What is Inference?
Inference in AI is the process of running a trained model to generate outputs -- such as predictions, text responses, image classifications, or recommendations -- from new input data. It is the production phase of AI where the model delivers value to end users, as opposed to the training phase where the model learns.
What Is Inference in AI?
Inference is the stage where an AI model actually does its job. After a model has been trained on data and learned its patterns, inference is the process of feeding it new input and getting a useful output. Every time you ask ChatGPT a question, use Google Translate, get a product recommendation on an e-commerce site, or have your email automatically classified as important, an AI model is performing inference.
Think of the difference between training and inference as the difference between education and employment. Training is the learning phase -- it happens once (or periodically) and is expensive. Inference is the working phase -- it happens continuously and is where the model creates business value.
Why Inference Matters for Business
While AI training gets most of the headlines, inference is where businesses actually spend their money and where AI delivers its value. Consider these realities:
Cost distribution: For most businesses using AI, 80-90 percent of their ongoing AI costs are inference costs, not training costs. Every API call to OpenAI, every chatbot interaction, every automated document analysis is an inference operation that costs money.
Performance = User Experience: The speed of inference directly affects how users experience your AI-powered features. A chatbot that takes 10 seconds to respond loses customers. Product recommendations that take too long to load reduce engagement. Inference speed is a critical factor in the quality of AI-powered experiences.
Scale challenges: A model that performs well for 10 users might struggle with 10,000 users. Inference infrastructure must scale with demand, and understanding inference helps you plan for growth.
Inference in Practice
When a business deploys an AI model, inference happens through one of several approaches:
Cloud API Inference The simplest approach. You send requests to a provider like OpenAI, Anthropic, or Google, and they handle all the infrastructure. You pay per token or per request. This is how most SMBs access AI today.
- Pros: No infrastructure to manage, instant scalability, access to the latest models
- Cons: Ongoing per-request costs, data leaves your network, dependent on provider availability
Self-Hosted Inference Running models on your own servers or cloud infrastructure. Requires more technical expertise but offers greater control.
- Pros: Data stays on your infrastructure, predictable costs at high volume, customizable
- Cons: Hardware investment, technical maintenance, you manage scaling
Edge Inference Running models directly on end-user devices (phones, laptops, IoT devices). Made possible by model optimization techniques like quantization.
- Pros: No internet required, zero latency, complete data privacy
- Cons: Limited model size, device hardware constraints, harder to update models
Optimizing Inference Costs
Since inference is the dominant ongoing cost of AI deployment, optimizing it is a key business concern:
Choosing the right model size: Using a 7-billion parameter model for a task that a 1-billion parameter model can handle well is wasting money. Match model size to task complexity.
Batching requests: Processing multiple inference requests together is more efficient than processing them one at a time. This is relevant for batch operations like nightly report generation or bulk document classification.
Caching: If the same or very similar queries are made repeatedly, storing and reusing previous inference results eliminates redundant computation. This is particularly effective for FAQ-style chatbots and recommendation systems.
Model optimization: Techniques like quantization and model distillation reduce the computational resources needed per inference call, directly lowering costs.
Inference Considerations for Southeast Asian Businesses
Latency and geography: If your AI provider's inference servers are in the United States but your users are in Southeast Asia, each request incurs network latency. Look for providers with inference endpoints in ASEAN regions (Singapore is a common hub) or consider cloud providers with regional data centers.
Cost predictability: For businesses scaling AI usage, the shift from development to production means inference costs can grow rapidly. A pilot chatbot handling 100 conversations per day might cost USD 50 per month, but scaling to 10,000 conversations per day pushes costs to USD 5,000 per month. Build cost models that account for scale.
Regulatory compliance: Some industries and jurisdictions in ASEAN require that data processing (including AI inference) happens within national borders. Understand where your inference is physically running and whether it complies with local data residency requirements.
Right-sizing your approach: Most SMBs should start with cloud API inference for simplicity and flexibility. Consider self-hosted inference only when you reach volumes where it becomes more cost-effective, typically above 100,000 inference calls per month, or when data privacy requirements mandate it.
Inference is the operational phase where AI delivers business value and where 80-90 percent of ongoing AI costs occur. Understanding inference helps business leaders manage AI budgets effectively, make informed decisions about AI infrastructure, and ensure that AI-powered features perform well enough to deliver positive user and customer experiences.
- Track inference costs from the start of deployment and project how they will scale with usage -- a chatbot handling 100 queries per day costs very differently from one handling 10,000 per day
- Choose AI providers with inference infrastructure in or near Southeast Asia (Singapore data centers are common) to minimize latency for your users and comply with regional data residency requirements
- Right-size your AI models for each task -- using the smallest model that delivers acceptable quality for each use case can reduce inference costs by 50-80 percent without noticeable quality loss
Frequently Asked Questions
What is the difference between training and inference?
Training is the learning phase where an AI model studies data and develops its capabilities. It happens once (or is periodically updated) and requires massive computing resources. Inference is the production phase where the trained model processes new inputs and generates outputs. It happens continuously whenever anyone uses the AI and constitutes the majority of ongoing costs. For most businesses, you never do training yourself -- you use pre-trained models and pay for inference through API calls.
How much does inference cost?
Costs vary widely depending on the model and provider. For text-based AI like GPT-4o, inference costs approximately USD 2.50-10 per million input tokens. A typical business chatbot handling 1,000 conversations per day might cost USD 100-500 per month in inference costs. Image generation typically costs USD 0.02-0.10 per image. For most SMBs, monthly inference costs range from USD 50 to USD 5,000 depending on volume and model choice. Starting with smaller models and scaling up only when needed is the most cost-effective approach.
More Questions
For most SMBs, cloud API inference is the right choice because it requires no infrastructure investment, scales automatically, and provides access to the latest models. Self-hosted inference becomes worth considering when you reach high volumes (typically 100,000+ queries per month) where the economics favor dedicated hardware, or when data privacy regulations require processing within your own infrastructure. Evaluate the total cost of ownership including hardware, electricity, maintenance, and technical staff before committing to self-hosted inference.
Need help implementing Inference?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how inference fits into your AI roadmap.