The real story here isn’t about building a clever algorithm in a lab. As Domino’s CEO Nick Elprin rightly pointed out in a statement covered by Financial IT, “AI experimentation is easy, but delivering ROI at scale remains a challenge.” The challenge lies in the unglamorous, behind-the-scenes work of operationalising AI. It’s about taming the beast after it’s been built. This is the world of AI platform management, and it’s where the battle for AI profitability will be won or lost. It’s about robust model monitoring, sensible governance layers, and something as seemingly dull but utterly critical as cost allocation.
So, What Exactly is AI Platform Management?
Let’s be clear: AI platform management is the grown-up conversation about AI. It’s what happens after the data scientists have declared victory, popped the champagne, and moved on to the next exciting project. It’s the set of practices, tools, and strategies that ensure an AI application doesn’t just work on a laptop, but works reliably, securely, and cost-effectively in the real world, for years to come.
Think of it this way. Building an AI model is like designing a prototype for a revolutionary new car engine. It’s brilliant, innovative, and shows incredible promise. But AI platform management is about building the entire factory. It’s figuring out the supply chain for parts, the assembly line, the quality control checks, the safety regulations, the fuel efficiency standards, and how to track the running cost of every single vehicle that rolls off the line. Without the factory, that amazing engine is just an expensive piece of art.
The core components of this “AI factory” are what separate the successful from the frustrated. It involves a clear strategy for:
– Cost Management: Understanding precisely how much each AI model costs to run.
– Governance: Establishing rules of the road to ensure models are secure, compliant, and fair.
– Operations: Deploying, updating, and monitoring models without constant manual intervention.
– Scalability: Ensuring the infrastructure can handle demand without collapsing or costing a fortune.
The Tangled Web of Multi-Cloud Complexity
To make matters even more entertaining, most large organisations no longer live in a single, neat digital home. They are sprawling across multiple cloud providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. This strategy, known as multi-cloud, is sensible. It avoids being locked into one vendor’s ecosystem, offers flexibility, and can improve resilience. What company wants its entire operation to grind to a halt because one provider has a bad day?
However, this freedom comes at a price: multi-cloud complexity. It’s one thing to manage an application within a single, coherent environment. It’s quite another to manage it when your data lives in one cloud, your model is trained in a second, and your application is deployed on a third. This distributed setup creates coordination nightmares.
For AI, this is a particularly acute headache. How do you ensure consistent security policies across different cloud environments? How do you move gigantic datasets between them efficiently? And, crucially, how do you maintain a single, coherent view of everything? This logistical nightmare is a huge barrier to scaling AI effectively. It’s like trying to cook a gourmet meal with ingredients scattered across three different supermarkets, each with its own layout and payment system. You spend more time travelling and translating than you do cooking.
Getting a Grip on Costs
If you ask a Chief Financial Officer what keeps them up at night about AI, they probably won’t say “sentient robots.” They’ll say, “I have no idea what we’re spending, or if we’re getting any value from it.” The dynamic, on-demand nature of cloud computing that makes AI experimentation so accessible is also what makes its costs so difficult to pin down.
The Black Hole of AI Spending
Effective cost allocation is non-negotiable. Without it, your AI budget becomes a black hole. You know money is going in, but you have no visibility into where it’s going or what it’s achieving. Is the marketing department’s new recommendation engine a roaring success or a colossal waste of money? Is the R&D team’s experimental model quietly racking up a five-figure cloud bill every month? Without granular cost allocation, you can’t answer these basic questions. You can’t separate the valuable projects from the vanity projects.
Smart Strategies for AI Cost Control
This is where IT teams need to get smarter. Simply throwing money at cloud providers is not a strategy. Recent innovations, like those announced by Domino Data Lab, are pointing the way forward. They are focusing squarely on taming this infrastructure beast. The ability to use Autoscaling compute resources means you only pay for what you use, automatically spinning up servers when demand is high and shutting them down when it’s not.
Even more powerfully, as highlighted by a report in Financial IT, platforms are now integrating support for Spot Instances. These are spare compute resources that cloud providers sell at a massive discount—often up to 60-90% off the standard price. The catch is that the provider can reclaim them with very little notice. For many traditional workloads, this is a non-starter. But for many AI training tasks, which can be paused and resumed, it’s a game-changer. Domino claims its clients can reduce infrastructure costs by as much as 60% by intelligently using these resources. This isn’t just about saving money; it’s about making far more ambitious AI initiatives economically viable.
We Need to Talk About Governance
The “move fast and break things” ethos of the early web just won’t fly in the age of enterprise AI. When your AI models are involved in approving loans, setting insurance premiums, or contributing to medical diagnoses, “oops” is not an acceptable outcome. This is where governance layers come into play.
From the Wild West to a Regulated City
Think of governance layers as the laws, building codes, and police force for your AI ecosystem. They provide the structure and guardrails that allow data scientists to innovate freely but safely. These layers dictate:
* Who can access which data and models.
* What kind of data can be used for training to avoid bias.
* How models are tested and validated before being deployed.
* Where models can run to comply with data sovereignty laws.
Implementing effective governance isn’t just about ticking boxes for the regulators. It’s about building trust. Trust from your customers that you’re using their data responsibly. Trust from your employees that the tools they’re using are fair and reliable. And trust from your board that you aren’t exposing the company to unnecessary legal or reputational risk. A single biased model that makes headlines for the wrong reasons can undo years of brand-building.
Are Your Models Working, or Just Running?
Finally, let’s talk about the most overlooked aspect of the AI lifecycle: what happens after a model is deployed. A common misconception is that an AI model, once live, is a finished product. Nothing could be further from the truth.
The Silent Killer: Model Drift
An AI model is a snapshot of the world at the time it was trained. But the world changes. Customer behaviour shifts, economic conditions fluctuate, new slang emerges online. This phenomenon, known as “model drift,” causes a model’s performance to degrade over time. It starts making less accurate predictions, not because it’s “broken,” but because the reality it was trained on no longer exists.
This is why continuous model monitoring is absolutely critical. It’s the check-engine light for your AI. Model monitoring tools track a model’s predictions in real-time, comparing them against actual outcomes and flagging any drop in performance. Without it, your prized AI asset could be silently getting dumber every single day, making poor decisions that cost your business money or damage your brand. It’s the difference between an asset and a ticking time bomb.
Effective model monitoring gives you the insight to know when a model needs to be retrained with fresh data or even completely redesigned. Integrated solutions that can operate across a multi-cloud complexity are essential here, providing a single dashboard to watch over your entire fleet of models, regardless of where they are running.
The Industrialisation of AI
The era of AI experimentation is drawing to a close. We are now entering the era of AI industrialisation. The competitive advantage will no longer go to the company that can build the cleverest model, but to the one that can deploy and manage hundreds of models efficiently, responsibly, and profitably.
The future of enterprise AI hinges on getting the “boring” stuff right. It requires a strategic approach to AI platform management that embraces the realities of multi-cloud complexity, enforces disciplined cost allocation, builds robust governance layers, and relies on relentless model monitoring. The tools and strategies to achieve this are now maturing, as evidenced by the direction of platforms like Domino Cloud. The real question for IT and business leaders is no longer if AI is a strategic priority, but whether they have the operational backbone to actually deliver on its promise.
So, what’s the state of your organisation’s AI factory? Is it a well-oiled machine, or is it still a collection of expensive prototypes?
