Edge AI vs. Cloud AI in 2025: Where Should You Run Your Workloads?

Introduction

Artificial Intelligence (AI) has evolved from being an esoteric function to becoming a foundational component of modern business. In 2025, the biggest question will not be whether to incorporate AI into your business; the question will be which computing model to run your AI workloads in: on the edge or in the cloud. This decision will affect your performance, cost, security, and scalability outcomes.

According to IDC, the world will create 180 zettabytes of data in 2025, with an estimated 60% created outside of traditional data centres. The data explosion is leading to the adoption and increase of Edge AI to perform real-time tasks and work with sensitive data, while Cloud AI will continue to play a crucial role in training large-scale AI models and analysing data at scale beyond what any edge device can handle.

That said, compliance is looming. Legislation such as the EU AI Act, or new interpretations of GDPR, require organisations to consider how they will handle their data in terms of AI workloads, which in some cases is taking them closer to the creation point or source of the data.

In this blog, we will explore the primary differences between Edge AI and Cloud AI, identify key trends for 2025, and provide information and guidance on how to determine the right deployment model for your AI workloads. You will also gain access to real-world examples, updated research and data, insights, and next steps to expedite your decision-making.

What is Edge AI?

Edge AI processes AI models directly on local devices (IoT sensors, smartphones, drones, industrial equipment, etc.). Edge AI enables data to be analysed at the location where it was created, allowing decisions to be made in real time without waiting for data to be sent to the cloud for processing.

This ability is critical for applications such as autonomous driving, where milliseconds of delay are unacceptable. This capability is also important from a data privacy standpoint, since sensitive information can stay on the actual device, reducing the risk of breaches.

Real-World Examples:

• Autonomous Vehicles: Tesla’s cars process sensor data locally for instant driving decisions.
• Smart Retail: Amazon Go stores use on-site AI for cashierless checkout, minimising cloud dependency.
• Healthcare Devices: Portable ultrasound machines, like Butterfly iQ+, analyse scans directly on the device, making diagnostics accessible in remote areas.

Edge AI is ideal for scenarios where low latency, autonomy, and data privacy are crucial.

What is Cloud AI?

Cloud AI is based in centrally-located data centres run by a provider like AWS, Microsoft Azure, or Google Cloud. It typically runs heavy workloads for things like training large AI models, big data analytics, and managing enterprise AI pipelines.

Cloud AI provides unmatched scalability. For example, training a generative AI model like GPT-5 requires thousands of GPUs running parallel. There is no way that an on-premises data centre could ever support that level of access. Additionally, Cloud platforms offer pre-trained AI services, APIs, and AutoML pipelines for companies that do not have AI expertise at their disposal.

However, Cloud AI comes with trade-offs:

• Latency: Transmitting data to and from the cloud introduces delays.
• Privacy Risks: Sensitive data may be exposed during transmission or storage.
• Cost: Long-term cloud usage for inference can become expensive compared to localised processing.

Real-World Examples:

• Generative AI Training: OpenAI and Google DeepMind train large models in cloud supercomputers with thousands of GPU clusters.
• Financial Fraud Detection: Banks process billions of transactions in the cloud to detect fraud patterns globally.

According to Statista, the cloud AI market will exceed $300 billion in 2025, fueled by demand for scalable AI infrastructure and AI-as-a-Service solutions.

2025 Trends Driving AI Workload Decisions

As AI continues to reshape industries, deciding where to run workloads—on the edge or in the cloud—has become a critical strategic decision in 2025. Several market shifts are influencing this choice.

The Push for Real-Time AI

In sectors such as automotive, manufacturing, and healthcare, real-time responsiveness is now a baseline requirement. AI systems must be able to act instantly on live data, and edge processing is becoming the preferred option for such latency-sensitive applications. As Gartner has noted, by the end of 2025, 70% of AI-driven decisions will be made at the edge, indicating the demand for zero-latency processing that is only increasing.

Data Privacy and Compliance Pressures

Meanwhile, data protection regulations are proliferating globally. The EU AI Act, for example, along with a newer wave of data localisation regulations in countries such as India and Brazil, is forcing organisations to rethink their data processing approaches. In many cases, Edge AI systems process data on local devices, enabling businesses to remain compliant and earn customer trust. For industries receiving sensitive personal data, this is a matter of necessity, not option.

