The past few years have seen the incredible rise of cloud-native AI start-ups, many of them born during the pandemic. These companies emerged agile, experimental, and ready to scale. But as their ambitions grow and their AI models become more complex, they face a critical crossroads: how to manage infrastructure sustainably while continuing to innovate at speed.
In the early days, public cloud services were the obvious choice. They offered flexibility, speed, and scalability - everything a fledgling AI company needed to iterate quickly and push new models to market. For pandemic-era start-ups, it was a low-barrier, high-reward option that helped them build MVPs and gain traction fast.
But what worked during the initial growth stages hasn’t always stood the test of time. Today, many of those same start-ups are encountering serious growing pains, especially around infrastructure decisions, cloud costs, and long-term scalability.
Growing Pains: When Cloud Starts to Clog
The most immediate issue? Cost. For many AI start-ups, public cloud bills have ballooned. We've spoken with organisations that have seen their cloud expenses more than double year-on-year. This is unsustainable for any company, let alone one still seeking profitability. These surging costs are prompting a wave of companies to explore alternatives, including private and hybrid cloud infrastructure. For those considering making the jump from public cloud and investing in their own hardware, the return on investment can be achieved within around 18-months.
But the challenge isn’t just cost, it’s infrastructure suitability too.
The Knowledge Gap Holding AI Start-Ups Back
Many AI companies simply don’t have the internal infrastructure expertise to make informed choices. Their teams are made up of data scientists, machine learning engineers, and application developers, not infrastructure architects. That lack of experience often leads to a mismatch between the company’s needs and the infrastructure it chooses.
At Pulsant, we’ve had clients come to us with a pre-selected solution, only to discover, after a deep dive into their workloads, security requirements, and growth plans, that a different approach would better suit them. Without that guidance, they might have committed to an expensive or inflexible infrastructure model, potentially creating blockers later that would be costly and time-consuming to unwind.
Security in a Cloud-Native World
Ironically, some of the start-ups most invested in public cloud are also the ones most anxious about security. Many fear that moving workloads away from the big cloud providers could compromise their intellectual property or increase their exposure to cyber threats.
But in reality, tailored private cloud environments, whether single-tenant or secure multi-tenant configurations, often offer better security, more granular control, and stronger data sovereignty. With purpose-built compliance frameworks and reduced attack surfaces, private or hybrid cloud setups can actually lower security risks.
Innovation and the UK Economy
The implications of these infrastructure challenges go far beyond individual companies. The UK’s AI sector is expected to contribute over £232 billion to the economy by 2030, according to PwC. Many of the breakthroughs that will drive this growth are coming from small, nimble AI start-ups, not just tech giants.
But if these start-ups can’t scale effectively because of cost, complexity, or infrastructure missteps, UK innovation could suffer. That’s why equitable access to digital infrastructure matters. We need scalable, cost-effective options not just for those with deep pockets, but for all promising AI ventures, regardless of size.
Closing the Infrastructure Gap
This is where digital infrastructure providers can make a significant impact. The role is no longer just about renting rack space or offering standardised cloud services. It’s about providing education, strategic guidance, and tailored solutions.
Start-ups don’t just need infrastructure, they also need infrastructure insight. Providers must understand each company's workloads, security needs, latency requirements, and future plans. With that knowledge, they can craft hybrid solutions combining public cloud agility with private cloud security, or leverage edge computing for faster inference and data processing close to the source.
With the right advice, AI start-ups can avoid expensive detours and build infrastructure foundations that scale with them, rather than hold them back.
Infrastructure Expertise as a Growth Catalyst
The digital infrastructure decisions AI start-ups make today will help pave the way for their future success. Without experienced guidance, they risk being caught in cycles of inefficiency, overspend, and limited scalability. But with the right partners who understand their unique needs and growth trajectory, these companies can unlock new levels of performance, security, and resilience.
The future of AI depends not just on algorithms, but on the infrastructure that supports them. We need to encourage start ups to think about their digital foundations at the outset, not as an afterthought, so that scaling is a walk in the park, not an uphill struggle.