Artificial intelligence is no longer an experiment. It has become the new operating system of business.

While hundreds of new AI tools appear every month, the most powerful and practical one still sits right under your nose: ChatGPT.

Yet in 2025, the question is no longer “Should we use ChatGPT?”

The real question is “How should we use it securely, efficiently, and strategically across our organization?”

Let’s explore the five main ways companies are integrating ChatGPT and GPT-class models today, from simple chat use to fully private deployments.

1. Using ChatGPT via Free or Paid Versions on [chat.openai.com]

You can access ChatGPT directly through the public web platform - either with a free account or a ChatGPT Plus subscription.

This option is the simplest way to start using AI for personal productivity, drafting, brainstorming, or summarization tasks.

However, it’s important to understand the privacy implications: the data you enter may be used to improve OpenAI’s models unless you opt out by disabling chat history in the app’s settings. This version is therefore not recommended for any sensitive, confidential, or regulated data. It’s best for general, non-sensitive work.

Best for: individual productivity, learning, and safe experimentation before your company commits to a larger rollout.

2. Using ChatGPT Enterprise

ChatGPT Enterprise offers the same familiar interface as the public version but adds enterprise-grade privacy, security, and administration controls.

None of the data you enter is used for model training, and everything is encrypted both in transit and at rest. The setup is backed by strong SLAs and Data Processing Agreements (DPA).

You also gain access to higher usage limits, admin dashboards, analytics tools, and integrations such as single sign-on (SSO) and domain verification.

ChatGPT Enterprise is a safe option for use across multiple business functions and departments - including internal communications, customer support, and knowledge management, while maintaining compliance.

Best for: companies ready to scale AI safely across departments with governance and compliance in place.

3. Using the ChatGPT API

For companies that want to integrate ChatGPT into their internal systems, websites, or applications, the OpenAI API is the most flexible choice.

You can embed ChatGPT to automate business workflows or create AI-powered assistants for CRM, HR, or operations. By default, data sent via the API is not used for model training, ensuring that private inputs remain confidential. Pricing is usage-based (per token or character) and completely separate from ChatGPT Plus or Enterprise subscriptions.

This method gives you full control over how AI behaves and how deeply it’s embedded in your processes.

Best for: businesses building custom applications or process automations powered by GPTs.

4. Using ChatGPT via Azure OpenAI Service

Organizations operating under strict data-governance or regulatory frameworks can access OpenAI models through Microsoft’s Azure OpenAI Service.

All data remains within the company’s Azure cloud tenant, which ensures compliance with security and residency requirements. Microsoft acts as the data processor, not OpenAI.

This approach allows highly secure use cases across finance, healthcare, and the public sector, with options for custom deployments, fine-tuning, and integration with other Azure services such as Cognitive Search and AI Foundry.

Best for: regulated industries that prioritize compliance and control above experimentation.

5. Open-Weight or Self-Hosted GPTs – Total Control Without Strings Attached

Finally, for organizations seeking complete control and transparency, there’s a new and rapidly growing option: open-weight or self-hosted GPTs. OpenAI has released models like gpt-oss-20B and gpt-oss-120B under Apache 2.0, letting you host them on-premise, in the cloud platforms such as Together.ai, or at the edge infrastructure.

What that means for you:

• Full transparency and ownership

• Freedom from vendor lock-in

• The ability to fine-tune, retrain, and audit the model internally

• Lower long-term costs at scale

But it is not for everyone.

You will need infrastructure, skilled MLOps people, and patience to maintain it.

Still, for large enterprises or data-sensitive sectors, self-hosting is becoming the most desirable path, combining full control with open innovation.

Best for: organizations with technical resources that want to own their AI stack.

Additional Notes and Refinements

For the free version, users now have the ability to disable chat history, which minimizes, but doesn’t eliminate the risk of their data being used to retrain models. Always treat this version as non-private.

Between API and Azure, both protect data effectively, but Azure OpenAI is preferred for organizations needing full compliance (GDPR, HIPAA, SOC 2, etc.) or geographic data residency.

The Enterprise plan is more than a privacy upgrade, it’s a managed SaaS platform offering admin tools, monitoring dashboards, and analytics.

Fine-Tuning and Customization in 2025

Whether you use OpenAI’s platform, Azure, or open models, you can now fine-tune models such as GPT-4o, GPT-4.1, and GPT-5 on your proprietary data.

But in many cases, you do not need to.

Smart prompt engineering, custom instructions, and RAG setups can achieve similar results faster and cheaper.

As we tell executives in our ChangeAI programs:

“Don’t start by building. Start by teaching. The best AI is not the one you train but the one you guide well.''

Fine-Tuning via Azure OpenAI Service

The Azure OpenAI Service supports fine-tuning for models like gpt-35-turbo, gpt-4o, and gpt-4o-mini.

Using the Azure AI Foundry portal, you can:

1. Prepare training and validation datasets.

2. Use the “Create Custom Model” wizard to train your model.

3. Deploy and use your fine-tuned model within your private Azure environment.

Key considerations:

• Fine-tuning requires specific Azure roles and permissions (e.g., Cognitive Services OpenAI Contributor).

