As someone who runs U.S. operations for a cloud infrastructure provider, I have an obvious stake in this argument — but I’d make it even if I sold nothing at all: the data below shows enterprises are struggling with inference economics regardless of vendor.
Enterprise AI has moved beyond the question of whether a model can produce an impressive result. The more important question is whether that result can be delivered reliably, securely, and at a sustainable cost — thousands or millions of times, across real users and operating conditions.
That question is becoming urgent because adoption is moving faster than operational maturity. The 2026 Stanford AI Index found that 88% of surveyed organizations use AI, yet agent deployment remains in the single digits across nearly every business function. That gap matters: “using AI” often means a chatbot or copilot embedded in existing software, while “agent deployment” means a system taking autonomous action on business processes — a much higher bar for reliability and trust. Enterprises have demonstrated that they can experiment with AI. Turning those experiments into dependable production systems, however, is proving far more difficult.
The challenge comes into focus at inference: the stage at which a trained model receives new information and produces a prediction, recommendation, classification, or decision. This is where an AI investment encounters real workloads, customers, financial constraints, and regulatory obligations. It is also where an enterprise discovers whether its AI strategy can hold up at scale.
Training may be computationally intensive, but it is generally a bounded activity. Inference is continuous. It must work for every request, every user, and every location in which an application operates. Its cost and performance accumulate for as long as the system remains in production.
If you hold budget authority over AI but don’t write the code yourself, the five decisions below are the questions to put to your team — not tasks for you to execute personally. Knowing what to ask is what separates leaders who can hold their engineers accountable from leaders who are simply told the project is “going well.”
The model is only one part of the product
Enterprises often evaluate AI by asking which model performs best. That question is becoming less decisive.
The same Stanford AI Index found that leading models are increasingly clustered near one another on widely used performance measures. As model capabilities converge, competitive differentiation shifts toward cost, reliability, and performance within a specific business domain.
In production, an AI system also needs data to arrive in the right format and at the right time. It needs storage, networking, security controls, monitoring, version management, and a process for handling failures. If an application uses proprietary company information, it may need to retrieve that information, rank it, add it to the model’s context, and verify the resulting answer before the user notices a delay.
This creates a chain of dependencies. A fast model cannot compensate for a slow data pipeline. More GPU capacity cannot correct poor retrieval. An impressive answer is not valuable if it arrives after the customer has abandoned the interaction.
Organizations should therefore stop treating deployment as the final step in a model-development project. Production AI is an operating discipline. That discipline begins with five practical decisions.
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Define the service before selecting the infrastructure
Begin with the business outcome, not the model. What decision will the system improve? How quickly must it respond? How many requests can it handle simultaneously? What error rate is acceptable? When does a human need to intervene?
These questions should become measurable service targets. Track response time under peak demand, availability, cost per request, and — most importantly — cost per successful business outcome. A cheaper inference request is not an improvement if it produces more errors or requires employees to redo the work.
This approach also prevents organizations from defaulting to the largest available model. For many narrowly defined tasks, a smaller or specialized model can deliver sufficient quality with lower latency and better economics. The right model is not necessarily the one that tops a general benchmark. It is the one that performs the required task consistently within the organization’s operational and financial constraints.
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Match the hardware to the workload
There is no universally correct processor for inference. GPUs are well suited to highly parallel, computationally intensive workloads, including large generative models and high-volume image processing. CPUs may be more economical for smaller models, conventional machine learning, data preparation, or applications with modest throughput requirements. The most effective architecture may use both.
Consider a document-classification system that a mid-size insurer built on a GPU cluster because it’s what the vendor recommended for “AI workloads.” When the team finally benchmarked it against the actual traffic — short documents, moderate volume, no real-time requirement — a CPU-based deployment handled the same load at roughly a third of the infrastructure cost, with latency the business never noticed. The GPU cluster wasn’t wrong for AI in general; it was wrong for this workload and nobody had checked.
The range of options is enormous. MLCommons reports that inference systems span at least three orders of magnitude in power consumption and five orders of magnitude in performance, from embedded devices to data-center platforms. That variability makes buying infrastructure by brand, specification, or reputation a poor substitute for testing.
Ask your team to show you benchmark results run on your actual traffic – realistic input lengths, concurrency levels, data movement, and response-time requirements. They should also consider whether the workload is steady or highly variable and whether it must run centrally, in a particular jurisdiction, or closer to the user at the edge.
The goal is the best balance of quality, speed, utilization, energy consumption, and cost.
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Optimize the entire inference path
Infrastructure teams should measure the full journey from input to useful output.
