What is Inference?
Inference is the process of using a trained AI model to make predictions or decisions on new, unseen data in real time, representing the production phase where AI delivers actual business value by processing customer requests, analysing images, generating text, or making recommendations.
What Is Inference?
Inference is the phase where a trained AI model is put to work, processing new data and producing predictions, classifications, recommendations, or generated content. If training is like studying for an exam, inference is like taking the exam and applying what you learned to answer new questions.
Every time you ask ChatGPT a question, receive a product recommendation on Shopee, get a fraud alert from your bank, or use a language translation app, an AI model is performing inference. It is the moment when AI delivers tangible business value, transforming trained intelligence into actionable outputs.
Training vs. Inference
Understanding the distinction between training and inference is essential for managing AI costs and infrastructure:
Training:
- Happens periodically (daily, weekly, or monthly)
- Requires massive computational resources (many GPUs for hours or days)
- Processes historical data to learn patterns
- A high one-time or periodic cost
- Typically done in the cloud
Inference:
- Happens continuously, often millions of times per day
- Requires moderate computational resources per request
- Processes new data to make predictions
- An ongoing operational cost that scales with usage
- Can happen in the cloud, on edge devices, or on-premise
For most businesses, inference costs eventually exceed training costs because inference runs continuously while training happens periodically. This makes inference optimisation one of the most impactful areas for reducing AI operating expenses.
Types of Inference
Different business applications require different inference approaches:
- Real-time inference: Predictions are returned within milliseconds in response to individual requests. Used for chatbots, recommendation engines, fraud detection, and any customer-facing AI application where speed matters.
- Batch inference: Large volumes of data are processed at once, typically on a schedule. Used for overnight risk scoring, weekly demand forecasting, bulk document processing, and other non-time-sensitive workloads.
- Streaming inference: Data is processed continuously from a real-time data stream. Used for IoT sensor analysis, live video processing, and real-time monitoring systems.
Inference Infrastructure
The infrastructure choices for inference significantly impact cost, performance, and scalability:
GPU inference provides the highest throughput for complex models like large language models and computer vision systems. NVIDIA T4 and A10G GPUs are popular choices for inference workloads, offering a good balance of performance and cost. Cloud providers in ASEAN regions offer these through dedicated instances or serverless GPU services.
CPU inference is sufficient for many simpler models like decision trees, logistic regression, and small neural networks. CPU instances are significantly cheaper than GPU instances and more widely available. Many businesses over-invest in GPU infrastructure when their models would run effectively on CPUs.
Specialised hardware designed specifically for inference is becoming increasingly available. Google TPUs, AWS Inferentia chips, and Apple Neural Engine are optimised for inference workloads and can offer better price-performance than general-purpose GPUs for supported model types.
Edge inference runs models directly on local devices such as smartphones, cameras, or IoT hardware. This eliminates network latency and cloud costs but requires model optimisation to run on less powerful hardware.
Optimising Inference Costs
Inference optimisation is crucial for businesses running AI at scale. Several techniques can dramatically reduce costs:
- Model quantisation: Reducing the numerical precision of model weights from 32-bit to 16-bit or 8-bit. This can reduce inference costs by 50-75% with minimal accuracy impact.
- Model distillation: Training a smaller, faster model to mimic the behaviour of a larger model. The smaller model often retains 90-95% of the accuracy at a fraction of the cost.
- Batching: Grouping multiple inference requests together and processing them simultaneously. This improves GPU utilisation and throughput.
- Caching: Storing and reusing results for frequently seen inputs. If many customers ask similar questions to your chatbot, caching can reduce inference calls significantly.
- Auto-scaling: Dynamically adjusting the number of inference servers based on demand. Scale up during peak hours and scale down during quiet periods to avoid paying for idle resources.
- Right-sizing: Matching your hardware to your model's actual requirements. Many organisations use expensive GPU instances when a cheaper CPU instance would provide adequate performance.
