Big Tech is pouring over $300 billion into AI infrastructure in 2024-2025. Microsoft, Google, Amazon, and Meta are reshaping enterprise technology in real time, and the decisions you make about where to put your AI infrastructure budget need to account for what these hyperscalers are building. This infrastructure breakdown is part of our comprehensive guide to Big Tech valuation dynamics, which explores why these companies have reached trillion-dollar market caps and what it means for technology leaders.
The way they spend their money tells you which platforms will be around for the long haul, what capabilities are coming down the pipeline, and where your dollars should be going. This article breaks down spending across cloud platforms, GPU investments, data centre construction, and the HBM memory supply chain that’s constraining everything.
Let’s get into it.
What Percentage of Big Tech Capital Expenditure Goes to AI Infrastructure?
Microsoft, Alphabet, Amazon and Meta plan to increase their capital expenditures to more than $300 billion in 2025. That’s roughly 60-70% of their total capex going into AI-related infrastructure.
Microsoft’s fiscal 2024 capex hit $55.7 billion. Microsoft reported nearly $78 billion in quarterly revenue for Q1 FY2026, with Azure and cloud services revenue surging 40% year-over-year. The Microsoft Cloud segment alone pulled in $49.1 billion in revenue.
Amazon is projected to spend $75 billion on capex in 2024, with AWS AI infrastructure as the primary driver. Google’s Alphabet is spending approximately $52 billion annually on data centres and AI compute. Meta is allocating $35-40 billion to AI infrastructure including custom silicon development.
These figures are part of a broader industry shift. IT consultancy Gartner reckons a total of $475 billion will be spent on data centres this year, up 42% on 2024. And McKinsey predicted in April that $5.2 trillion of investment in data centres would be required by 2030.
This represents a 3-4x increase from pre-ChatGPT 2022 levels. Spending growth is outpacing revenue growth, which tells you these companies are prioritising strategic positioning over short-term profitability. As Jensen Huang put it: “I don’t know any company, industry or country who thinks that intelligence is optional – it’s essential infrastructure.”
What does this mean for you? Platform stability. When a company is betting billions on AI infrastructure, they’re in it for the long haul. Your choice of platform needs to factor in both today’s features and who will still be heavily invested five years from now. For a broader understanding of how these investments translate to trillion dollar market capitalisation, see our overview of Big Tech valuation dynamics.
How Does AI Infrastructure Spending Break Down Across Categories?
Let’s look at where the money actually goes.
GPU and accelerator procurement accounts for 35-45% of AI infrastructure capex. Data centre construction and expansion takes 30-40%. Networking and interconnect infrastructure requires 10-15%, and cooling and power systems need 5-10%.
GPU costs dominate. The chips to operate a 1-GW center are estimated to cost approximately $20 billion, on top of $10 billion for the facility. That’s two-thirds of your cost just in silicon.
Power costs are substantial and often underestimated. Racks of computers running Nvidia’s chips consume at least 10 times as much power as regular web servers. A rack with the latest AI chips needs the same power as 10 to 15 racks at a conventional site.
Operating costs run into millions of dollars per MW per year, mainly due to power consumption. And if you need dedicated power? It would cost about $3.5 billion to build a big enough gas power plant to run a 1-GW data center.
These hyperscaler figures translate to your budget at a different scale. Promethium estimates that technology and talent costs represent 30-40% of total AI investment, including AI specialist hiring and training. Data and process transformation costs add another 20-30%.
HBM memory supply constraints are driving a 15-20% premium on GPU costs. We’ll dig into why Samsung’s profits are surging later, but the short version is that memory is tight and will stay tight.
When you’re calculating total cost of ownership, make sure you’re capturing the full picture: compute, memory, power, cooling, networking, and the people to run it all.
How Does Azure’s AI Infrastructure Compare to AWS and Google Cloud?
The cloud platform decision often comes down to your existing stack, but let’s look at what each platform brings to AI workloads.
