The most common question we hear from businesses exploring AI: "Should we just use ChatGPT, or is there something better?" The answer depends on what you need. Here's an honest comparison.
The Quick Comparison
| Factor | ChatGPT (OpenAI) | LLaMA (Self-Hosted) |
|---|---|---|
| Setup time | Minutes | Days to weeks |
| Data privacy | Data sent to OpenAI | Data stays on your servers |
| Customisation | Limited (system prompts) | Full (fine-tuning, RAG) |
| Cost (small team) | Lower | Higher upfront |
| Cost (heavy usage) | Scales with users/tokens | Fixed infrastructure |
| Offline capability | None | Full airgap support |
| Model quality | Excellent (GPT-4 class) | Very good (closing the gap) |
| Vendor lock-in | High | None |
| Audit trail | Limited | Complete |
When ChatGPT Makes Sense
ChatGPT is the right choice when:
- You need AI quickly with minimal setup
- Your use cases involve non-sensitive, general information
- You have a small team with light usage
- Data privacy isn't a primary concern
- You don't need AI connected to internal systems
For a marketing team drafting social media posts or a developer asking general coding questions, ChatGPT is fast, capable, and cost-effective.
When LLaMA (Self-Hosted) Wins
A self-hosted open-source model like LLaMA is the better choice when:
- Data sensitivity: You handle client data, patient records, financial information, or anything covered by GDPR/FCA/SRA regulations
- Customisation: You need AI that understands your specific domain, terminology, and processes
- Integration: You want AI connected to your internal databases and tools via MCP servers
- Cost at scale: You have 50+ users or heavy API usage where per-token costs add up
- Control: You need full audit trails, role-based access, and the ability to modify the model
- Offline: You need AI in airgapped environments
The Quality Gap Is Closing
Two years ago, GPT-4 was clearly ahead of any open-source alternative. That gap has narrowed dramatically. Modern open-source models perform comparably on most business tasks:
- Document summarisation and analysis
- Question answering over company data
- Email drafting and communication
- Code generation and review
- Data extraction and classification
Where GPT-4 still has an edge is in complex reasoning and creative tasks. But for the bread-and-butter business use cases, a well-configured LLaMA deployment delivers equivalent results.
The Hidden Cost of ChatGPT
ChatGPT's pricing looks simple: £20/user/month for Plus, or pay-per-token for API access. But the hidden costs include:
- Enterprise plans with better security guarantees cost significantly more
- API costs scale unpredictably — a busy month can blow your budget
- No customisation means your team spends time crafting prompts that a fine-tuned model would handle automatically
- Compliance risk — if a data breach occurs through a public AI tool, the cost dwarfs any subscription savings
The cheapest AI is the one that doesn't cause a data breach.
The Hybrid Approach
Many organisations use both. Public AI for general, non-sensitive tasks. Private LLaMA deployment for anything involving company data, client information, or regulated content. This gives you the convenience of ChatGPT where it's safe, and the security of private AI where it matters.
Making the Decision
Ask yourself three questions:
- Does your AI use case involve sensitive or regulated data?
- Do you need AI connected to your internal systems?
- Do you need full control over how AI processes your data?
If you answered yes to any of these, self-hosted LLaMA (or a similar open-source model) is the right foundation. If all three are no, ChatGPT will serve you well.
Talk to us about deploying a private LLM for your organisation.