The Growth of AI Model Complexity

Large AI models, particularly in generative AI, are growing in both size and sophistication. Training these models requires considerable computational power that only cloud hyperscalers can provide. For example, GPT-5 and other next-generation models use trillions of parameters, which push training costs significantly above $20 million per model. While cloud infrastructure remains desirable for training, inference (i.e. running pre-trained models) is moving to edge devices for operational efficiencies.

Network Constraints and Connectivity Gaps

Although the rollout of 5G networks and early 6G is improving connectivity, global connectivity remains patchy. In need of a reliable offline AI processing platform, not to mention reliance on AI in defence, logistics, and remote health care applications, Edge AI will remain crucial regardless of improvements to power and coverage.

Balancing Costs

Cloud AI is often the most expensive AI deployment option. Companies can save operational costs and reduce cloud billing by shifting on-device cloud inference workloads to the edge, which reduces recurring cloud compute and network usage costs over time. Continuous data streaming, model inference in the cloud, and various workloads on GPU-fed clouds can result in embarrassing operational costs.

Comparing Edge AI and Cloud AI in 2025

The following table summarises the key differences between Edge AI and Cloud AI in 2025, helping clarify which solution best fits specific business needs:

Real-Time Applications and Industry Examples

AI is no longer confined to research labs—it’s actively transforming industries across sectors. Both Edge AI and Cloud AI play crucial roles, often complementing each other in hybrid systems.

Edge AI Use Cases in 2025

Edge AI wins out in latency-sensitive, privacy-sensitive, and bandwidth-sensitive contexts. The following are examples of real-world applications:

• Healthcare: Portable diagnostic instruments like the Butterfly iQ+ empower clinicians and treat patients in real-time by providing ultrasound imaging with AI-assisted interpretive analysis on-device, which is increasingly important for service delivery in remote, rural or underserved areas in countries globally.
• Retail: Platforms like Amazon Go and other cashier-less retail use edge AI to track movement over time, manage stock in real-time, and ensure frictionless experiences under time constraints rather than dealing with cloud-based limitations.
• Smart Cities: Urban centres, such as Singapore and Tokyo, leverage edge AI-based traffic cameras and environmental sensors to obtain real-time readings of traffic flow and air quality conditions, which updates their real-time monitoring system autonomously, right on the edge.

Cloud AI Use Cases in 2025

Cloud AI powers workloads that require massive datasets, complex models, and global collaboration:

• Generative AI & LLMs: Companies like OpenAI, Google, and Anthropic are using cloud supercomputing infrastructure to experiment with the training of large-scale models like GPT-5 and Gemini Ultra. This would not be feasible on edge devices.
• Financial Services: Banks are using cloud AI for fraud detection systems. These systems can analyse billions of transactions globally in real-time across geographies.
• Enterprise SaaS: CRM systems that have features like Salesforce Einstein AI can derive predictive analytics and customer insight features that reside in the cloud and integrate with multiple touch points.

Security and Privacy Considerations

In 2025, AI security and data privacy have become central to business risk management. The increasing volume of sensitive data processed by AI systems—ranging from personal health information to financial transactions—has made AI workload placement a critical part of cybersecurity strategy.

Security in Edge AI

Edge AI is hugely favourable to privacy, simply because it keeps data local. The processing happens on the device itself, minimising the need to transmit sensitive data over a network or to a centralised data centre. Keeping the data local to the device reduces the size of the attack surface and decreases the risk associated with outside threats of data breaches or interception.

However, Edge AI has risks. There’s a question of physical security, as the edge devices often exist outside of the monitoring of a sensor network, and therefore are much more susceptible to tampering, theft, or interaction. Managing firmware equivalent updates or model updates, across potentially thousands of different devices that are geographically distributed, comes with dependencies that generate possible risks if done improperly.

Security in Cloud AI

Most cloud AI platforms come with enterprise-grade security systems that include encryption-at-rest, data masking, intrusion detection, and compliance certifications like ISO/IEC 27001, SOC 2, and HIPAA. Cloud providers make substantial investments in cybersecurity protections, including AI-enabled threat detection, to protect their proprietary infrastructure.

