Specialised GPU providers are emerging as challengers to cloud giants in AI deployment, offering tailored performance and cost advantages. Mark Venables explores whether these alternatives can truly rival the scalability, integration, and support of traditional cloud ecosystems, or if a hybrid approach is the smarter path forward.
Artificial intelligence (AI) is reshaping industries at an unprecedented pace, but at its core, AI remains a problem of computation. As enterprises scale their AI ambitions, they require vast processing power to train, fine-tune, and deploy models. Traditionally, hyperscale cloud providers such as Google Cloud, AWS, and Microsoft Azure have dominated this landscape, offering robust ecosystems that integrate compute, storage, and AI services. However, a growing wave of specialised GPU providers is challenging the status quo, promising tailored hardware, cost efficiency, and optimised infrastructure for AI workloads.
Whether these emerging players offer a viable alternative for enterprises seeking flexibility and performance or whether the benefits of established cloud ecosystems outweigh the potential advantages of specialisation. So, what are the key considerations for businesses navigating this evolving landscape?
Weighing risk and integration
Choosing between traditional cloud providers and specialised GPU firms is, at its core, an exercise in risk management. “With established providers like Google Cloud, you are getting the entire ecosystem, with integrations and resources that come from years of development and support,” Cassiano Surek, CTO at Beyond, says. “It is like deciding where to service a high-end car: you can go to the dealer, where everything’s guaranteed to align or choose a smaller garage with fewer resources but possibly lower costs. Emerging GPU providers have definite advantages but do not always come with the same extensive integrations and support.”
Infrastructure decisions should be based on more than just raw compute power. Selecting the correct type of CPU or GPU is critical, as well as considering memory needs. “Every AI workload is unique; some require more memory, others more parallel processing, or multiple GPUs working in tandem,” Surek adds. “When you look at established providers like Google Cloud, they offer a wide range of GPUs, including TPUs optimised specifically for AI tasks. For instance, fine-tuning a BERT model requires 16 GB of memory, but for much larger models like GPT-3, you’re looking at hundreds of gigabytes across numerous GPUs.”
Beyond hardware specifications, integration matters. Preconfigured AI environments also play a significant role. When tools like TensorFlow are already set up, the team can get to work faster. Don’t underestimate the importance of auto-scaling,” Surek continues. “AI workloads fluctuate significantly; scaling up or down automatically is a big advantage with traditional providers.”
Balancing performance with operational complexity
The choice between traditional cloud and GPU providers depends on the specific AI task. Training a model and running inference require different infrastructure. When training, you need more memory, processing power, and parallel capabilities. Inference is about delivering results quickly and efficiently, keeping latency as low as possible.
Traditional providers often have the upper hand in scalability for large-scale AI tasks. “Traditional providers like Google Cloud have a clear advantage in scaling up to meet the demands of large-scale models,” Surek explains. “The ability to scale to vast processing power is essential for substantial tasks, such as running a full-scale NLP model. While smaller providers can be beneficial for specific tasks, the established clouds can scale models to sizes that smaller providers can’t match. It’s especially true when running these larger models across multiple geographies.”
Yet, cost efficiency is not always as straightforward as it seems; cost comparisons are tricky. When people compare costs, they often focus on GPU usage alone. However, the total cost includes data storage, integration, and transfer expenses. GPU providers might seem cheaper at first glance, but operational complexity can quickly erode those savings. Integrating multiple vendors can take time, and while it may seem cheaper on paper, managing several providers may increase long-term expenses.
Beyond direct costs, performance variability is another consideration. “Not all GPUs perform the same under different AI workloads,” Surek adds. “Some are optimised for deep learning, others for rendering, and some for general-purpose computing. Choosing a GPU provider means understanding how their hardware aligns with your AI tasks. The latest GPUs from Nvidia or AMD may offer incredible performance, but only if they are efficiently utilised within your model architecture.”
Security, compliance, and data governance
For industries operating under strict regulatory frameworks, cloud provider choice is as much about compliance as performance. Finance and healthcare sectors, for example, tend to be cautious with cloud due to data security. “In healthcare, you need providers with specific compliance, like HIPAA, which covers data storage and handling regulations,” Surek says. “Retail, however, is a different story. They generally want simpler integration, and traditional cloud providers may be the most straightforward choice for them. Health and finance sectors, though, need to consider additional data protection and compliance layers, and sometimes a hybrid approach is best.”
Traditional cloud providers often have an advantage in governance with robust governance tools. Tools like Google’s AutoML or Vertex AI allow businesses to validate ideas and deploy faster. This can be more challenging with GPU providers that don’t offer the same integrated support.
Data sovereignty is another major factor. Surek explains that data must be stored in compliant jurisdictions, especially for regulated industries like finance or healthcare. “Established cloud platforms generally offer those options, with certified data centres and industry-compliant solutions that make them a reliable choice for sensitive workloads,” he adds.
Managing security within hybrid models also presents challenges. Splitting workloads across providers means maintaining consistent security policies across platforms. That includes identity management, encryption, and secure data transfer between different environments. If security is an enterprise’s primary concern, consolidating under one provider might be the better option.
The case for a hybrid approach
Despite the clear advantages of traditional cloud providers, many enterprises are adopting hybrid models to balance flexibility, cost, and performance. “We live in a world of multi-vendor solutions,” Surek says. “Even if 80 per cent of a workload is on Google Cloud, you’re likely using services from AWS or another provider. The flexibility of a hybrid setup is invaluable, but managing the sheer number of vendors takes time and consideration.”
The thorny issue of vendor lock-in remains a concern for enterprises making long-term investments in AI infrastructure. “Vendor lock-in is often necessary for larger, established companies,” Surek explains. “Mature companies typically have longer cycles, which align better with stable partnerships. Startups, however, might benefit from cloud providers’ startup programs, which offer credits and support to help them scale.”
Ultimately, AI infrastructure decisions come down to risk appetite and business priorities. It goes back to risk tolerance. “If you are comfortable with a higher risk, smaller GPU providers can offer flexibility to try various solutions,” Surek concludes. “But for most, the stability of a traditional provider like Google Cloud is more valuable, especially for long-term projects.”
As AI evolves, enterprises must weigh the trade-offs between cost, scalability, and integration. While specialised, GPU providers offer compelling advantages, the full-service ecosystems of traditional cloud vendors remain a powerful draw for businesses looking to deploy AI at scale. Companies that can successfully navigate a hybrid approach, leveraging the best aspects of traditional cloud and specialised GPU services, may ultimately gain the most significant competitive edge.
