2026 Cloud Trends Tech Firms Need to Plan for Now

Most technology firms are no longer debating cloud adoption. The question now is whether their cloud environments, teams, and operating models match how the business actually runs today, and whether they are equipped for future needs.  

What separates firms that make progress from those that struggle is how effectively they manage scale, cost, risk, and capability – and that starts with having the right people in place. Organisations that delay their talent planning will see issues emerge, such as skills shortages, architectural constraints, or operational failures. 

This guide breaks down the cloud trends tech firms need to plan for now and what they mean for technology choices, workforce planning, and execution in 2026. 

Multi-cloud becomes the default operating model

Multi-cloud adoption is no longer limited to large enterprises or regulated industries. It increasingly reflects real business conditions, global expansion, acquisitions, and regional requirements. 

Research shows that 92% of organisations have already adopted multi-cloud approaches, but with this model comes risks like unmanaged complexity, duplicated tooling, inconsistent controls and security, and rising overhead. 

Talent implications 

Operating across multiple cloud providers increases the need for professionals who can think in systems rather than tools. Teams require leaders who understand how decisions in one environment affect performance, security, and cost elsewhere. Single-cloud depth alone is no longer sufficient for senior roles. 

What to plan for now

Treat multi-cloud as a long-term operating reality. Standardise architectural principles, align governance and identity models, and hire leaders with experience running environments across providers, not just deploying them. 

Hiring priorities 

• Cloud and enterprise architects with experience across multiple providers 
• Senior engineers are comfortable operating in mixed environments 
• Platform leaders who have managed multi-cloud environments at scale 

Cloud costs are rising, and engineering must own optimisation 

Cloud spending in 2026 faces pressure from multiple directions. For example, AWS recently raised GPU instance prices by 15% for capacity blocks, with some regions seeing increases up to 20%. GPU costs for AI and ML workloads now range from $10,000 to $30,000 per GPU. 

Cloud cost is now also driven far more by technical decisions than by contracts or budgets. Architecture design, automation choices, and workload placement determine whether cloud spend stays under control. Organisations that treat cost as a finance problem rely on reports that arrive after the spend has already happened. 

Talent implications 

Engineering and platform leaders are increasingly expected to understand the financial impact of their decisions. Cost awareness is becoming part of senior technical accountability. FinOps adoption has matured significantly, with 63% of organisations now tracking AI spend, up from 31% in 2024.  

What to plan for now

Give engineering leaders ownership of cost alongside performance and reliability. Introduce FinOps capability, embed cost visibility into design decisions, and align incentives to sustainable usage. Rising GPU and infrastructure costs make this more critical than ever. 

Hiring priorities 

• Platform and cloud engineers with FinOps exposure 
• Architects who understand cost-performance trade-offs 
• Engineering leaders accountable for cost as well as delivery 

Platform engineering becomes a core capability – with AI as a dual mandate 

As cloud estates grow, ad hoc infrastructure support models will struggle to keep up. Gartner predicts that in 2026, 80% of large software engineering organisations will establish platform engineering teams, up from 45% in 2022. 

Platform engineering in 2026 carries a dual mandate: building AI-powered platforms and building platforms that enable AI workloads. Teams must provide self-service infrastructure while also supporting data-intensive, GPU-dependent applications. Well-designed platform teams remove friction for product teams, while poorly defined ones slow delivery. 

Talent implications 

Platform engineering introduces a different talent profile, blending cloud engineering, automation, and internal product thinking. Teams now also need to understand how AI workloads differ from traditional applications – compute intensity, data gravity, and cost sensitivity all become more complex. 

What to plan for now

Define the role of your platform team by deciding what they own, how teams interact with them, and how success is measured. This includes defining how the platform supports both traditional workloads and AI initiatives. Hire for product mindset, automation capability, and internal stakeholder influence. 

Hiring priorities 

• Platform engineers with strong automation skills, cloud fundamentals, and AI workloads 
• Technical leads experienced in building internal platforms 
• Leaders who can balance enablement with governance while navigating AI requirements 

Cloud security and identity become design decisions 

In multi-cloud environments, security is no longer something added at the end. Identity, access control, and policy enforcement now sit at the centre of cloud design. 

Firms that separate security from architecture can experience delays, rework, and conflict as environments scale, so cloud and platform leadership should be involved with security decisions early on. 

Talent implications 

Senior cloud professionals must understand identity and access models themselves, not defer them entirely to specialist teams. Security expertise needs to be closely integrated with platform and architecture roles.  

What to plan for now

Embed security capability earlier in platform and architecture design. Clarify shared accountability between security, platform, and engineering leadership to reduce friction and improve resilience. 

