Enterprise AI
Cloud infrastructure
Also known as: cloud computing infrastructure
Cloud infrastructure is the compute, storage, networking and supporting services that a provider runs and rents out on demand, rather than an organisation buying and housing its own servers. It covers virtual machines, containers, object storage, databases, content delivery and the identity and monitoring layers around them. Teams use it to scale capacity up and down without long procurement cycles or fixed hardware costs.
What it is
Cloud infrastructure is the layer beneath applications, usually consumed as infrastructure as a service or as managed platform services from providers such as AWS, Microsoft Azure and Google Cloud. It is defined by on demand provisioning, metered billing and the ability to place workloads in multiple regions. Most organisations now run a mix of cloud and on premise systems rather than one or the other.
Why it matters
How your site is hosted and delivered decides whether crawlers, assistants and shoppers get fast, reliable responses, which feeds into page experience, indexing and the freshness of what appears in AI answers. Cloud infrastructure also carries the cost of AI features, because embeddings, vector search and model inference consume compute and storage that must be budgeted. Poor infrastructure choices show up as slow pages, timeouts during peaks and outages during campaigns.
How it works
Teams choose regions close to their audience, put a content delivery network in front of static assets, and use autoscaling so traffic spikes from launches or seasonal peaks do not exhaust capacity. Infrastructure as code and staged deployments keep changes repeatable, while logging and monitoring track latency, error rates and spend. For AI work, practitioners add managed vector stores, model endpoints and caching to keep response times and unit costs predictable.
When it applies
It applies whenever you run a website, app, data pipeline or AI feature that needs capacity beyond a single fixed server. It becomes a board level topic during replatforming, international expansion or any project that introduces model inference at scale.
Examples
- A retailer autoscales its storefront ahead of a Black Friday campaign and adds edge caching for product images
- A publisher moves its image and video delivery to a CDN and cuts largest contentful paint on article pages
- A B2B software firm hosts a retrieval layer and embedding store in cloud services to power an on site AI assistant
How it is measured
- Server response time and error rate at origin and at the edge
- Uptime and number of incidents affecting public pages or APIs
- Cloud cost per thousand sessions, or cost per AI request
- Crawl errors and timeouts reported in server logs and Search Console
Insights on Cloud infrastructure
Related terms in Enterprise AI
- AI agentsAI agents are software systems that use a language model to plan and carry out multi-step tasks, rather than simply returning a block of text. They can call tools, query APIs, browse websites and write to other systems in pursuit of a goal, with varying degrees of human oversight. The term covers everything from a scripted assistant that books a meeting to a research agent that gathers sources and drafts a report.
- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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