Open-Source vs. Closed-Source AI Models
Learn the difference between open versus closed and why it matters to your business.
FIRST, LET’S BE CLEAR: We’re not talking about chatting with ChatGPT.
This is about building software that uses AI as its brain.
Real-World Example:
Imagine you run an e-commerce company and want to build a unique customer service program where AI:
Reads incoming customer emails
Checks your order database
Writes personalized responses
Escalates complex issues to humans
Works 24/7 handling thousands of conversations
This isn’t you chatting with ChatGPT. This is you building a software application that uses an AI model as its engine (brain).
Other Examples of AI-Powered Software:
A real estate app that analyzes property listings and writes personalized investment recommendations
A medical practice’s internal tool that reads patient notes and suggests billing codes
A law firm’s document analyzer that reviews contracts and flags risky clauses
A marketing agency’s content generator that creates social media posts in each client’s brand voice
When you build these applications, you face a fundamental choice: Open-source or closed-source AI models? Let’s define both below along with the pros and cons.
Closed-Source LLMs (The Usual Way)
You connect your software to a company’s AI service (like ChatGPT, Claude, or Gemini) through something called an API (think of it like a phone line to their AI). Every time your software needs the AI to think, it calls the company’s server, they process it, and send back the answer. You pay per use.
Analogy: Renting a car. You pay each time you use it, it’s maintained by someone else, but you never own it and the rental company can change prices or stop renting to you anytime.
Examples: ChatGPT API, Claude API, Gemini API
Real Scenario: Your customer service app sends an email to OpenAI’s servers → ChatGPT reads it → ChatGPT writes a response → sends it back to your app → you pay $0.03 → your app sends the response to the customer.
Open-Source LLMs (The Own-It-Yourself Way)
You download the entire AI model (often 10-50 gigabytes) onto your own servers. Your software talks directly to the AI running on your hardware. Nobody else is involved. Zero ongoing fees per use (just electricity and server costs).
Analogy: Buying a car. High upfront cost, you maintain it yourself, but you own it completely and nobody can take it away or charge you per mile.
Examples: Meta’s Llama, Mistral, DeepSeek, Google’s Gemma
Real Scenario: Customer email comes in → your server’s AI (running locally) reads it → generates response → your app sends it to customer → you pay nothing per transaction (just your monthly server costs).
WHY THIS CHOICE MATTERS FOR YOUR BUSINESS
If you’re building AI-powered software, this decision affects:
How much it costs - $0.03 per API call vs. fixed monthly server costs
Where your data goes - Through their servers vs. stays on yours
What happens if they change prices - You’re stuck vs. you’re protected
How fast it runs - Internet round-trip vs. instant local processing
Whether you can customize it - Limited vs. complete control
Bottom Line: For a customer service tool handling 10,000 emails/day, closed-source might cost $108,000/year. Open-source might cost $15,000/year after initial setup. But open-source requires technical expertise closed-source doesn’t.
CLOSED SOURCE: PROS & CONS
The Upside
Easy to Start Building: Get an API key, write 10 lines of code, your app is talking to ChatGPT. Takes an afternoon, not months.
Example: A developer can build a basic customer service bot in a weekend using Claude’s API.
It Just Works: No servers to configure, no AI models to download (50GB files), no GPUs to buy ($30K each). Your app makes requests, gets responses, done.
Better Performance: GPT-5 and Claude Sonnet 4.5 still give smarter answers than open-source models for complex tasks.
Professional Support: When your customer service bot breaks at 2 AM and customers are waiting, you can call someone. Service-level agreements matter.
Safety Built-In: Recent study showed ChatGPT refused 82% of harmful requests. Open-source? Only 63%. For customer-facing tools, this matters.
The Downside
Cost Adds Up Fast: That customer service bot handling 10,000 emails daily at $0.03 each = $300/day = $109,500/year. Forever.
Your Data Leaves Your Building: Every customer email goes to OpenAI’s servers for processing. Many companies can’t accept this legally or ethically.
You’re Hostage to Their Pricing: OpenAI raised API prices 40% in 2024. Your costs went up 40%. No choice.
Dependency Risk: When OpenAI’s servers went down in November 2025, thousands of businesses’ software stopped working completely. Your app becomes their app.
Limited Customization: Can't modify the core AI architecture. You CAN use RAG (feeding it your company data) with closed-source APIs just fine, but you can't deeply fine-tune the model to fundamentally change how it thinks and behaves.
OPEN SOURCE: PROS & CONS
The Upside
Massive Cost Savings at Scale: After buying servers ($50K-100K upfront), that same customer service bot costs you $15K/year vs. $109K/year. Savings: $94K annually.