Of course, storing data centrally presents increased risks. If the cloud system is breached, the extent of damages could be substantial. After all, sending your data to the cloud also poses security risks while in transit, even with encryption.

Cost and Infrastructure Analysis

Understanding the true cost of AI deployment requires looking beyond just cloud subscriptions or hardware purchases. Organisations must consider long-term operational expenses, scalability needs, and the total cost of ownership.

Cost of Edge AI

Edge AI typically requires a larger initial investment to acquire speciality hardware like AI-enabled sensors, GPUs, or embedded chips. Over time, it lowers operational overhead by reducing cloud consumption, data transfer costs, and reliance on constant connectivity.

For example, a smart manufacturing operation that deploys 1,000 edge AI units for predictive maintenance may achieve up to 40% lower annual costs for bandwidth and cloud compute versus streaming collected sensor data to the cloud for processing.

Cost of Cloud AI

Cloud-based AI operates on a pay-as-you-go basis, so entry is cheaper at first because the enterprise does not buy hardware or manage infrastructure. However, as AI scales—especially workloads that are inference-heavy—the costs to leverage cloud computing can increase dramatically.

Flexera's 2025 Cloud Cost Report revealed that 53% of enterprises overspending on their AI cloud budgets are spending most of their money on GPU compute costs, storage, and outgoing data transfer. Training large models has a tendency to be expensive overall; however, inference workloads can also become expensive depending on the time that the workload spends connected to the cloud.

AI Model Development: Training vs. Inference in 2025

In AI deployment, understanding the distinction between model training and inference is essential. Both stages have different infrastructure needs and are often the deciding factors in whether workloads should run in the cloud or at the edge.

Cloud AI for Model Training

Teaching AI models requires teaching algorithms to attribute patterns among the data in mass datasets. This is a computationally intensive and repeatable operation, and we'll need to use multiple, specialised pieces of hardware to run many parallel processes for large amounts of time. In 2025, AI models, notably the ones related directly to their specific fields of practice—natural language processing, computer vision, and generative AI—are larger and greater than ever.

For example, it is normal to think of large language models, running billions or trillions of parameters, e.g., GPT-5 or Gemini Ultra. Training these models requires access to petascale GPU clusters or dedicated AI hardware like:

• NVIDIA H100 Tensor Core GPUs
• Google TPU v5e Pods
• AWS Trainium chips

This kind of computational requirement will not be possible at the edge. Only cloud platforms allow for adequate scalability, as well as distributed compute environments, and storage necessary for model training. Cloud AI services also provide global collaborative environments for global teams, which facilitates shared iteration and changing of models in the same environment.

Edge AI for Model Inference

After a model is trained, it proceeds to the next phase, inference. Inference is the process of using the model on new, real-world data when predicting or making decisions. By 2025, inference is increasingly likely to live on Edge AI devices, given the need for having faster response times, lower bandwidth consumption, and more privacy.

Edge inference allows an AI system to make real-time decisions without having to communicate back-and-forth to the cloud. For example:

• Autonomous vehicles, equipped with onboard AI chips, monitor sensor data telemetries to simultaneously make a fraction-of-a-second driving decisions - while never being reliant on the internet.
• Retail security systems with edge-based computer vision can detect (and report) suspicious behaviour while minimising the need to stream constant video feeds to a centralised server.

Embracing a Hybrid AI Strategy

The cloud is still critical for training large resource-intensive models, whilst the edge is now the standard for executing AI inference at scale in real-time situations. Most companies in 2025 no longer make the choice between edge and cloud: they use both in hybrid AI architecture.

This hybrid model allows an organisation to:

• Flexibility: Train once in the cloud and deploy many times at the edge
• Efficiency: Reducing the cost of using a cloud service by running the real-time inference operations using local device resources
• Compliance: Retaining greater control over data privacy, processed locally when possible

For example, a healthcare firm might train a diagnostic AI model against anonymised patient data on a cloud supercomputer, but deploy its inference engine to an edge-based medical device in a hospital to conduct real-time analysis of a patient's vitals without exposing their information outside the device.

