Inference

The moment AI delivers business value — where trained models meet real-world decisions at scale.

By 2026, AI inference — the process of using a trained model to make predictions or generate outputs — accounts for over 80% of total AI compute spending globally, a figure that surprises many executives who assume training is the expensive part. As organisations move from experimenting with AI to deploying it in production, inference costs can reach $5-50 million annually for large-scale deployments. Understanding inference is essential for every C-suite leader because it directly determines the cost, speed, and reliability of every AI-powered feature your customers and employees interact with.

What It Is

Inference is the deployment phase of artificial intelligence. If training a model is like educating a new employee — a lengthy, expensive process of exposing them to millions of examples — then inference is that employee sitting at their desk making decisions. You show the model a new input (a customer query, a medical image, a financial transaction) and it produces an output (an answer, a diagnosis, a fraud score). Every time you ask Siri a question, receive a product recommendation on Amazon, or get a fraud alert from your bank, inference is happening behind the scenes.

The critical difference between training and inference is that training happens once (or periodically), whilst inference happens continuously — potentially millions of times per second at enterprise scale. This makes inference the dominant cost driver in production AI. A large language model like GPT-4 required an estimated $100 million in compute to train, but serving it to hundreds of millions of users costs substantially more on an annual basis. Think of training as building a factory; inference is running the factory. The factory construction is a one-time capital expense, but the day-to-day operations are where the ongoing costs accumulate.

Inference also has strict performance requirements that training does not. A training job can take days or weeks and still be acceptable, but inference must happen in real time or near-real time. A customer waiting for a chatbot response expects an answer in under two seconds. An autonomous vehicle must process sensor data and make decisions in milliseconds. These latency requirements constrain the hardware, architecture, and optimisation techniques available for inference workloads.

Why It Matters for Business

Inference cost is the single largest variable in the economics of AI deployment. For a large language model serving 10 million queries per day, inference costs can range from $50,000 to $500,000 per month depending on model size, hardware, and optimisation techniques. At scale, the difference between an optimised and unoptimised inference pipeline can represent tens of millions of dollars annually. Companies like Character.ai reportedly spent over $20 million per year on inference before implementing aggressive optimisation, and OpenAI's inference costs for ChatGPT have been estimated at $700,000 per day at peak usage.

Inference speed directly impacts user experience and revenue. Amazon found that every 100 milliseconds of latency costs 1% in sales. Applied to AI-powered features — search relevance, product recommendations, customer service chatbots — inference latency translates directly into customer satisfaction, conversion rates, and ultimately revenue. Google reported that improving inference latency by 30% for its search AI features increased user engagement by 10%. In financial trading, inference speed for AI-driven algorithms is measured in microseconds, and a 10-microsecond advantage can be worth millions in annual trading profits.

Reliability of inference systems is a business continuity concern. When an AI inference service goes down, every product feature dependent on it goes down too. If your customer support chatbot, fraud detection system, and content moderation tools all rely on the same inference infrastructure, a single point of failure can cascade across your entire operation. Enterprise inference architectures must be designed for high availability, redundancy, and graceful degradation.

Enterprise Use Cases

Meta serves AI inference for its recommendation models over 4 billion times per day across Facebook, Instagram, and WhatsApp, making it one of the largest inference workloads on the planet. To manage costs, Meta designed its own custom inference chip — the Meta Training and Inference Accelerator (MTIA) — which reduced inference costs by approximately 40% compared to off-the-shelf GPUs for its specific workload patterns. At Netflix, inference models power personalised thumbnail selection, selecting the image most likely to engage each individual subscriber from a catalogue of thousands of titles. This inference runs in under 50 milliseconds, serving 260 million subscribers with dynamically selected artwork that has measurably increased viewing engagement.

JPMorgan Chase runs real-time inference on every single payment transaction crossing its network — over $10 trillion in daily value. The models score each transaction for fraud probability in under 10 milliseconds, enabling the bank to block suspicious transactions before they complete whilst allowing legitimate ones to flow without interruption. The inference system processes over 500 million events per day with a reliability target of 99.999%. In healthcare, NHS England deployed inference models across 40 hospitals for real-time sepsis detection, analysing patient vital signs and lab results every minute to identify early warning signs. The system flags potential sepsis cases an average of 6 hours before clinicians would typically identify them, reducing sepsis mortality by an estimated 20%.

Vendor Landscape

The inference vendor landscape spans cloud providers, chipmakers, and specialist platforms. AWS offers SageMaker Endpoints and Inferentia2 custom chips for cost-effective inference at approximately $0.001-0.01 per 1,000 tokens for large language models. Google Cloud provides Vertex AI Prediction alongside its custom TPU inference hardware, with competitive pricing for Gemini model inference. Azure offers Azure ML Endpoints with optimised NVIDIA GPU instances. For on-premises inference, NVIDIA dominates with its L4 and T4 inference GPUs, whilst AMD's Instinct series offers a growing alternative. Specialist inference platforms include Together AI, Anyscale, and Modal, which offer serverless inference with pricing based on usage. Open-source inference engines such as vLLM and TensorRT-LLM have become the standard for organisations running their own models, offering 3-5x throughput improvements over naive deployments.

Strategic Considerations

Before investing in inference infrastructure, rigorously evaluate whether you need to run models yourself or can use managed AI services. For most organisations deploying standard capabilities — text summarisation, image classification, translation — managed API services from OpenAI, Google, or AWS are significantly cheaper and more reliable than self-hosted inference. Reserve self-hosted inference for cases involving proprietary models, data sovereignty requirements, extreme latency needs, or very high volume where per-query API costs become prohibitive. Implement comprehensive monitoring for inference latency, throughput, and cost from day one. Negotiate GPU capacity commitments with cloud providers well in advance — inference demand is growing faster than supply, and priority access increasingly goes to customers with long-term agreements. Finally, invest in model optimisation techniques such as quantisation, distillation, and caching, which can reduce inference costs by 50-90% with minimal impact on output quality.