• Vendor provides support, documentation, and handles infrastructure
• Updates are automatic — you always have the latest version

⚠️ Why This Matters: If your organisation doesn't have a strong AI/ML engineering team, the hidden costs of open-source (staff time, debugging, maintenance) can exceed closed-source subscription fees.

5. 🎯 Performance for Your Specific Task

The performance gap between open and closed source has largely closed for common tasks. But nuances remain:

The Smart Answer: Often Both

Many organisations are moving toward a hybrid approach — using different tools for different purposes:

• Closed source for customer-facing applications that need polished, reliable outputs and professional support
• Open source for internal tools, cost-sensitive high-volume tasks, or applications requiring deep customisation

💡 The Strategic Question: Don't ask "open or closed?" Ask "which combination best serves our goals, budget, and risk tolerance?"

Decision Framework

Use this checklist to guide your organisation's choice:

Key Takeaways

• Open-source AI has caught up with closed-source performance — this is no longer a one-sided choice
• Open source wins on cost, customisation, and data privacy — but requires technical expertise to run
• Closed source wins on ease of use, enterprise support, and reliability — but costs more and offers less control
• Cost isn't just licensing — factor in engineering time, infrastructure, and maintenance for open-source
• Most mature organisations will use both — choosing the right tool for each specific use case
• The decision should always align with your organisation's strategic goals, technical capabilities, and risk tolerance