Inference in Practice: ASEAN Examples
Practical inference deployments across Southeast Asia include:
- E-commerce: Shopee and Lazada use real-time inference for product recommendations, search ranking, and fraud detection across millions of daily transactions
- Financial services: Banks across the region use inference for real-time credit scoring, transaction fraud detection, and customer risk assessment
- Healthcare: Hospitals in Singapore and Thailand deploy AI inference for medical image analysis, assisting radiologists in detecting anomalies
- Customer service: Companies across ASEAN use language model inference to power multilingual chatbots that handle customer queries in Bahasa, Thai, Vietnamese, and other local languages
Getting Started with Inference
- Profile your model to understand its compute requirements. Measure latency, throughput, and resource usage before choosing infrastructure.
- Start with managed inference services from your cloud provider. AWS SageMaker Inference, Google Vertex AI Prediction, and Azure ML Endpoints handle infrastructure management.
- Optimise your model using quantisation and pruning before deploying. These techniques are often straightforward to apply and can halve your costs.
- Implement auto-scaling to match capacity to demand and avoid paying for idle resources.
- Monitor inference performance continuously, tracking latency, error rates, and cost per prediction.
Inference is where AI delivers its return on investment. Every dollar spent on data collection, model training, and infrastructure ultimately aims to produce inference, the moment when AI makes a prediction, recommendation, or decision that creates business value. For CEOs, understanding inference economics is critical because inference costs are the ongoing operational expense that determines whether your AI initiatives are financially sustainable at scale.
For CTOs, inference optimisation is one of the highest-leverage technical investments you can make. A 50% reduction in inference costs through techniques like quantisation or right-sizing hardware directly impacts the bottom line of every AI-powered service your company operates. As AI usage grows, these optimisations compound, potentially saving hundreds of thousands of dollars annually for businesses running AI at significant scale.
In Southeast Asian markets, where price sensitivity is high and competition is fierce, efficient inference gives companies the ability to offer AI-powered features that would be cost-prohibitive with unoptimised deployments. The difference between a chatbot that costs $0.10 per interaction and one that costs $0.01 per interaction determines whether AI-powered customer service is viable for mass-market businesses in the region. This cost efficiency can be a decisive competitive advantage.
- Distinguish between training and inference costs in your AI budget. Inference is an ongoing operational expense that scales with usage, while training is a periodic investment. Plan accordingly.
- Choose the right hardware for inference. Not every model needs a GPU. Profile your model performance on different hardware types and select the most cost-effective option that meets your latency requirements.
- Apply model optimisation techniques like quantisation and distillation before deploying. These techniques can reduce inference costs by 50-80% with minimal accuracy loss and are often straightforward to implement.
- Implement auto-scaling for inference infrastructure. Paying for peak capacity 24/7 when demand fluctuates is one of the most common sources of wasted AI spending.
- Monitor inference latency and throughput in production. Degraded performance directly impacts user experience and can indicate infrastructure issues or model problems.
- Consider batch inference for non-time-sensitive workloads. Processing data in batches is significantly more cost-effective than real-time inference and is appropriate for many business applications.
- Plan for inference scaling as AI adoption grows across your organisation. What starts as a single model serving a few requests can quickly grow to multiple models handling millions of daily predictions.
Frequently Asked Questions
How much does AI inference cost?
Inference costs vary enormously based on model complexity, hardware, and volume. A simple classification model on a CPU might cost $0.001 per prediction, while a large language model on a GPU could cost $0.01-0.10 per request. At scale, these costs add up quickly. A chatbot handling 100,000 daily conversations at $0.05 per interaction costs $5,000 per day. This is why inference optimisation techniques like quantisation, caching, and right-sizing hardware are so important for cost management.
What is the difference between real-time and batch inference?
Real-time inference processes individual requests as they arrive and returns results within milliseconds, suitable for customer-facing applications like chatbots and recommendation engines. Batch inference processes large datasets at once, typically on a schedule, and is used for non-time-sensitive tasks like overnight credit scoring or weekly reporting. Batch inference is significantly more cost-effective because it can fully utilise hardware resources and does not need to maintain always-on infrastructure. Most businesses use a combination of both approaches.
More Questions
Yes, inference can run on local hardware, edge devices, or on-premise servers. Many AI models can be optimised to run on smartphones, embedded devices, or standard office servers without any cloud dependency. This is common for applications requiring low latency, offline capability, or data privacy. However, cloud inference offers advantages in scalability, managed infrastructure, and access to specialised hardware like GPUs. The best approach depends on your specific requirements for latency, privacy, cost, and scale.
Need help implementing Inference?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how inference fits into your AI roadmap.