In the global race for dominance in the field of artificial intelligence, investments in infrastructure are reaching astronomical levels. Market giants such as Microsoft, Google and AWS are committing billions of dollars to expand AI-optimised data centres. For enterprises, this intense competition translates into unprecedented availability of advanced models and services, often offered in a seemingly simple ‘one-click’ model in the vendor panel.
However, behind this facade of technological convenience lies one of the biggest business challenges of the coming decade: managing costs. As organisations move from the experimental phase (Proof of Concept) to full-scale production deployments, cloud bills are beginning to grow exponentially. In this light, Gartner analysts’ key advice to “leverage existing cloud provider relationships to negotiate advanced AI services” takes on new meaning. It ceases to be merely operational advice and becomes the foundation of strategic financial governance.
The dilemma of an established relationship
For most organisations, the path to AI deployment naturally leads through the existing, mainstream cloud provider. If a company has been building its infrastructure on the Azure platform for years, choosing Microsoft’s AI services seems logical and organisationally the simplest. This convenience, while valuable from an integration and team competence perspective, carries financial risks.
Relying on the path of least resistance can lead to the unreflective acceptance of standard price tags for services that are inherently resource-intensive and costly. Meanwhile, in a strategic approach, legacy, often multi-million dollar cloud spend should not be seen as a burden or a sunk cost. On the contrary, they represent the company’s strongest negotiating asset.
Changing perspective: from transactions to partnerships
The evolution of the dialogue with the supplier becomes crucial. A conversation conducted from the purchasing department level, focused on the question “What AI services do you have on offer?” needs to be replaced by a strategic discussion at management level: “We are planning key investments in AI for our business and see this area as a long-term collaboration. Let’s discuss the commercial framework that will ensure the viability of these initiatives and allow us to succeed together.”
This shift changes the dynamics of the relationship. The organisation ceases to be merely a customer buying a service and becomes a partner offering the provider a share in strategic, long-term development in exchange for risk participation and optimised terms and conditions.
The anatomy of a strategic AI contract
What should such discussions be about? Standard volume discounts are just the beginning. A strategic contract for AI services must address a much broader context.
Firstly, optimised cost models. Special computational resource credit packages should be negotiated, especially for energy-intensive tasks such as model training or mass inference (inference).
Secondly, access to expertise. It can be crucial to guarantee in the contract access to support from the provider’s most experienced cloud architects. Their help in optimising AI workloads, even before full scaling, can save more than any percentage discount.
Thirdly, contractual flexibility. The AI market is extremely dynamic. The source text rightly points to the growing role of specialist players, so-called ‘Neochmur’ (such as CoreWeave) or niche providers. Analysts stress that a hybrid approach will be key. This means that a prime contract with a hyperscaler must not become a ‘golden cage’ that financially penalises an organisation for wanting to use a more efficient or cheaper supplier for one specific task.
Fourthly, cost transparency. A detailed discussion of hidden cost generators is needed, above all data transfer (egress) and storage charges, which can quietly undermine the viability of the entire project.
Financial consequences of omission
It should be clear: the alternative to proactive negotiation is not simply a ‘higher bill’. The alternative is the real risk of project failure. Many promising AI initiatives pass the PoC phase successfully, generating enthusiasm within the organisation. The problem arises when attempting to scale, when the first production invoice arrives on the CFO’s desk, often an order of magnitude higher than the original estimates.
In such a scenario, innovation is halted not because of technological flaws or lack of business value, but solely because of an unsustainable cost model. Thus, effective financial and contract management becomes a competency as critical to the success of AI as MLOps or data science.
Implementing artificial intelligence is a strategic marathon, not a series of short sprints. The relationship with cloud providers must evolve to support this long-term journey. As the market moves towards managing autonomous AI agents, the challenges of governance and security – and therefore cost – will only increase. Hyperscalers are investing billions to win this market. A wise organisation should see them not just as suppliers, but as partners who should be willing to invest in its success by creating an environment that fosters sustainable innovation.
Key findings
1. negotiating power lies in existing relationships: To date, significant cloud spending is the strongest asset in negotiating new AI services.
2. AI contract is more than price: Strategic negotiations should include access to vendor expertise, credits for computing resources and the flexibility to implement a hybrid and multi-cloud strategy.
3 Cost is the main risk of scalability: Many AI projects fail not because of the technology, but by uncontrolled cost increases once they move from the test phase to production.
4 Evolving the supplier relationship: Successful implementation of AI requires a change in the supplier relationship from a transactional one to a strategic partnership, focused on achieving mutual, long-term benefits and ensuring the viability of the innovation.