Hiring priorities 

• Cloud security architects with multi-cloud experience 
• Platform engineers with strong IAM and policy knowledge 
• Technical leaders comfortable owning shared security risk 

AI and data workloads change how cloud infrastructure is designed 

Data-heavy and AI-driven workloads place very different demands on cloud infrastructure than traditional applications. Compute intensity, data movement, latency, and cost all become more sensitive. Glocomms’ latest Technology Talent Report revealed that 53% of tech professionals received AI skills training in the past year to factor in changes like these, and 27% received a pay increase as a result. 

Firms that have not planned for where data lives and how it moves often find their AI initiatives limited by infrastructure decisions made earlier. The current GPU shortage and price volatility make architectural decisions around AI workloads more consequential. 

Talent implications 

Organisations need professionals who understand how data location affects architecture and cost. General cloud experience without exposure to data-intensive workloads leaves gaps. Teams must also navigate GPU procurement challenges and understand when to build versus rent capacity.  

What to plan for now

Architecture decisions should factor in data gravity, cross-cloud integration, GPU availability, and long-term cost implications. This has direct consequences for both platform design and hiring. 

Hiring priorities 

• Cloud architects with experience supporting data and AI workloads  
• Engineers familiar with cross-cloud data integration and GPU optimisation  
• Platform leaders who understand data-driven infrastructure trade-offs and AI cost dynamics  

Sustainability and GreenOps emerge as governance requirements 

Cloud sustainability is moving from voluntary initiative to an operational requirement. Data center power demand is projected to increase significantly, and organisations increasingly face pressure from regulatory bodies and stakeholders to track and report on cloud carbon emissions. 

While sustainability remains a lower priority than cost optimisation for most teams, forward-thinking organisations are building carbon tracking into their cloud governance frameworks now, before regulatory requirements make it mandatory. 

Talent implications 

Cloud architects and platform leaders need to understand the carbon impact of architectural decisions. This includes data center location, instance type selection, and workload scheduling. Teams that develop this capability early will be better positioned as requirements tighten. 

What to plan for now 

Organisations should establish baseline carbon metrics for cloud workloads and factor sustainability into architectural standards. This work integrates with existing FinOps and governance practices. 

Hiring priorities 

• Cloud architects who understand sustainability considerations 
• Platform engineers who can implement carbon-aware workload scheduling 
• Leaders who can balance cost, performance, and environmental impact 

Talent strategy becomes a limiting factor for cloud execution 

One of the most common blockers to cloud progress in 2026 is talent availability, as the expansion into AI workloads, rising cost pressures, and maturing platform engineering practices all increase skill requirements. 

Demand for experienced cloud architects, platform engineers, and security leaders is outpacing supply, making hiring harder, but organisations that rely on a small number of key individuals will expose themselves to delivery risk as environments grow more complex.  

Talent implications 

Execution slows when roles are poorly defined, and succession planning is weak. Teams become fragile as dependency on individuals increases, and the addition of AI competencies to existing cloud roles compounds this challenge. 

What to plan for now

Firms should assess whether their current teams can realistically support the complexity of their cloud environments. This often means redesigning roles, strengthening leadership layers, and hiring ahead of demand to avoid reactive recruitment later. 

Hiring priorities 

• Senior cloud leaders with experience operating at scale 
• Platform and architecture roles that reduce single points of failure 
• Specialists aligned to future operating models, not legacy structures 
• Professionals who bridge traditional cloud and AI infrastructure expertise 

Cloud workforce planning checklist for 2026 

Use this checklist to assess whether your cloud workforce strategy is aligned with where your business is heading: 

• Do we have leadership experience running multi-cloud environments in production?  
• Are platform engineering responsibilities clearly defined and properly resourced?  
• Can our platforms support both traditional workloads and AI initiatives?  
• Is cloud cost ownership embedded within engineering and architecture roles?  
• Do we have visibility into GPU costs and AI infrastructure spending?  
• Are security and identity considered part of cloud design, not an afterthought?  
• Can our teams support data and AI workloads across platforms?  
• Have we established baseline sustainability metrics for cloud operations?  
• Do current role designs reflect how work actually gets done? 

Gaps here often indicate future delivery and hiring risk. 

Planning cloud capability for 2026? 

In 2026, cloud capability depends as much on people and structure as it does on technology. Firms that plan their headcount and skills gaps early are better positioned to manage complexity, control cost, and scale with confidence. 

Glocomms works with technology leaders who are reassessing their cloud teams in light of these developments by helping to define roles, benchmark talent, and secure cloud professionals with experience operating in complex, real-world environments. 

If you are planning to strengthen cloud capability ahead of 2026, request a call back to discuss workforce planning, role design, and current market conditions, or submit a vacancy to access targeted cloud talent aligned to your technical and commercial priorities.