Azure and cloud services revenue surged 40% year-over-year in Q1 FY2026, with AI services driving over half of new revenue. Azure’s OpenAI integration gives you exclusive access to GPT-4 and future models for enterprise deployments. If you’re a Microsoft shop with Active Directory and existing licensing, this integration is hard to beat.
AWS maintains the largest overall cloud market share at about 32%. They’ve got the most mature SageMaker ML platform and the broadest GPU instance selection. If you want maximum flexibility and the widest range of pricing options—including Savings Plans and Spot Instances for GPU compute—AWS delivers.
Google Cloud offers something different: TPU alternatives to GPUs. Cloud TPU v5e delivers 2.7x higher performance per dollar compared to TPU v4. For compatible workloads, you’re looking at 30-40% cost reduction. Google’s Vertex AI also provides the strongest MLOps automation for teams with limited ML engineering resources.
Domenic Donato, VP of Technology at AssemblyAI, noted that “Cloud TPU v5e consistently delivered up to 4X greater performance per dollar than comparable solutions.”
For pricing specifics: Azure OpenAI Service offers Standard pay-as-you-go, Provisioned for predictable costs, and Batch API with 50% discount. GCP offers H100 on 8-GPU instance from us-central1 for $88.49 per hour, A100 80GB from $6.25 per hour.
Here’s the practical guidance: Azure for Microsoft-heavy environments, AWS for maximum flexibility and GPU availability, GCP for Google AI model access and teams that want strong MLOps without building everything from scratch.
What Are the Key Differences Between NVIDIA Blackwell, H100, and Competitor GPUs?
Nvidia commands approximately 90% of the AI chip market and has received more than $500 billion in orders for AI chips extending through 2026. That market dominance is why GPU prices are what they are.
The current enterprise standard is the H100 with 80GB HBM3 memory and 3.35TB/s bandwidth. The newer H200 pushes to 144GB of memory. Blackwell architecture delivers 2.5x training performance and 5x inference performance over H100. The company manufactures Blackwell GPUs in Arizona and plans to deliver 14 million additional units over the next five quarters.
The A100 offers 60-70% of H100 performance at approximately 40% lower cost for budget-conscious deployments. Reserved cluster pricing puts NVIDIA H200 starting at $2.09/hour, H100 starting at $1.75/hour, A100 starting at $1.30/hour.
AMD is making inroads. The MI300 node has 1,536GB HBM capacity vs H100 node’s 640GB HBM capacity. That’s a significant advantage for memory-bound workloads. AMD MI325X delivers better performance per dollar for large dense model serving and certain latency scenarios.
Intel Gaudi2 targets inference at 40-50% lower cost than NVIDIA equivalents but with limited training capability.
Here’s what matters for your decision: For hyperscalers and enterprises owning GPUs, Nvidia has stronger performance per dollar in some workloads while AMD has stronger perf/$ in others. But for customers using short-term rentals from Neoclouds, Nvidia always wins on performance per dollar due to AMD’s limited availability.
If you’re renting cloud instances, you’re probably going NVIDIA. If you’re buying hardware for high-utilisation inference, AMD deserves serious evaluation.
Why Did Samsung’s Chip Profits Surge 160% and What Does It Mean for AI Infrastructure?
HBM—High Bandwidth Memory—is the bottleneck everyone’s talking about. Samsung and SK Hynix are the primary producers, and AI demand is consuming 30%+ of global HBM production capacity.
Samsung is advancing HBM4 high-bandwidth memory chips for AI servers, targeting production next year. They’ve also announced a partnership with Nvidia to establish an AI Megafactory deploying more than 50,000 GPUs.
Here’s why HBM matters to your planning: The two main system specifications that matter for inference are HBM capacity and HBM bandwidth. More capacity means you can run larger models. More bandwidth means faster token generation.
HBM commands a 5x price premium over standard DRAM due to complex 3D stacking manufacturing. SK Hynix is capturing over 50% of the HBM market with NVIDIA-validated HBM3 and HBM3e production. Samsung has been racing to qualify HBM3e with NVIDIA after quality issues delayed initial shipments.
What does this mean practically? Memory supply constraints directly impact GPU availability and pricing for enterprise buyers. GB200 NVL72 faces massive delays due to challenges integrating NVLink backplane. When high-end GPUs are delayed, everyone downstream feels it.
