Yrieix Garnier, VP of Products at Datadog, explains how optimising cloud data costs can help eliminate barriers to cloud growth.
UK and European companies continue to unknowingly waste money on cloud resources they don’t need. As the race to adopt emerging technology like AI becomes more prevalent, organisations are losing visibility on the cost of cloud resources needed to fuel the modernisation of IT infrastructure.
In Europe alone, organisations are expected to spend more than US$221 billion on public cloud services in 2025, increasing to US$373 billion by 2028, according to the latest forecast from IDC.
For accurate and reliable use of AI and Machine Learning (ML) models, like the large language learning models (LLM) that underpin Generative AI (Gen AI), organisations need strong data strategies. Data needs to be clean, stored in one location and secure. The best place to do this is the cloud. Hence the continued urgency in the uptake of cloud. For businesses to have a head-start in the AI race, they need to modernise IT infrastructure.
Managing the AI expansion
Regardless, the adoption of AI and ML technologies continues at pace with research indicating that European spending on AI will reach US$144.6 billion by 2028. This is largely driven by investment in Generative AI (Gen AI) with companies developing their own products and services, either from scratch or building on existing LLMs from the likes of Open AI, Google Gemini or Anthropic. It’s estimated that Gen AI will account for nearly a fourth of the total AI market in Europe.
It’s anticipated the financial services sector will take the lead in terms of AI infrastructure spending, closely followed by retail, software and services. The UK Government and the European Union (EU) are also driving initiatives to stimulate the growth of AI and ML, with the EU releasing US$56 million to invest in its own open-source AI model.
Growth is generally positive, however as new technologies like arm-based processors and graphics processing units (GPUS) which enable AI capabilities are expanding the breadth of services offered, cloud environments are becoming increasingly complex.
Dealing with the ‘tech debt’
This makes for a difficult paradox, in which organisations are pulling out all the stops for the cloud, putting pressure on their developers to adopt or integrate cloud technology, while losing sight of the overall budget.
If IT modernisation isn’t approached with a strategic, evidence-based plan, costs can quickly be exacerbated, increasing waste while also inflating an organisation’s ‘tech debt’.
Despite the immense focus on cloud adoption and growth, a recent McKinsey report found that companies with the most tech debt diverted up to 20% of budgets earmarked for new product investment into addressing challenges related to that debt.
This creates a juxtaposition, where businesses race to invest in new technology and instead of basking in the new capabilities they offer, technology teams spend their time trying to cut down exacerbated budgets – halting innovation, growth and wasting resources.
Accounting for spiralling cloud costs
So where are these costs coming from? A recent report on the State of Cloud Spend found 83% of container costs are associated with idle resources. This often comes from the difficulty development teams have in accurately forecasting and right-sizing each new and existing application’s resource requirements, making it difficult to allocate the right amount of resources. In addition, resource needs often change based on the nature and utilisation of workloads.
Another cause for excess cloud spend are data transferring costs between applications. More specifically, the cost of AZ (Availability Zone) traffic – which refers to the traffic between resources in different availability zones often required by applications to achieve scalability.
A total of 98% of organisations are affected by AZ traffic charges. These common charges are also a great opportunity to reduce cloud cost spend, by collocating related resources into a single AZ whenever availability and application requirements allow.
These solutions are only possible when engineers have direct access to cloud cost data. Even better, if engineers have the built-in assistance of cloud cost management software, teams can have visibility on what is pushing out cloud budgets and assess opportunities to optimise.
Analysing cloud costs
Traditionally, business costs are the concern of finance teams, with engineers focused on the performance of applications. But with cloud spend ramping up for most businesses, it is becoming increasingly important for engineers to have visibility across an applications performance, cost and utilisation to help the wider business make more informed decisions.
This gives engineers, and the wider business, the ability to make cost-benefit analyses of an application’s performance and use relative to its cost. Helping organisations make informed decisions on one optimisation versus another.
Secondly, it allows organisations to keep track of how optimisations are impacting overall costs and performance once they’ve been implemented. It offers clear insights into how optimisations are directly impacting cloud costs and performance. The savings from visualising cloud spend, resource utilisation and performance, can be significant. Even Datadog reduced its cloud spend by a total of US$17.5 million annually.
Optimising cloud resources
There’s an old saying about ‘not seeing the wood for the trees’, which means you can’t always see the big picture. That’s either because you’re looking too closely at the smallest details, or in this instance, you lack the visibility to make the right decisions. Cloud cost optimisation gives you the ability to have clear oversight over cloud platforms and their costs. It allows UK and European businesses to stay in the cloud and AI race, able to utilise resources for their intended purpose and maximise productivity while reducing excess spend.
