At Accelerate Tech we believe the future of enterprise AI lies in flexibility. Microsoft’s announcements over the past week reinforce that view. They bring OpenAI’s most advanced models into Azure and make open‑weight alternatives accessible. Here’s what matters to your organisation and how we’re already using these capabilities.
GPT‑5 Family Hits Azure AI Foundry
Microsoft has rolled out the GPT‑5 family across Azure AI Foundry. You can now access gpt‑5 for advanced reasoning and code generation, gpt‑5‑mini for low‑latency real‑time agents, gpt‑5‑nano for fast retrieval and Q&A, and gpt‑5‑chat for multimodal conversations – techcommunity.microsoft.com.
A real‑time model router automatically selects the right model for each query. This router can reduce inference costs by up to 60 % while maintaining accuracy – opendatascience.com.
We’ve already built an AI assistant for a QLD Government Agency as part of a proof of concept using gpt‑5 to process technical manuals. The model identifies award related queries that help staff submit time sheets for complex field work scenarios.
Open‑Weight Models for On‑Premises Scenarios
Not every organisation can send data to the cloud. That’s why Microsoft’s release of gpt‑oss models matters. These open‑weight models can be fine‑tuned with your own data and run locally. As of Aug 5, the gpt‑oss‑20B model is available on Windows devices with GPU acceleration – blogs.windows.com. You can install Foundry Local via WinGet and start running the model with a single command, or you can download it through the AI Toolkit for VS Code – blogs.windows.com.
We’ve testing with gpt‑oss‑20B for a healthcare use case that must process patient data on‑site. Running the model locally will allow them to comply with privacy regulations while still benefiting from powerful language understanding. When combined with Azure AI Foundry, this hybrid approach gives them full control over where inference happens.
AI Toolkit v0.18.3 – Experiment and Deploy Faster
The new AI Toolkit release adds full support for the GPT‑5 family and GPT OSS models – techcommunity.microsoft.com. The toolkit’s Model Catalog and Playground let our engineers compare models, generate SDK snippets and integrate them into production workflows – techcommunity.microsoft.com. We use the Playground to evaluate which model best fits a client’s cost‑performance requirements before deployment.
Our Advice
- Identify high‑impact use cases – GPT‑5 enables deeper reasoning. Start with tasks where context and logic matter, such as legal document analysis or code generation.
- Leverage the model router – Use Azure AI Foundry’s router to balance cost and performance across gpt‑5 variantsopendatascience.com.
- Consider open‑weight models for sensitive data – gpt‑oss‑20B can run locally, giving you data controlblogs.windows.com.
If you’d like help evaluating or integrating these models, reach out. We’ve delivered deployments in as little as two weeks using our Accelerate AI framework.