HBM supply is expected to remain tight through 2025 as demand outpaces capacity expansion. Micron is entering the market as a third supplier, potentially easing constraints in 2026.
For procurement planning, expect 6-12 month lead times on high-end GPU configurations. If you need guaranteed capacity, start the conversation now and consider reserved instances with your cloud provider. To understand how different company spending strategies have led to varying outcomes, including Samsung’s memory supply advantage and Meta’s profit challenges, see our comparison of Magnificent Seven AI strategies.
How Much Does Cloud AI Infrastructure Cost Compared to On-Premise Deployment?
Let’s talk numbers.
On-premise? On-prem total system cost for 8x H100: approximately $833,806. That includes the hardware but not the facility costs.
The breakeven point is approximately 8,556 hours or 11.9 months of usage. But that’s assuming high utilisation. On-premises hosting becomes cheaper than cloud when usage stays above 60-70% throughout hardware lifespan. Below that utilisation threshold, cloud wins.
Add in facility costs. Estimated on-prem power and cooling cost: approximately $0.87 per hour at $0.15/kWh. That doesn’t sound like much until you multiply it across a year of continuous operation.
Don’t forget egress. Cloud providers charge substantial fees for data egress when data leaves their networks. If you’re moving large datasets around, these costs add up fast.
For mid-market companies, cloud typically wins. You don’t have the capital for $800K hardware purchases, you probably won’t hit 60-70% utilisation, and you need the flexibility to scale down during development phases.
Here’s the practical approach: Start with cloud for proof-of-concept and early production. Monitor your utilisation closely. Once you have stable, high-utilisation inference workloads, evaluate bringing those specific workloads on-premise while keeping development and burst capacity in the cloud.
What ROI Timeline Should You Expect from AI Infrastructure Investments?
Plan for these typical timelines.
Short-term (6-12 months): Process efficiency gains of 15-25%, cost reductions of 10-20%, time savings of 2-4 hours per employee per week. Medium-term (12-24 months): Revenue impact of 5-15% increase, customer satisfaction 10-30% improvement. Long-term (24+ months): Business transformation with 3-5x faster development of new products and services.
The statistics back this up. Organisations see average return of $3.50 for every dollar invested in AI technology. Forrester research shows 333% ROI and $12.02 million NPV over three years for well-implemented AI.
But here’s the reality check: Only 10% currently see significant, measurable ROI from agentic AI, but most expect returns within 1-5 years. And 60% expect ROI from advanced AI automation levels to take longer than 3 years.
Infrastructure costs represent 20-40% of total AI project investment. Skills and integration dominate the budget. That’s why cloud deployment reduces time-to-value by 40-60% compared to on-premise—you’re not spending months on infrastructure before you write your first line of model code.
The takeaway: Plan for 18-36 months to full ROI realisation. Build in quick wins at 6-12 months to maintain organisational support. And remember that 30-40% of enterprise AI projects fail to reach production, so phased investment isn’t being conservative—it’s being sensible. For detailed guidance on ROI measurement frameworks and how to calculate AI returns effectively, see our practical frameworks article.
How Should You Approach AI Platform Selection?
Start with your existing vendor relationships. Microsoft shops benefit from Azure integration. AWS shops can leverage existing tooling and institutional knowledge. Don’t underestimate the value of staying in ecosystem.
Evaluate GPU availability and pricing before features. Instance availability varies significantly by region. If you can’t get the compute when you need it, the features don’t matter.
Consider data location requirements. Australian compliance may limit your platform options. Check whether your chosen provider has Australian regions with the GPU instances you need.
Nearly 9.7 million developers are currently running AI workloads in the cloud, making it the leading deployment model for good reason: no upfront capex, faster time-to-value, and flexibility to experiment.
Begin with managed services—SageMaker, Azure ML, Vertex AI—to reduce operational overhead. You can always move to more custom setups later.
Watch out for the cloud credit trap. Cloud credits from major providers mask true costs, leading to “financial cliff” when credits expire. Model your costs without credits from day one.