Complete Privacy: Customer emails never leave your building. The AI runs on your server in your office. Critical for healthcare, finance, legal.
Example: A medical practice analyzing patient records can’t send that data to OpenAI legally. They must use local AI.
True Ownership: You downloaded it. You own it. Meta can’t shut down your Llama installation, can’t raise prices, can’t discontinue it. You’re independent.
Deep Customization: Train the AI on your company’s data, terminology, style. A law firm can make it an expert in their specific practice area.
No Internet Needed: Your AI works even if your internet goes down. Responses are instant (no round-trip to someone’s server).
The Downside
Expensive to Start: High-end GPUs cost $30K-40K each. You might need 2-4. Plus server costs. $50K-100K upfront investment before you write a line of code.
Requires Real Expertise: You need developers who understand machine learning. Can’t just hire a regular web developer. Talent is expensive and scarce.
You’re the IT Department: When something breaks at 2 AM, you fix it. No phone number to call. No guaranteed help.
Safety Is Your Problem: Recent study found alarming weaknesses:
44% gave harmful responses (synagogue addresses + gun stores)
68% explained how to acquire illegal firearms
14% provided Holocaust denial content
Why? Once downloaded, anyone can remove safety filters. You’re responsible for preventing misuse.
Performance Gap: Still behind closed-source for cutting-edge tasks. Getting better, but GPT-5 is still smarter.
2025 REALITY CHECK
The Gap Is Narrowing: Chinese lab DeepSeek released innovative open models in early 2025, prompting OpenAI’s Sam Altman to admit “we have been on the wrong side of history here.” OpenAI released its first open-weight models in 5+ years (August 2025).
But Enterprises Stick with Closed: Despite momentum, most businesses use closed-source in production due to risk aversion, convenience, support, and safety requirements.
Mistral’s Bet: French startup Mistral AI raised $1.4B with high-quality open-source models, proving “open” can be a viable business strategy.
DECISION GUIDE: Which Should You Use to Build Your AI-Powered Software?
Choose Closed Source If Your Project Needs:
✅ Fast development (launch in weeks, not months)
✅ Customer-facing features (safety matters)
✅ No ML engineers on staff
✅ Guaranteed uptime and support
✅ Best possible AI performance
✅ Low upfront investment
Example Projects: Customer service chatbot, automated email responder, content generation tool for clients, AI-powered search for your website
Choose Open Source If Your Project Has:
✅ ML engineers who can set it up
✅ Strict privacy requirements (healthcare, finance, legal)
✅ High volume (100K+ requests/day where costs matter)
✅ $50K+ budget for hardware
✅ Need for deep customization
✅ Internal use only (lower safety stakes)
Example Projects: Medical record analyzer (HIPAA), internal document search for 10,000 employees, financial analysis tool on proprietary data, manufacturing quality control system
The Realistic Path for Most Businesses Building AI Software
Year 1: Build your customer service bot using ChatGPT API. Launch fast, learn what customers need. Cost: ~$50K-100K.
Year 2: Measure your costs. If you’re spending $200K+/year on API fees and handling sensitive data, consider open-source.
Year 3: Hire ML engineers, buy servers, migrate to self-hosted Llama. New cost: $50K/year. Savings: $150K/year.
Don’t overthink it. Start with closed-source when possible to validate your idea. Switch to open-source later if the economics make sense.
THE BOTTOM LINE
Open vs. closed isn’t about which is “better”—it’s about which fits your needs.
The Reality:
Most businesses use closed-source (and that’s fine)
Some absolutely should use open-source (privacy + scale = big savings)
Both will coexist—competition drives innovation
Two Competing Perspectives:
ACLU (Freedom First): Open-source prevents centralized control, enables auditing for bias/censorship, and protects against authoritarianism. A few companies shouldn’t control humanity’s most powerful technology.
ADL (Safety First): Open-source models lack enforceable safety measures. The ease of removing guardrails makes them dangerous in the wrong hands.
The Tension: How do we balance freedom and innovation with safety and responsibility? There’s no easy answer.
Your Action Plan:
Start somewhere - Pick a tool and begin building (or reply to this email and let my team build it for you)
Match tool to task - Closed for customer-facing, open for internal/privacy-sensitive
Measure everything - Track costs, performance, risks
Stay flexible - Build systems that could switch if needed
Keep learning - The landscape changes monthly
The AI revolution is happening now. The question isn’t whether to use AI, it’s how to use it wisely for your specific business. Open or closed, the best strategy moves you forward today while keeping options open for tomorrow.
Coming Next: Mastering prompting, your AI magic wand! (I’m on holiday this weekend so expect these AI secrets on Tuesday).
Thanks for reading. Have a tool or news story we should cover or need help implementing AI at your business? Just reply to this email…