AI 101: What is Hybrid AI?

How AI will evolve in the near future is one of the biggest questions for developers, enterprises, everyday users, and governments alike. And what do they all really need from AI? High speed, strong efficiency, and robust security. Cost matters too. For example, AI-powered search can cost up to ten times more per query than traditional search. So how do we overcome these constraints on the path toward AI operating at peak efficiency?

Until recently, most AI workloads had to run in the cloud because end devices weren’t powerful enough. But inference happens far more often than training, and running it primarily in the cloud is becoming too expensive to scale.

Cloud-only AI systems struggle when decisions need to be made in milliseconds. Edge-only systems, meanwhile, lack the compute and storage required to train, update, and maintain complex models.

There are additional constraints. Sending large volumes of sensor or video data to the cloud creates bandwidth bottlenecks, while edge devices cannot realistically store or manage multiple model versions. Used in isolation, neither approach meets the requirements of modern enterprise AI systems.

So what’s the solution?

Recent computing history offers a clue. We moved from centralized mainframes to a hybrid model that combines cloud infrastructure with powerful personal devices. AI is following the same trajectory. To scale effectively, it needs a deliberate split of work between the cloud and the edge.

Microsoft has also argued that the future of AI is hybrid.

Hybrid AI is reorganizing computing around where it runs, not around a single “best” chip, and we stop thinking in the concept of monolithic models, reframing AI as multi-tier systems. Hybrid AI is the architectural split of intelligence across local devices and cloud infrastructure. And they can really work together.

Today, we’ll explore how Hybrid AI is built not only from a theoretical perspective, but also from the real-world practices of major companies: Microsoft, Google, Apple, and Samsung. Let’s see why hybrid AI can offer a better future for AI, making it cheaper, faster, and more efficient.

In today’s episode, we will cover:

• What is Hybrid AI in general?

• Three common Hybrid AI setups

• Application: how big players use Hybrid AI

  • Microsoft’s Hybrid AI strategy

  • Apple: on-device first, cloud as fallback

  • Google’s hybrid design across consumer products

  • Samsung and feature-driven hybrid

• Benefits of Hybrid AI

• Not without limitations – when Hybrid AI breaks

• Conclusion

• Sources and further reading

What is Hybrid AI in general?

Firstly, let’s cover some basics. Hybrid AI does not mean “hybrid models” (like symbolic + neural) or “hybrid architectures” in the classic AI sense. It is about where intelligence runs and how workloads are split across two compute layers:

• Local, on-device/edge AI (PCs, phones, other edge devices like cameras, sensors, machines, or vehicles) that is is about speed and privacy.

• Remote, cloud-scale AI (large models running in data centers on GPU infrastructure), whose main benefits are scalability and raw compute power.

Intentional task allocation is guided by latency, cost, privacy, and power constraints. But how should this work actually be split?

It depends on the task. Simple requests can run fully on the device. Harder ones can be shared between the device and the cloud. Tasks that need fresh or global information use the cloud. And in some cases, both run at the same time: the device handles a lighter version of the model, while the cloud runs a larger one and steps in if needed.

So Hybrid AI means a distributed execution model across edge devices and cloud.

Edge inference is commonly deployed on devices such as IoT sensors, edge gateways, industrial PCs and controllers, and embedded AI platforms (for example, NVIDIA Jetson, Google Coral, Intel Neural Compute Stick, and others). These systems may include dedicated hardware accelerators – GPUs, NPUs, TPUs, or СPUs, designed to efficiently execute matrix and tensor operations. Hardware selection for edge inference depends on the factors including power consumption, thermal limits, available memory, and the inference formats supported by the target platform.

So models typically require optimization prior to deployment with techniques, such as:

• Quantization – reducing numerical precision (like FP32 → INT8 or INT4, where supported) to decrease memory footprint and improve inference efficiency.

• Pruning – removing redundant or low-importance weights or structures.

• Knowledge distillation – training a smaller model to approximate the behavior of a larger teacher model.

These techniques can reduce model size, often by 50–90% in aggregate.

Raw sensor data is filtered and summarized on edge devices before being sent to the cloud. Here is what runs on device: noise filtering, event detection, aggregation (counts, averages, alerts). As a result, edge part of AI mostly stores recent data, temporary buffers, and cached models.

Meanwhile, the cloud stores full-precision models, model versions and metadata, performance benchmarks, long-term datasets, and runs hardware clusters (more often A100 and H100 GPUs, and TPUs), distributed training frameworks (training and fine-tuning), encouraging scheduling and autoscaling.

Overall, typical hybrid AI workflow looks like this:

• Data or aggregated results are collected from edge devices.

• Models are trained or retrained in the cloud.

• Updated model versions are pushed back to edge devices.

But that’s not all. Here are more concrete scenarios showing how everything can be coordinated between the two spaces.

Three common hybrid AI setups