• Not all Azure regions support fine-tuning for every model—check local availability.

• Fine-tuning is ideal for tasks that need specialized knowledge or domain-specific tone (finance, legal, healthcare).

Alternative Customization Methods

If fine-tuning isn’t practical or necessary, several alternative approaches can deliver strong results:

• Prompt engineering: Craft highly specific prompts to guide model output.

• Custom instructions: Define default behavior and response style.

• Embeddings and vector databases: Use retrieval-augmented generation (RAG) to give the model external context from private data sources.

These techniques are often cheaper and easier to maintain than full fine-tuning.

Payment and Pricing Differences: Azure OpenAI Service

When you use ChatGPT through Azure, you pay for usage under your regular Azure billing account.

Costs depend on model type, token volume, and region.

Azure’s pricing model is pay-as-you-go. You pay per 1,000 tokens processed (both prompts and completions). Azure sets its own rates, usually slightly higher than OpenAI’s direct API pricing, because it includes hosting, compliance, and enterprise support overhead.

You’ll see your usage alongside your other Azure services, and billing is done in your local currency.

You pay separately for prompt tokens (input), completion tokens (output), model type (e.g., GPT-4 vs GPT-3.5), and any extra costs from fine-tuning or RAG infrastructure.

Large customers can negotiate Enterprise Agreements (EA) or commitment pricing for volume discounts.

If you fine-tune a model, you’ll incur additional training and inference costs.

Example: Processing 1 Million Words with ChatGPT on Azure (October 2025)

On average, one word equals about 1.5 tokens.

That means 1 million words ≈ 1.5 million tokens.

Approximate Azure OpenAI Service costs (as of October 2025):

• GPT-4o – $0.003 per 1K prompt tokens and $0.009 per 1K completion tokens.

• GPT-3.5 Turbo – $0.0015 per 1K prompt tokens and $0.002 per 1K completion tokens.

Rough total estimates for 1 million words:

• GPT-4o: ≈ $9.00

• GPT-3.5 Turbo: ≈ $2.63

Prices vary slightly by Azure region.

ChatGPT on Private Azure Infrastructure with RAG: Benefits and Trade-offs

Using ChatGPT privately in Azure with Retrieval-Augmented Generation (RAG) provides strong security but comes with several caveats.

1. Vendor Lock-In Still Exists

Even if you host within your own Azure tenant, you’re still tied to OpenAI’s proprietary model APIs.

Any changes to pricing, throttling, or API terms will still apply. Transparency into model reasoning remains limited.

2. Limited Customization

You cannot deeply fine-tune ChatGPT within a standard Azure environment—only apply system prompts or inject external context using RAG.

You can’t retrain the base model to change tone, logic, or compliance behavior.

3. Data Residency ≠ Full Data Control

While your data stays inside Azure, model weights remain a black box.

You can’t audit decision-making or entirely eliminate hallucinations, which can be problematic in regulated sectors.

4. Costs and Compute Can Escalate

Running GPT-4-class models with RAG pipelines is expensive.

Vector databases, embeddings, and storage requirements add significant operational cost at scale.

5. Dependency on the Azure Ecosystem

You’re tied to Azure services - compute, storage, orchestration - which limits flexibility to switch to AWS, GCP, or on-prem setups later.

6. Limited Innovation Access

You don’t get cutting-edge open-source models (like Mistral, Mixtral, LLaMA 3, DeepSeek) that can be fine-tuned freely.

Competitors using open models may gain personalization and cost advantages.

In short:

• Vendor lock-in and limited transparency remain

• Customization is constrained

• Costs rise quickly at scale

• Innovation pace depends entirely on OpenAI’s roadmap

• Azure OpenAI is ideal for secure, compliant deployments,but not for teams seeking deep control or experimentation.

The Hidden Costs of “Private” ChatGPT on Azure

Hosting ChatGPT in Azure with RAG is not the same as full ownership.

Here’s what that really means:

• Vendor Lock-In: You’re still dependent on OpenAI APIs and Azure’s pricing.

• Limited Customization: You can’t fine-tune; only prompt-engineer or provide RAG context.

• Opaque Model Behavior: You can’t audit why hallucinations occur or how answers are generated.

• Data Privacy Limits: Data stays in Azure, but the model internals remain closed-source.

• High Costs at Scale: RAG setups combined with GPT-4o usage can become unpredictable and expensive.

• Ecosystem Lock-In: All tools, pipelines, and integrations rely on Azure services.

• Slower Innovation: You miss out on the rapid advances from open-source AI ecosystems.

The alternative?

Adopting open-source large language models can offer full control over cost, deployment, and innovation speed.

Summary

Artificial intelligence is not replacing people.

It is replacing processes.

The companies that win will not be the ones chasing hype but those that quietly, methodically, and securely make AI part of their everyday operations.

So before you buy a “custom AI solution,” start with what is already in your hands:

• If you use Microsoft 365, you already have Copilot.

• If you use Google Workspace, you already have Gemini.

• And if you want to go deeper, choose the right ChatGPT deployment for your goals.

Because in the end, it is not about what AI can do, it is about what you choose to do with it.