AI Training vs Inference, Explained (Building the Model vs Running It)

We've all heard about the amount of computing power required to develop large AI models. Thousands of GPUs, enormous datasets, and training runs that can take weeks or even months. With how quickly AI is being adopted, it's reasonable to wonder whether every company building an AI application needs access to that kind of infrastructure.

Fortunately, most don't. A large number of AI applications today use models that have already been trained, allowing developers to focus on building the application rather than developing the underlying model.

This comes down to two different stages of machine learning: training and inference.

The Basics

Training is the process through which a machine learning model learns patterns and relationships from data. During training, the model processes examples, compares its predictions against expected results, and adjusts its internal parameters to improve performance. This happens repeatedly across large datasets until the model reaches an acceptable level of performance.

Inference is the process of using that trained model to generate predictions or responses. When you ask an LLM to summarize a document or generate a piece of code, it uses the parameters established during training to process your request. These parameters generally remain unchanged during inference.

Training requires the model to learn, while inference allows applications to use what the model has learned. The two stages can use similar computing resources, but their infrastructure requirements are often quite different.

Why It Exists

Training large AI models requires substantial computing resources. Depending on the model, this can involve thousands of GPUs working together, high-speed networking to coordinate calculations, and storage systems handling datasets and model checkpoints. Training can also involve multiple experiments before a model is ready for deployment.

Inference focuses on serving requests using the trained model. Instead of updating model parameters, the infrastructure processes incoming inputs and generates outputs. Performance is typically measured through latency, throughput, and the number of concurrent requests the system can support.

There's also fine-tuning, which involves taking an existing model and training it further on a smaller, specialized dataset. Organizations can use fine-tuning to adapt a model's behavior to specific tasks without having to develop a foundation model from scratch.

Common Pitfalls

• Assuming every AI application requires training: Many applications can use existing models through APIs without developing their own.
• Confusing fine-tuning with RAG: Fine-tuning updates model parameters, while RAG retrieves external information and supplies it during inference.
• Underestimating training costs: GPU compute, storage, networking, experimentation, and checkpoint management all contribute to expenses.
• Ignoring inference costs: Training may be a one-time or occasional expense, but inference costs continue as long as the application is being used.
• Choosing infrastructure without considering the workload: Hardware optimized for large training jobs may not be the most economical choice for inference.

Why It Matters

Cloud providers offer different services for training and inference workloads. AWS SageMaker AI and Azure Machine Learning support model development and training, while GPU-based cloud instances allow organizations to build custom training environments.

For inference, services such as Amazon Bedrock, Microsoft Foundry, and DigitalOcean Inference Engine provide managed access to AI models through APIs. Organizations can also deploy models on dedicated GPU infrastructure when they require additional control over the serving environment.

Consider a company building an internal knowledge assistant. It could use an existing language model, connect it to company documentation through RAG, and begin serving employees without training a model. A company developing a specialized image recognition system, however, might need to train or fine-tune a model using its own datasets. The infrastructure requirements, development effort, and costs would be quite different.

The TAM Lens

Training and inference introduce different considerations when planning cloud infrastructure. For training workloads, GPU availability, networking performance, storage capacity, and expected training duration are important factors. For inference, the focus shifts toward request volumes, response times, model size, and resource utilization.

The cost structures differ as well. Training often involves concentrated periods of high compute usage, while inference expenses generally increase with application usage. An application serving a small number of requests may be well suited to managed inference, while one handling sustained traffic could benefit from dedicated infrastructure.

Understanding these requirements early helps determine which services and hardware configurations are appropriate. It also allows teams to estimate how infrastructure costs may change as their applications grow.

How to Stay Sane

• Evaluate existing models: Determine whether an available model can meet your requirements before considering custom training.
• Understand the objective: Identify whether the application requires training, fine-tuning, or inference.
• Separate infrastructure budgets: Account for training expenses and ongoing inference costs independently.
• Measure the right metrics: Training throughput, inference latency, and request concurrency provide different insights.
• Consider fine-tuning selectively: Evaluate whether changes to the model's behavior are necessary or whether prompting and retrieval can address the requirement.
• Plan for production usage: Estimate traffic, token consumption, and infrastructure utilization before choosing a deployment approach.

Final Thoughts

Training and inference are closely related, but they serve different purposes and introduce different infrastructure requirements. Training develops a model's capabilities, while inference makes those capabilities available to applications and users.

For most businesses adopting AI, developing a model from scratch isn't necessary. Understanding the distinction between training and inference makes it easier to choose the appropriate infrastructure, manage costs, and build applications that can scale as usage grows.