What is your view on open-source vs enterprise AI tools?

Neither is universally better. The right choice depends on your security requirements, operational maturity, and data sovereignty constraints.

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Open-source models (Llama, Mistral, Hugging Face) let you run everything locally, fine-tune on your data without restrictions, and avoid per-use licensing costs. They're valuable when data sovereignty is a hard requirement or when you want full independence from cloud providers. The trade-off: you need infrastructure (GPUs, servers) and expertise to deploy, maintain, and troubleshoot production models.

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Enterprise solutions (Azure OpenAI, Copilot Studio, AWS Bedrock) are managed services. The provider handles infrastructure, security, updates, and scaling. You pay per use and get reliability plus support. The trade-off: less control over the model, some vendor dependency, and data leaving your organisation (unless using private Azure deployments).

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Roborana's recommendation: choose based on constraints, not ideology. If your data must stay on-premise and you have ML operations capability, open-source works well. If you want strong model performance with minimal operational overhead, enterprise services deliver better results with less effort. Hybrid approaches (cloud for general tasks, local models for sensitive data) are increasingly common.