9 min read
Machine learning is no longer just a cloud thing. Devices from phones to laptops now include chips and runtimes designed to run models locally. That means developers face a real choice. Do you run inference on the device or in the cloud?
Both approaches are valid. Both have trade offs. The right choice depends on the product goals, cost targets, privacy needs, latency requirements, and development resources.
This guide explains how on-device AI works, why you might pick it, when cloud AI is better, and how to design a hybrid system that uses the best of both worlds. I start with on-device AI because that is the area where many teams have the weakest intuition and the biggest opportunity.
On-device AI means running model inference, and sometimes light training, entirely on the user device. The model binary, the runtime, and inputs are all local. No user data needs to leave the device for the inference step unless the app explicitly sends it to a server.
Major platforms and vendors now provide on-device model runtimes and tooling. Apple offers Core ML and a set of device-optimized foundation models under Apple Intelligence for iOS and macOS developers. The Apple developer pages and WWDC material describe workflows for converting and optimizing models to run efficiently on Apple silicon.
Google and the Android ecosystem provide a family of runtimes, including LiteRT and TensorFlow Lite, to run optimized models on smartphones and other edge devices. These runtimes and conversion tools let teams convert TensorFlow, PyTorch and JAX artifacts into formats that run well on mobile processors.
Qualcomm and other silicon vendors have also published guidance and examples that show the latency, privacy, and power advantages of moving some inference workloads to device.
Here are the main reasons teams choose on-device inference.
On-device inference avoids network round trip time. For user interactions that require instant feedback like real time audio processing, camera effects, augmented reality overlays, or UI suggestions, the difference is immediate. Users notice and prefer interfaces that respond without a network delay.
When inference happens locally, user inputs do not need to be uploaded for processing. That improves privacy by default. Platforms and vendors are investing in private computation patterns that favor on-device processing to meet regulatory and user expectations. Apple in particular has emphasized personal intelligence and privacy as a key design principle.
If your app performs millions of inferences per day, cloud costs can become the dominant line item. On-device inference shifts compute cost to the device. That can drastically reduce recurring server bills once the development and optimization cost is paid.
On-device models let apps function when users are offline or have poor connectivity. That expands the contexts where the feature is useful and improves reliability for users on slow or intermittent networks.
On-device AI is not a silver bullet. Here are the key constraints.
Device memory and compute budgets are limited compared to cloud clusters. Even with efficient runtimes and quantization, on-device models must be smaller or heavily optimized to run within memory and thermal constraints. This limits you from running very large foundation models locally on most consumer devices.
Mobile and desktop hardware vary widely. Optimizing a model for Apple silicon and for a range of Android devices can require multiple conversion and tuning steps. Frameworks exist to help, but fragmentation raises testing overhead and maintenance costs.
Running heavy inference will consume battery and generate heat. That matters for mobile apps where user tolerance for battery drain is low. Engineers must measure and tune for power efficiency.
If you want to update the model often, deploying new model weights usually requires an app update or a model download step. That adds complexity compared to central cloud deployments where the server side can be changed instantly.
If you are building for on-device inference, these are the practical tools and steps teams use.
Common workflows use model conversion tools to convert PyTorch or TensorFlow checkpoints to platform formats such as Core ML, TFLite, or ONNX. Vendors provide guidelines for quantization, pruning, and model stitching to reduce size and run time. Apple and Google both provide conversion and optimization docs.
Quantization reduces model size and speeds inference by using lower precision arithmetic such as 8 bit or 4 bit, often with minimal accuracy loss. Modern quantization strategies let some large models approach cloud-level accuracy at a fraction of the memory cost. Recent evaluations show that 4 bit and 8 bit quantized models can keep most task accuracy while using considerably less memory.
Pick the runtime that fits your platform. Core ML remains the primary choice for Apple devices. TensorFlow Lite or LiteRT, PyTorch Mobile, and ONNX runtimes are the typical options for Android and cross platform use. Vendor SDKs can accelerate inference on specialized NPUs or GPUs.
Some frameworks allow light on-device training or fine tuning for personalization. TensorFlow Lite supports on-device training scenarios. On-device personalization can improve model accuracy for individual users without sending personal data to servers.
On-device inference is ideal for these scenarios.
These are the features that get immediate user love because they are fast and private.
Cloud AI remains essential when a problem requires more compute, larger models, or fast iteration.
Cloud infrastructure lets you run models that are orders of magnitude larger than what devices can support. If your feature needs a large foundation model or very large context windows, the cloud is usually the practical option.
Deploying model updates and running A B tests is easier in the cloud. You can iterate rapidly without pushing new app versions.
Tasks that require a shared knowledge base, global search over documents, or centralized retrieval augmented generation setups benefit from cloud deployment. RAG systems use vector stores and server side compute to combine retrieval and generation at scale. Research shows RAG pipelines involve trade offs in latency and storage, and are often more efficient when centralized, or when hybrid architectures are used.
Cloud compute gives you elasticity for spikes and complex batching strategies for cost control. For very bursty workloads, cloud scaling may be cheaper than pushing more work to devices.
Most real products use hybrid designs that place a small model on device and route heavier tasks to the cloud when needed.
Distributed RAG and edge-cloud research show that keeping a small knowledge cache on the device and using the cloud only for heavy retrieval or synthesis reduces latency and data exposure while retaining centralized recall when necessary.
Making the right architecture decision is rarely only about tech. Here are the business and legal considerations.
Estimate both one time costs and recurring costs. On-device development usually requires an upfront engineering investment to optimize and test models for many hardware targets. Cloud inference places costs in the recurring operational budget. For high volume inference, on-device processing can cut cloud bills significantly after the development investment is amortized. A recent cost comparison review finds on-device options reduce per-inference cost but require higher initial optimization work.
If you process personal data, local inference reduces the amount of data that leaves the user device. That lowers your exposure under privacy laws and can simplify compliance. However, compliance requirements depend on the jurisdiction and the specific data types you handle. When legal risk is high, prefer local processing or design the system so sensitive data never leaves the device.
Cloud models are easier to monitor. If you need rich telemetry, drift detection, or centralized logging for model behavior, cloud solutions simplify observability. For on-device models, consider safe telemetry patterns that respect privacy, such as differential reporting or opt in collection.
Use this checklist to decide which path to pick.
Choose on-device if most of these are true:
Choose cloud if most of these are true:
Consider hybrid if:
If you decide to move features on device, follow these steps.
Apple and Google have publicly invested in on-device tooling and model runtimes. Apple announced Apple Intelligence and improvements to Core ML during WWDC and offers conversion and stitching tools that target Apple silicon performance improvements.
Google continues to invest in LiteRT and TensorFlow Lite to support efficient edge inference and has developer guides for converting models for mobile runtime.
Quantization and efficient numeric formats are improving fast. Recent evaluations show stable accuracy for many workloads even with 4 bit and 8 bit quantization, which makes running larger models closer to device viable than a few years ago.
On-device AI gives you speed, privacy, and offline reliability. Cloud AI gives you scale, large model capability, and fast iteration. Most production systems will need both.
If you are building a product where latency, privacy, or offline use matter, start by prototyping a small on-device model. Use cloud fallback for heavy operations. If your primary need is large context understanding, centralized knowledge, or rapid iteration, cloud is the faster path to shipping.
Make the call on a per feature basis. Start small, measure everything, and be ready to move components between device and cloud as usage patterns and technology change.
For more on cutting-edge cloud AI, check out Google’s Genie 3 overview here.