That means examining data ingestion, cleaning, retrieval, network transit, model execution, post-processing, and response delivery. Caching repeated results, batching compatible requests, shortening unnecessarily large prompts, quantizing models, or routing routine questions to smaller models can sometimes produce greater gains than adding compute.
Consider a customer-support system routing every query through a large general-purpose model. After profiling the inference path, a team might find that 70% of requests are simple lookups — order status, return policy — that a smaller, fine-tuned model handles just as accurately at a fraction of the latency and cost, reserving the large model for genuinely complex escalations. The aggregate savings often exceed anything achievable by negotiating better GPU pricing.
Observability is essential. Teams need to see latency, resource utilization, failures, model drift, and output quality together. If these measures live in separate operational silos, the root cause of a problem will be difficult to find and expensive to fix.
This is also a governance requirement. The NIST AI Risk Management Framework recommends testing AI systems before deployment and regularly during operation. It calls for production monitoring, clearly defined human-oversight responsibilities, and contingency plans for failures involving third-party AI systems or data.
A model is not ready for production simply because it passed a pre-launch evaluation. Its behavior must be monitored as users, data, and operating conditions change. Teams also need clear thresholds for when to retrain, replace, restrict, or deactivate a system.
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Model the economics before usage scales
Proofs of concept are forgiving because traffic is limited. This is not the case in a production environment.
AI costs can rise with query volume, token consumption, storage, networking, data retrieval, idle capacity, and transfers between systems or regions. Even a low per-request price becomes material when an application serves millions of interactions.
The FinOps Foundation’s 2026 State of FinOps report found that 98% of respondents now manage AI spending, up from 63% in 2025 and 31% in 2024. Respondents identified limited cost visibility, widely varying pricing models, difficulty allocating costs, and uncertainty about return on investment as significant challenges.
Before launch, organizations should model average and peak demand. That model should include long prompts, retries, seasonal spikes, redundant capacity, monitoring, and data-transfer charges. Leaders should establish spending thresholds and assign every material cost to a product, team, or business outcome.
It’s appropriate, also, for organizations to compare consumption-based services with reserved or dedicated capacity. Variable demand may favor elastic infrastructure, while steady workloads can benefit from resources that remain highly utilized. Some applications may warrant a hybrid approach, with predictable baseline demand running on dedicated capacity and bursts handled through elastic resources.
Cost predictability should be treated as an architectural requirement, not something left for finance to investigate after deployment.
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Make control and portability design requirements
Inference frequently involves an organization’s most valuable information: customer records, internal documents, operational data, source code, or intellectual property.
Leaders need to know where prompts, outputs, logs, embeddings, and temporary data are stored; who can access them; how long they are retained; and whether they can be used to improve a third party’s model. Data sovereignty shapes customer trust, business continuity, and an organization’s freedom to operate across jurisdictions.
The same principle applies to technology choices. Open frameworks, standard interfaces, portable containers, and exportable data reduce the cost of changing models or infrastructure later. Proprietary services can accelerate an initial launch, but that convenience must be weighed against long-term dependency.
Portability isn’t free, and it isn’t always worth it. Designing for it — abstraction layers, standard interfaces, avoiding proprietary APIs — can add real engineering overhead and slow an initial launch. For a low-stakes internal tool, that cost usually isn’t justified. For a system touching regulated data or core customer workflows, it usually is. The switching costs that show up later are specific and often underestimated: re-embedding an entire vector database for a new model, rewriting prompts that were quietly tuned to one model’s quirks, retraining fine-tuned variants from scratch, and honoring contractual data-deletion timelines with a vendor you’re trying to leave. They are routine costs that only become visible when someone actually tries to switch.
Every production AI plan should therefore include an exit test: Can we move the model, data, application, and operational telemetry? How long would it take, what would it cost, and which capabilities would be lost?
Portability does not require enterprises to move workloads constantly. It gives them leverage, resilience, and the ability to respond when pricing, technology, regulations, or business requirements change.
Own the advantage, not necessarily every server
Owning an AI strategy does not mean owning every chip, server, or data center. Most enterprises will operate across a mix of public cloud, private infrastructure, hosted platforms, third-party APIs, and edge systems.
What they must own is the ability to choose, and to change course as needed. That means controlling data policy, performance requirements, unit economics, model selection, and migration paths, regardless of who owns the underlying infrastructure.
Here is the test that will actually separate enterprises over the next three years: if your primary model’s price rose 30% tomorrow, or your vendor discontinued the API you depend on, could you execute this quarter, not next year? For most organizations today, the honest answer is no. That answer, more than adoption statistics or benchmark scores, is the real measure of enterprise AI readiness in 2026. Inference is where that readiness gets tested, every day, whether or not anyone is watching.