The Regulatory Landscape in 2025

In 2025, AI regulations and compliance mandates are among the most significant factors influencing where workloads are deployed. Global governments and regulatory bodies are actively shaping how AI is developed, deployed, and monitored.

Expansion of AI-Specific Laws

The EU AI Act, officially activated in early 2025, will impact AI governance globally. It creates three risk tiers—unacceptable, high-risk, and limited-risk—with corresponding compliance obligations across sectors. High-risk AI applications, such as facial recognition, healthcare/image diagnostics demands, require stringent data governance, auditability, and even real-time monitoring, which makes Edge AI appealing as an on-premise compliance vehicle.

Other jurisdictions are getting in on the action:

• India's Digital Personal Data Protection Act requires data localisation for personal data, effectively requiring organisations to shift to the local edge via Edge AI.
• Brazil's LGPD updated clauses for 2025 that included AI transparency mandates, but companies must explain algorithmic decision-making—more easily proposed in localised edge contexts.

Cross-Border Data Restrictions

Cloud AI's challenges are multiplying given cross-border data transfer limitations. Countries are imposing data residency requirements, limiting the organisation's ability to centralise sensitive data in global cloud environments. For example, financial services firms operating across Europe and Asia are often required to process transactional AI workloads in their respective jurisdictions to comply with regional data protection laws.

Regulatory Compliance Drives Deployment Choices

In regulated sectors such as healthcare, banking, and defence, organisations are favouring Edge AI for real-time decision-making on sensitive data, while using Cloud AI for non-sensitive analytics, R&D, and model development.

Future Outlook: The Convergence of Edge and Cloud AI

When referencing the future of AI to come, the question will no longer be about cloud or edge deployment, but how to build systems in which both cloud and edge will work together in a seamless way. According to analysts, by 2027, over 80% of enterprise AI workloads will exist in some hybrid form that combines centralised cloud training and distributed edge inference.

Key Emerging Trends:

• Federated Learning: Instead of transferring private data to the cloud for AI model training, model updates are shared in the cloud for aggregation while the model is trained locally on multiple edge devices. This provides data privacy and allows for collaborative training.
• Edge-Cloud Collaboration Platforms: Cloud providers like AWS, Azure, and Google Cloud are building AI orchestration tools that manage workloads on both the cloud and edge automatically.
• Smarter Edge Devices: Edge processors with AI accelerators are getting smarter, enabling them to now run complex models previously restricted to GPU execution in the cloud.

This convergence alleviates the previously standard trade-offs between performance, security and scalability, resulting in a better overall approach to AI deployments.

Conclusion

By 2025, the discussion of Edge AI vs. Cloud AI is not about which one will replace the other. It's about orchestration and strategy - when, where, and how to utilise each to optimise business outcome. AI has evolved significantly from proof of concept experiments to an essential business capability with real applications ranging from autonomous vehicles to predictive health management, real-time manufacturing automation to sophisticated generative use cases.

Edge AI is an advantage for enterprises because it allows for real-time local decision making, saving bandwidth, and protecting sensitive data according to data privacy regulations. It reduces latency, keeps sensitive data safe, and provides resiliency in environments with poor or inconsistent connectivity. For sectors like manufacturing, retail, defence, or healthcare, it is an operational necessity, not merely a nice-to-have feature.

Conversely, Cloud AI is crucial for its unparalleled scalability, compute power, and the ability to collaborate. Cloud AI allows organisations to construct, train and iteratively build large-scale AI models on large datasets with teams anywhere in the world, all over the cloud. The cloud also provides long-term data storage, central monitoring, and enterprise analytics that require predictable availability of large compute resources.

Leading edge organisations are adopting a hybrid AI model involving the real-time and on-site features of the edge with the global capacity and reach of the cloud. The hybrid model not only delivers better performance, but it enables organisations to lessen risks related to data governance, cost, and growing regulatory environments.At HashRoot, we help businesses navigate this complex decision-making process by offering end-to-end AI consulting, hybrid deployment architectures, and secure model development services tailored to your industry. Whether you're building AI at the edge, in the cloud, or across both, our team ensures your infrastructure is optimised for performance, compliance, and cost-efficiency.