What is the difference between training and inference in AI?

Training is when a model learns patterns from data - it is the expensive, time-consuming process of building the model. Inference is when the trained model processes new inputs to generate outputs - answering questions, making predictions, or generating content. Training happens once or periodically. Inference happens every time someone uses the model, making it the ongoing operational cost.

Why does inference cost matter for AI companies?

Inference is the primary ongoing expense for AI products. Every time ChatGPT answers a question or Google AI Overview generates a summary, it costs the provider money in compute. OpenAI reportedly spends hundreds of millions annually on inference costs alone. These costs directly affect pricing, availability, and the economic viability of AI-powered products.

What Is Inference in AI?

Q: What is edge inference?

Edge inference runs AI models directly on user devices like phones, laptops, or IoT sensors rather than sending data to cloud servers. Apple's on-device AI features and Google's on-device language models are examples. Edge inference offers lower latency, better privacy, and no internet dependency, but is limited by the device's processing power and memory.

Inference in AI is the process of using a trained machine learning model to generate outputs from new inputs - answering questions, making predictions, classifying images, generating text, or producing any other output the model was designed for. When you ask ChatGPT a question and receive an answer, the computation that generates that answer is inference. According to McKinsey's analysis of AI infrastructure costs, inference accounts for approximately 60 to 90% of the total compute costs for deployed AI systems, making it the dominant expense in running AI products at scale.

How Does Inference Differ From Training?

Training and inference are the two fundamental phases of any machine learning system, and they require very different resources:

Training is the learning phase. A model processes massive datasets - sometimes trillions of tokens for large language models - to learn patterns, relationships, and structures in the data. Training a frontier LLM like GPT-4 or Claude costs tens to hundreds of millions of dollars in compute and takes weeks to months on thousands of specialized GPUs. Training happens once per model version (though models may be fine-tuned additionally).

Inference is the application phase. Once trained, the model uses its learned patterns to process individual inputs and generate outputs. Each ChatGPT response, each Google AI Overview, each AI-generated image is an inference operation. Inference is cheaper per operation than training but happens millions or billions of times per day across all users, making total inference costs enormous.

The analogy is education versus work. Training is like going to school - expensive, time-consuming, but you do it once. Inference is like doing your job every day - cheaper per task but the ongoing cost that never stops.

Why Do Inference Costs Matter?

Inference costs are the economic bottleneck of the AI industry. According to analysis from SemiAnalysis, OpenAI's inference costs for ChatGPT's free tier alone likely exceed hundreds of millions of dollars annually. Every query to every AI product costs money in GPU compute, electricity, and cooling.

This cost structure affects everything users experience:

Pricing. AI subscription prices ($20/month for ChatGPT Plus, Claude Pro) are set to cover inference costs plus margin. If inference costs drop, prices can drop. If usage spikes beyond projections, companies face difficult choices about rate limiting or price increases.

Rate limiting. When AI tools limit how many messages you can send per hour or day, that is directly driven by inference cost management. The provider is balancing user experience against the cost of processing each request.

Response quality. Companies can reduce inference costs by using smaller, faster models - but smaller models generally produce lower-quality outputs. The choice between model size and inference speed is a constant tradeoff. This is why many AI products offer multiple model tiers - faster, cheaper models for simple tasks and larger, more expensive models for complex ones.

Availability. Inference capacity determines how many users can use a product simultaneously. Surges in demand (like when a new AI feature goes viral) can overwhelm inference infrastructure, causing slowdowns or outages.

How Does Inference Power AI Search?

Every AI search interaction involves multiple inference operations:

When you ask ChatGPT Search or Perplexity a question, the system first uses inference to understand your query and determine what information it needs. Then it searches the web, retrieves relevant pages, and uses inference again to read, synthesize, and generate a coherent answer with citations. A single AI search query may require several inference calls working in sequence.

This multi-step inference process is why AI search is significantly more expensive per query than traditional search. A Google search query costs fractions of a cent in infrastructure. An AI-powered search query with web retrieval and response generation costs substantially more. According to Morgan Stanley estimates, an AI-generated search response costs approximately 10 times more than a traditional search result to serve.

This cost difference explains why AI search monetization is one of the biggest open questions in the industry. The current model - subscriptions for premium tiers, ads alongside AI responses, or API-based pricing - is still evolving as companies figure out how to make AI search economically sustainable.

What Makes Inference Faster?

Several technical approaches reduce inference time and cost:

Model quantization. Reducing the numerical precision of model weights from 32-bit to 16-bit, 8-bit, or even 4-bit floating point. Quantized models use less memory and compute per inference call with minimal quality loss. This is how AI features run on smartphones despite limited hardware.

Speculative decoding. Using a smaller, faster model to draft initial tokens, then having the larger model verify and correct them. This can speed up inference by 2 to 3x because the smaller model handles easy predictions while the larger model only intervenes for complex ones.

Hardware optimization. Custom AI chips like Google's TPUs, Amazon's Trainium, and specialized inference accelerators are designed specifically for inference workloads. These chips deliver more inference operations per watt and per dollar than general-purpose GPUs.

Caching and RAG. Storing and reusing common computations reduces redundant inference work. If a thousand users ask similar questions, cached partial computations can speed up responses. Retrieval-augmented generation reduces the computational load by providing the model with relevant context rather than requiring it to recall everything from its parameters.

What Is Edge Inference?

Edge inference runs AI models directly on user devices - phones, laptops, cars, IoT devices - rather than sending data to cloud servers for processing. Apple's on-device intelligence features, Google's on-device language models, and offline AI assistants all use edge inference.

The advantages are significant: near-zero latency (no network round trip), privacy preservation (data never leaves the device), and offline functionality. The limitation is hardware - consumer devices have far less compute power than data center GPUs, so edge models must be smaller and less capable than cloud models.

For the AI industry, edge inference represents a potential path to reducing cloud infrastructure costs while improving user experience. As mobile chips become more powerful and model compression techniques improve, more AI functionality will shift from cloud inference to on-device inference - changing the economics of AI products fundamentally.