Implement multi-cloud readiness through containerisation even if you’re initially single-cloud. Kubernetes and containers give you options later without requiring architectural changes.
Budget for skills development. Platform-specific expertise significantly impacts project success. Factor training costs into your first-year budget.
Run proof-of-concept on 2-3 platforms before committing. Organisations typically deploy 2-3 simultaneous AI coding tools—apply the same evaluation approach to your infrastructure. Once workloads are proven, plan for reserved capacity; 40-70% savings are available for committed 1-3 year terms.
FAQ Section
How much are the Big Tech companies spending on AI in 2025?
Microsoft, Amazon, Google, and Meta are collectively putting over $300 billion into AI infrastructure during 2024-2025, with individual company capex ranging from $35-75 billion annually. This represents a 3-4x increase from 2022 spending levels.
Which cloud platform is best for enterprise AI workloads?
It depends on your existing technology stack. Azure excels for Microsoft-heavy environments with strong Active Directory integration, AWS offers maximum flexibility and GPU availability, while Google Cloud provides cost-effective TPU alternatives and superior MLOps automation.
What is HBM and why does it matter for AI infrastructure?
High Bandwidth Memory (HBM) is specialised memory stacked vertically to achieve 5-10x the bandwidth of standard DRAM. Modern AI GPUs require HBM to feed data to tensor cores fast enough for efficient training and inference. HBM supply constraints directly impact GPU availability and pricing.
How long until AI infrastructure investments show ROI?
Proof-of-concept projects typically show results within 3-6 months, production deployments demonstrate business impact in 6-12 months, and full ROI realisation occurs in 18-36 months. Cloud deployment reduces time-to-value by 40-60% compared to on-premise for initial projects.
Should mid-market companies use cloud or on-premise for AI?
Mid-market companies typically achieve better TCO with cloud-first strategies due to lower capital requirements and faster time-to-value. Cloud breaks even with on-premise when utilisation stays above 60-70% throughout the hardware lifespan—a threshold most mid-market companies won’t consistently hit.
What is the difference between NVIDIA H100 and Blackwell GPUs?
NVIDIA Blackwell architecture delivers 2.5x training performance and 5x inference performance over H100, with improved energy efficiency. Blackwell availability is constrained through mid-2025 due to HBM3e supply limitations; H100 remains the current enterprise standard.
Why are GPU prices so high for enterprise AI?
NVIDIA controls approximately 90% of the AI training GPU market, commanding significant price premiums over competitors. Additionally, HBM memory costs 5x more than standard DRAM, and supply constraints for both GPUs and HBM keep prices elevated.
How do I calculate total cost of ownership for cloud AI?
TCO calculation must include compute costs, storage, data egress, networking, managed service fees, and personnel time. For on-premise comparisons, add facility costs (30-40% overhead), depreciation, and refresh cycles. Reserved instances reduce cloud costs by 40-70% for committed usage.
What percentage of cloud growth is driven by AI workloads?
AI workloads are driving 50-70% of new cloud revenue growth for major providers. Microsoft reports AI services contributing over half of Azure’s 40% year-over-year growth, and AWS attributes significant growth to generative AI and ML services.
Is AMD MI300X a viable alternative to NVIDIA for enterprise AI?
AMD MI300X offers significantly more HBM memory than H100 and competitive performance for memory-bound workloads, particularly inference. However, NVIDIA’s CUDA ecosystem and software maturity make it better suited for training workloads where ecosystem support matters.
How do I get started with AI infrastructure on a limited budget?
Begin with cloud-based proof-of-concept using on-demand instances at $2-4/hour for GPU compute. Use managed ML platforms to reduce operational overhead. Transition to reserved instances once workloads are proven for 40-70% cost reduction.
What is the difference between GPU instances for training vs inference?
Training requires maximum GPU memory and compute power (H100, H200, MI325X), while inference can often use smaller, more cost-effective GPUs (A100, Intel Gaudi). Inference workloads typically have higher utilisation, making on-premise more cost-effective for production serving once volumes are proven.
