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Not every organisation has to choose between building or borrowing. This post explores how hybrid strategies combining pre-built models with custom fine-tuning, private hosting, or on-device optimisation, can deliver the best of both worlds.

Building your own AI model can be expensive, complex, and risky but there are scenarios where it pays off. This post looks at when developing a proprietary model offers genuine return on investment, and what conditions must be in place to justify the effort.

Building your own AI model may seem like a cost-saving move in the long term but the reality is often very different. This post explores the hidden and not-so-hidden costs of developing, training, and maintaining proprietary AI models, from staffing and compute to compliance and failures.

Choosing where and how to run your AI workloads can drastically affect your speed, cost, and scalability. This post breaks down the pros and cons of using hyperscale cloud providers, GPU-specific IaaS vendors like Cudo Compute, or running your own bare metal setup.

Building a model from scratch isn’t just a technical challenge, it’s a full-scale organisational investment. This post explores the depth of resource, skill, and infrastructure required, and why many underestimate what it really takes.

Foundational models like GPT-4 and Claude have already solved 90% of common business problems. This post covers the benefits of fine-tuning or embedding pre-built models, cost-efficiency, and real-world use cases where customisation outperformed full custom builds.