In this blog post How to Choose Claude GPT or Open Models in Microsoft Foundry we will explain how mid-market organisations can select the right AI model without turning the decision into a costly technical experiment.
The common mistake is to choose whichever model is leading the latest benchmark. Six months later, the business may discover it is paying premium rates for simple tasks, cannot meet its data requirements, or has built an application that is difficult to move.
Microsoft Foundry is an Azure-based platform for building, testing, deploying and governing AI applications. Instead of dealing separately with every AI provider, your team can access GPT, Claude and a large catalogue of open models through a more consistent Azure environment.
The model is the engine behind the application. Microsoft Foundry provides the workshop around that engine, including security controls, access management, monitoring, evaluation and connections to company data.
CPI Consulting helps organisations design, build and secure Azure AI solutions, from Azure OpenAI and Microsoft Foundry to agents, governance and production deployment.
Visit CPI ConsultingWhy model choice is now a business decision
GPT, Claude and open models can often complete similar tasks. The difference becomes clearer when you consider cost, response speed, privacy, customisation and the effort required to keep the system running.
A customer-service assistant answering routine questions does not need the same model as an AI tool reviewing a 200-page contract. Using one premium model for everything is like sending every delivery by express courier, even when standard post would do.
Our earlier guide to selecting OpenAI, Claude and Meta models in Microsoft Foundry covers the broader selection landscape. Here, we will focus on the practical decision mid-market leaders need to make for real workloads.
Where GPT usually fits
GPT models are often a strong starting point for organisations already invested in Microsoft 365 and Azure. They support a broad mix of business tasks, including writing, analysis, software development, image understanding and applications that need to call other systems.
In plain English, this means GPT can do more than answer questions. It can help an application look up a customer record, summarise the account and prepare a recommended response.
GPT is worth considering when you need:
- A general-purpose model covering many departments and use cases.
- Strong integration with Microsoft services and development tools.
- Support for text, images, audio or other types of information.
- A managed service without maintaining the underlying AI infrastructure.
The risk is paying for more capability than the workload needs. A premium reasoning model may be valuable for complex financial analysis but wasteful for classifying basic support tickets.
Where Claude usually fits
Claude is frequently considered for document-heavy work, detailed analysis, coding and AI agents that complete multi-step tasks. An AI agent is software that can follow instructions, use approved tools and carry out a process rather than producing only a written answer.
For example, a legal or professional-services firm might use Claude to compare contracts, identify unusual clauses and prepare a structured summary for human review.
Claude is worth testing when you need:
- Careful handling of long documents and detailed instructions.
- Structured reports that need a consistent tone and format.
- Complex coding, analysis or multi-step agent workflows.
- An alternative provider to reduce dependence on one model family.
Availability, processing location and commercial terms can differ between models in Foundry. Do not assume every Claude deployment has exactly the same data arrangements as a GPT deployment simply because both appear in the same catalogue.
For a closer comparison of the providers, see ChatGPT versus Claude for business. If Claude is already on your shortlist, our guide to Claude in Microsoft Foundry explores the Azure deployment considerations in more detail.
Where open models make sense
Open models include families from providers such as Meta, Mistral, DeepSeek and Microsoft. Many are more accurately described as open-weight models, meaning the trained model is available to deploy, but its licence and development process may not be fully open.
That distinction matters. Your legal and security teams should review the licence, acceptable-use terms, support arrangements and update process before approving a model.
Open models can make sense when you need:
- Greater control over where and how the model runs.
- Customisation for a specialised industry or repeatable task.
- Lower costs for predictable, high-volume workloads.
- More flexibility to move between hosting environments.
- A smaller model for a narrow task such as classification or data extraction.
However, there is no such thing as a free enterprise model. Depending on the deployment, you may pay for dedicated computing capacity, security testing, monitoring, upgrades and specialist staff rather than a simple per-use fee.
Use five questions instead of a leaderboard
1. What business outcome are we buying?
Define the result before comparing models. Are you reducing call-handling time, preparing proposals faster, finding information in company documents or automating an approval process?
If the outcome cannot be measured, the pilot is likely to become an interesting demonstration rather than a useful business system.
2. How sensitive is the information?
Identify whether the application will process customer records, employee information, contracts, financial data or intellectual property. Then confirm where that information is processed, stored and logged for the specific model and deployment type.
For Australian organisations, this review should support privacy obligations and existing cybersecurity controls. The Essential Eight, the Australian Government’s widely used cybersecurity framework, remains relevant, while organisations should also understand the newer ASD Essentials guidance for enterprise IT, cloud and AI environments.
3. What is the total cost per completed task?
Do not compare only token prices, which are the charges for the small chunks of text a model reads and produces. Include failed answers, repeated requests, integration work, employee review time and the cost of operating the application.
A cheaper model that requires extensive correction may cost more than a premium model that gets the task right the first time.
4. Can our team operate it safely?
A model moving into production needs identity controls, restricted access, monitoring, content safeguards and a plan for updates or retirement. Microsoft Foundry can centralise many of these controls, but they still need to be configured and reviewed.
This is where Microsoft Entra ID, which manages user identities and access, and security platforms such as Microsoft Defender and Wiz can help identify exposed services, excessive permissions and risky configurations.
5. How easily can we change models?
Models improve and retire quickly. Keep business rules, prompts, company knowledge and application logic separate from the chosen model wherever practical.
This reduces the cost of moving from GPT to Claude, introducing an open model, or running multiple models for different tasks.
A practical mid-market scenario
Consider a 180-person professional-services firm processing 20,000 AI requests each month. Most requests involve summarising emails, categorising documents and drafting routine responses, while a smaller number involve detailed contract and risk analysis.
Using the most capable frontier model for all 20,000 requests would be simple, but unnecessarily expensive. A better design could use a smaller GPT or open model for routine work and Claude or a premium GPT model for complex analysis.
Microsoft Foundry’s model router can also direct each request to a suitable model based on factors such as complexity, cost and quality. However, routing should be introduced only after the organisation has tested individual models and established clear quality standards.
The business outcome is not merely a lower AI bill. It is faster processing, controlled risk and the ability to use premium models only where they create premium value.
A sensible 30-day selection process
- Select one business process. Avoid starting with a company-wide AI assistant.
- Collect 50 to 100 representative tasks. Remove personal or sensitive information during early testing.
- Test at least three options. Include GPT, Claude and a suitable smaller or open model.
- Score business measures. Track accuracy, employee review time, response speed, failure rate and cost per completed task.
- Review security and contracts. Confirm data location, access controls, logging, licensing and model retirement arrangements.
- Run a controlled pilot. Give a small group access and require human approval for important decisions.
For a more detailed comparison framework, our AI model scorecard explains how to assess cost, governance, security and business value without relying on headline benchmark results.
The best answer may be more than one model
Most mid-market organisations do not need to bet the entire business on GPT, Claude or open models. They need a controlled portfolio in which each model has an approved purpose, budget and risk level.
CloudProInc approaches these decisions as a Microsoft Partner and Wiz Security Integrator with more than 20 years of enterprise IT experience. From Melbourne, we help organisations across Australia and internationally connect AI choices with Azure architecture, Microsoft 365 security and practical governance.
If you are unsure whether your AI pilot is using the right model, or whether its security and ongoing costs have been properly considered, we are happy to take a practical look with you — no strings attached.