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Many enterprise systems were designed for conventional analytics and general-purpose computing, not the different latency, throughput, memory, data-control, and power demands of production AI inference. The answer is not simply to add more accelerators: it is to match the full infrastructure stack and deployment location to each workload’s service goals and cost per useful outcome.
Why inference exposes gaps in conventional infrastructure
Inference is the work of running a trained AI model to produce responses or other outputs. Unlike a single, uniform application category, production inference spans interactive conversations, streaming voice, media generation, coding assistants, agents, and systems that interact with the physical world. They do not share one ideal balance of response time, throughput, memory, data movement, and energy use.
That mismatch is showing up in enterprise planning. A Cloudera-commissioned survey of 1,500 enterprise architects and infrastructure or data leaders, conducted by Wakefield Research across nine markets from June 5 to June 22, 2026, found that 72% said their current data architecture needed a significant overhaul for future AI requirements. The survey addresses enterprise AI broadly, not inference alone, so it is evidence of reported readiness concerns—not a measure of how many companies have inadequate inference systems.
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The same survey found that 75% said AI integrations had changed storage and architecture practices, 84% reported increased infrastructure costs driven by AI workloads, and 73% said AI had made data governance more complex. These are respondents’ reports from a vendor-commissioned survey, not universal measures of cost growth or infrastructure failure.
Inference needs more than accelerators
Accelerated compute can be essential, but a production system is a pipeline. A model needs compute to execute, memory to hold working data, networking to move data, storage to serve it, and orchestration and observability to keep services operating. Power, cooling, governance, security, and staff expertise also constrain what can be deployed and sustained.
An IDC brief hosted by Intel identifies heterogeneous or accelerated compute, faster memory, high-bandwidth networking, and flash storage as possible investments for production AI infrastructure. The OECD likewise describes energy-efficient data centers, accelerator clusters, workload orchestration, and advanced networking. Neither source establishes a universal bill of materials: the right combination depends on the service being delivered and the facility or platform that runs it.
This is why a GPU-only plan can disappoint. Accelerators do not by themselves fix slow data access, insufficient memory, network bottlenecks, poor scheduling, governance constraints, or facility limits. Conversely, buying every infrastructure component at the highest specification is not a sound strategy if the workload does not need it.
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Start with the workload’s service goal
Define what a successful response means before selecting hardware or placement. McKinsey’s workload groupings illustrate why one benchmark or server profile cannot stand in for all inference:
| Workload | Service characteristic | Infrastructure question to answer |
|---|---|---|
| Chat and search | Interactive and latency-sensitive | What response-time target matters to users, and can the system sustain it at expected concurrency? |
| Voice | Streaming and real-time | Can the system process a continuing stream without interrupting the interaction? |
| Media generation | Batch-oriented and throughput-sensitive | How much output can be produced over the required period, and at what cost? |
| Agentic and code workloads | Long-context, multistep, and cache-intensive | Can memory, caching, and orchestration support extended context and repeated steps efficiently? |
| Physical AI | Constrained by latency and power and thermal budgets | Can the system meet response needs within the device’s or site’s energy and heat limits? |
Set the service measures that correspond to the use case: response time or time to first token for interactive services, streaming continuity for voice, throughput for batch generation, and context or cache behavior for long-running agentic tasks. Physical AI also needs evaluation against its deployment’s latency, power, and thermal limits. The sources do not supply one universal threshold for any of these workloads.
Then measure the economics of the completed task, not just the component. McKinsey argues that enterprises may need to assess cost per useful outcome—for example, cost per resolved service ticket or accepted line of code. A cheaper token or faster accelerator is not necessarily a better result if it produces fewer accepted outputs or requires more retries and operational effort.
Choose cloud, on-premises, edge, or device by constraint
Cloud, enterprise or on-premises systems, edge infrastructure, and devices are all possible inference locations. The choice should follow the workload’s latency and throughput goals, economics and utilization, data location and governance needs, networking and memory demands, facility constraints, and the organization’s ability to operate the system reliably.
| Placement | Useful when | Questions to resolve |
|---|---|---|
| Cloud | AI compute is needed without placing every resource in an enterprise facility. | Do usage economics, data-control requirements, latency, and network access fit the service? |
| On-premises or enterprise infrastructure | Organizations need to operate inference within their own infrastructure environment. | Can the site support compute, memory, networking, storage, power, cooling, orchestration, and ongoing operations? |
| Edge | Workloads benefit from running nearer to where data is produced or action is required. | Can distributed sites meet their power, thermal, connectivity, security, and maintenance needs? |
| Device | Inference is suited to execution on the endpoint itself. | Can the device meet the task’s latency and capability needs within its resource and energy limits? |
IDC projected in 2025 that 75% of enterprise AI workloads would run on fit-for-purpose hybrid infrastructure by 2028. This is a forecast reproduced in an Intel-hosted analyst brief, not a measured result for 2028 or proof that hybrid is best for every workload. It does reinforce the practical point: organizations may need a mix of locations rather than one universal destination.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to evaluate an inference stack
- Describe the job. Identify the workload type, users or systems served, data involved, and what counts as a useful result. Record expected concurrency and whether work is interactive, streaming, batch, multistep, or tied to a physical process.
- Set service and governance requirements. Establish the response or throughput goal and determine where data may be processed, what governance applies, and which security or sovereignty requirements constrain placement.
- Map the complete path. Check compute and accelerators alongside memory, data movement and networking, storage, power and cooling, orchestration, and observability. Identify which component is limiting the service rather than assuming the accelerator is the bottleneck.
- Compare feasible placements. Evaluate cloud, on-premises, edge, and device options against the same service goals, data rules, facility conditions, operating skills, and reliability needs. Do not assume that one location wins on every axis.
- Measure useful outcomes and utilization. Compare the cost and performance of completed tasks—such as resolved tickets or accepted code—under representative workload conditions. Include whether available capacity is being used effectively, not just peak component specifications.
- Reassess as workloads change. AI integrations can affect architecture, storage, costs, and governance. Review those dimensions as services and operating requirements evolve rather than treating an infrastructure decision as permanent.
What the available figures do—and do not—show
An Akamai report page says 88.4% of early integration teams lacked visibility into AI unit economics and that 80% of organizations serving AI globally required sub-250 ms responses for their top use case. The accessible page does not state a publication year alongside those figures, so they should not be read as a dated industry census or as targets for every application. The reported response requirement concerns organizations’ top use case, not every inference request.
Other evidence also has limits. Uptime Institute’s survey listing identifies a survey of 1,141 people, but its detailed findings require access; no additional result can be established from that listing. Across the available sources, there is no single standard threshold, hardware configuration, or placement strategy that applies to all production inference. The useful conclusion is to make the workload’s service goal, data constraints, operating conditions, and cost per useful outcome the basis of the design.
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