# What is AI inference? AI inference means running a trained model on new data to make predictions or decisions. While training uses historical data to build a model, inference is about putting it to work in real-world scenarios. ### Key Takeaways - AI inference applies a trained model to new data to make predictions or decisions. - By location, inference can be cloud-based, edge or on-premises. - By processing type, inference can be batch, online or streaming. - Machine learning covers training the model, while inference is about using the trained model. - Benefits include real-time processing, scalability and automation; drawbacks include hardware requirements, latency and data privacy concerns. ## What is AI inference? AI inference uses a trained [AI](https://www.ionos.de/digitalguide/online-marketing/verkaufen-im-internet/was-ist-kuenstliche-intelligenz/) model to analyse new input – like an image, sentence or sensor data – and return a prediction, classification or suggested action. Unlike training, it doesn’t adjust **any model parameters** – the model or [neural network](https://www.ionos.de/digitalguide/online-marketing/suchmaschinenmarketing/was-ist-ein-neural-network/) simply runs. This allows learned patterns to be applied quickly to real-world situations. Inference can be used with many data types, including images, text, speech and sensor readings. How well inference performs depends on how well the model was trained and on whether it can generalise to new data. AI inference can run on different types of hardware, such as [CPUs](https://www.ionos.de/digitalguide/server/knowhow/cpu/), GPUs, dedicated AI chips or in the [cloud](https://www.ionos.com/en-ie/digitalguide/server/know-how/what-is-the-cloud/). Many uses of AI rely on fast, efficient inference. These include voice assistants, recommendation systems and real-time image recognition. In cases where results are needed immediately, inference speed often determines whether a system is viable or not. ## What types of AI inference are there? AI inference can be classified in two ways: by where it runs, and by how it processes data. ### Location-based inference #### Cloud-based inference In cloud-based inference, the model runs on **remote servers**. Users send their data to the cloud, where the model processes it and returns the results. This setup doesn’t require managing your own hardware, allowing you to scale easily. It also supports large models that might not be able to run locally. The downsides are [latency](https://www.ionos.de/digitalguide/server/knowhow/was-ist-latenz/) caused by data transfer and possible privacy concerns. Many cloud providers offer specialised inference services that include GPU acceleration. Cloud inference is a good fit for those needing AI output occasionally (i.e. not continuously). #### Edge inference Edge inference runs on local hardware, like smartphones, [IoT](https://www.ionos.de/digitalguide/websites/online-recht/internet-of-things/) sensors or industrial systems. Since the data **stays on the device**, there’s minimal latency and better data privacy. However, edge devices typically have limited processing power, so models must be optimised, compressed or reduced in size. This means edge inference works well for tasks that require immediate responses, such as autonomous vehicles or factory systems that rely on sensors. It also works reliably without a stable internet connection. #### On-premises inference [On-premises](https://www.ionos.de/digitalguide/server/knowhow/was-ist-on-premises/) inference runs on **your own servers**. This gives you full control over both data and infrastructure, which is essential when working with sensitive or confidential information. It also lets you run large models without relying on the cloud. The trade-off is higher cost as you need to invest in hardware and maintain it yourself. This makes on-premises inference ideal for those with strict security requirements or who work with large volumes of data. ### Processing-based inference #### Batch inference Batch inference processes data in groups at scheduled intervals – hourly, daily or to a fixed timetable. Instead of handling each request immediately, the model analyses larger batches of data all at once. Batch inference is a good fit when you don’t need real-time results, like when generating reports or analysing historical trends. Because of that, batch inference is also known as offline or static inference. #### Online inference Online inference delivers results in real time and is used when fast responses are essential. To meet these demands, online inference typically requires high-performance hardware and software tuned for low latency. #### Streaming inference Streaming inference continuously processes incoming data – making predictions, detecting changes or adjusting internal states as the data comes in. It’s typically used in scenarios that require constant monitoring, such as predictive maintenance, anomaly detection or real-time operations management. ## What’s the difference between AI inference and machine learning? [Machine learning](https://www.ionos.de/digitalguide/online-marketing/web-analyse/was-ist-machine-learning-so-lernen-maschinen-denken/) is how models learn from data. This includes collecting data, cleaning it, designing features and training the model. Inference comes after training. It uses the **trained model** to make predictions on new data. Training takes time and resources. Inference is fast and repeatable. The hardware needs are different, too. Training often relies on GPUs or specialised chips. Inference can also run on less powerful devices once the model is ready. The focus is also different. Training focuses on building a good model. Inference focuses on using it efficiently. But the two are closely connected. Without training, there’s no inference. Without inference, the model’s output can’t be used. ## What are the advantages and disadvantages of AI inference? AI inference brings major benefits, but it also comes with technical challenges. An AI solution’s speed, efficiency and real-time performance often depend on how well thought out the inference strategy is. ### Advantages of AI inference AI inference is used in **real-time situations** where fast decisions matter, like in autonomous systems. The same model can be reused for different tasks or datasets, making it both flexible and scalable. Running inference on edge or on-premises servers keeps sensitive data local and protects it from third-party access. Cloud-based setups offer flexibility in terms of computing power, without having to invest in your own hardware. Trained models can also automate tasks like image or text analysis. And compared to manual work, inference saves both time and money. ### Disadvantages of AI inference Large models often need **specialised hardware**, especially for real-time use. Cloud-based inference can introduce latency, which becomes a problem when fast reactions are critical. Running inference on edge devices is also more complex as models need to be compressed or quantised to fit limited hardware, which can affect accuracy. Cloud setups also raise concerns about data privacy and security since data is transferred to external servers. Some models lack transparency, which makes it harder to understand or explain their decisions. And setting up a reliable inference pipeline takes time, technical expertise and the right infrastructure. ### Overview of the advantages and disadvantages of AI inference
Advantages Disadvantages
Makes decisions in real time Speed depends on hardware
Can be reused multiple times Needs optimising for edge devices
Processes data locally Transferring cloud data can risk data privacy
Reduces infrastructure costs by using the cloud On-premises setups are expensive
Automates processes efficiently
## Where is AI inference used? AI inference is used across a wide range of industries. In healthcare, it helps analyse images in real time to support diagnosis. In manufacturing, it monitors sensors and automatically detects anomalies. Autonomous vehicles also use inference to assess their surroundings and make split-second decisions. Financial services rely on inference for fraud detection and risk assessment. Inference also powers recommendation systems that generate personalised suggestions in real time. And many large language models ([LLMs](https://www.ionos.de/digitalguide/server/knowhow/large-language-model/)) use AI inference to power text generation, chatbots and automated translations. In short, AI inference puts trained models to work wherever fast and accurate decisions are needed. This is a markdown version of: [https://www.ionos.com/en-ie/digitalguide/server/know-how/ai-inference/](https://www.ionos.com/en-ie/digitalguide/server/know-how/ai-inference/) for AI/LLM consumption.