Private AI vs ChatGPT – which is right for your organisation?
In our now AI-powered world, organisations must choose how they deploy generative AI tools carefully. Do you permit use of a public solution like ChatGPT, or build a private GPT, a self-hosted AI solution on your own infrastructure? Naturally there’s pros and cons to each. This post helps you understand the two options clearly, so that you can pick what will work best for your needs.
What do we mean by “Private GPT” and “ChatGPT”?
ChatGPT in this sense refers to cloud-based AI services supplied by third parties (e.g. OpenAI). The data, computing and models are managed off-site, under the provider’s control.
Private GPT or self hosted AI refers to versions of GPT-style models made available or deployed by your organisation on your own infrastructure (or in a private cloud), rather than relying on a third-party service. Sometimes this is also called an on premise AI platform.
Key criteria to compare
To assess which choice is right, organisations typically look at:
- Data security and privacy
- Control and compliance
- Cost and scalability
- Performance and latency
- Customisation and ownership
- Operational complexity
- Level of maintenance
Pros of a Private GPT (Self-Hosted / On-Premise AI Platform)
Data control and privacy
When you host AI models yourself, all sensitive data stays under your control and there is less risk of third-party exposure. This is critical if you’re handling sensitive or regulated data (e.g. healthcare, finance, legal) or covered by strong privacy laws (GDPR, HIPAA, etc.).
Compliance
Self hosted or on premise AI platforms make it easier to meet legal and regulatory requirements. Because you choose where data is stored and how access to it is controlled, you can audit internally.
Long-term cost
While initial investment in computing, infrastructure and maintenance may be higher for on-premise, over time, costs per use can be more stable. Cloud / SaaS models often involve usage fees, per-seat pricing, or increasing pricing models.
Lower latency / performance control
If latency (the time it takes for data to pass from one point on a network to another) or speed is important, hosting close to your users or inside your IT infrastructure can reduce delays. You also control provisioning and hardware.
Customisation and ownership
You can refine, adapt and fine-tune models in-house. You can integrate with internal systems without being constrained by a provider’s offerings or policies. Ownership of models and code gives you flexibility.
Cons of a Private GPT (Self-Hosted / On-Premise AI Platform)
Upfront cost and resource
Buying hardware or setting up infrastructure, plus hiring suitable expertise, is not a small cost. And then going forward, ongoing maintenance, updates, security and scaling are your responsibility.
Operational complexity
You’ll need to manage the stack: servers or GPU infrastructure, model deployment, monitoring, and ensuring security patches. If something goes wrong, you don’t have a large cloud provider to fall back on.
Scaling challenges
If organisation usage spikes dramatically, scaling on premise can be slower or more expensive compared to cloud AI, unless you build in enough flexibility (hybrid setups, elastic infrastructure).
Continuous updates
Cloud services often need, and benefit from, regular updates and improvements to become the latest models. If you have self hosted AI, you’ll need to keep up with advances yourself to avoid falling behind.
Pros of a ChatGPT (Cloud / Third-Party)
Deployment speed
You get started quickly as it’s right there ready to go. No need to buy hardware, set up infrastructure. The service provider handles all model updates, hosting, scaling.
Access to the latest version
You benefit from the provider’s R&D: the latest model architectures, training, domain improvements and performance updates. Your team are always using the latest version.
Less operational overhead
Maintenance, uptime, backups and security are largely managed by the provider so this reduces burden on your in-house teams.
Broad support and integrations
Many cloud-based AI platforms already include connectors, APIs, tooling and support services. This can speed up adoption and integration.
Cons of a ChatGPT (Cloud / Third-Party)
Less control over privacy and compliance
Even with strong security, you depend on the provider’s terms, their policies and how they choose to handle your data. For very sensitive or regulated data, that may be risky.
Variable costs
Per-use, per-token, API fees can add up, especially for large scale or frequent use. Also, pricing or policies may change and if you are a growing business, the bill will grow too.
Provider constraints
You may be bound by model limits, usage policies, availability, SLA, rate limits, etc. If the provider changes their terms, that could affect your usage.
Latency and performance
Depending on location and network, using remote services adds latency. If large datasets must be sent over the network, that adds overheads and possibly a security risk.
What do organisations typically choose, and why?
Many regulated industries such as finance, healthcare and government, prefer private AI solutions or on premise AI platforms to ensure compliance and reduce risk.
Companies with sensitive intellectual property (e.g. R&D, proprietary algorithms or data) often prefer self hosted AI solutions to avoid exposure.
Organisations that expect high volume usage often find cloud-costs escalating, so they see savings over time from hosting.
For smaller teams, or those without in-house AI expertise, ChatGPT or cloud-based AI makes sense as a lower-risk, faster setup.
Key questions your business should ask about AI platforms
To decide which type of AI setup is right, consider:
| Question | What to check |
|---|---|
| How sensitive / regulated is your data? | If data is highly regulated, private AI or on-premise tends to be safer. |
| What are your performance / latency requirements? | For real-time or internal-network use, on-premise AI may perform better. |
| What scale of usage do you expect (volume, frequency)? | High volume may make cloud AI costs steep. |
| Do you have in-house technical capability? | Deploying and maintaining private GPT or self hosted AI needs expertise. |
| How important is flexibility and customisation? | If you need to integrate tightly with internal systems, tune behaviour etc., self-hosting the AI gives is more flexible. |
| What is your budget short term vs long term? | Consider total cost of ownership, not only initial AI setup cost. |
When Private AI aka Self Hosted GPT aka On-Premise AI Platform is the Right Choice!
Here are some real-world scenarios where an on premise AI platform makes more sense:
- A hospital wanting to use AI on patient records and medical imaging text must meet data privacy laws, GDPR, etc.
- A law firm or financial institution handling sensitive client data or proprietary documents.
- A company with intellectual property, R&D data or trade secrets.
- Organisations with large, predictable AI usage where cloud costs (per-use / per-token) become substantial.
- When you need very low latency (e.g. internal tools, real-time decision support) or offline AI capability.
When ChatGPT / Cloud Solutions make more sense
These are cases where the cloud-based ChatGPT-style route might be better:
- A startup or small business wanting to experiment or prototype quickly, without a big upfront investment.
- Teams that need access to the latest features and models plus full vendor support.
- Use cases where data sensitivity is only moderate, or there are no regulatory or compliance impediments.
- Situations where scaling flexibly is more important than having full control.
Taking a hybrid or phased approach to AI implementation
You don’t always have to pick one or the other solution completely. Many organisations use hybrid strategies:
- Start with cloud-based ChatGPT for prototyping, then shift to an on premise AI platform as scale and maturity grow.
- Use private GPT for sensitive workflows, while non-sensitive tasks stay in the cloud.
- Use self-hosted AI internally, and integrate with cloud tools for certain features.
At OptimaGPT, we believe many organisations that care about confidentiality, control, and performance will find that a self-hosted, on premise AI platform gives them not only peace of mind, but often better ROI over time.
If you’re considering the move, we’re happy to help you map your current workflows, assess data sensitivity, estimate costs and plan a migration path. Reach out to us to discuss which option aligns best with your goals.