Edge AI for business, explained simply: it is the ability to run artificial intelligence workloads directly on a device at the point where data is generated; on a factory floor, inside a vehicle, at a retail checkout, or on a piece of infrastructure equipment - rather than sending that data to a remote cloud server for processing. In 2026, edge AI has moved from being a competitive advantage for early adopters to a practical operational tool that B2B companies across manufacturing, logistics, healthcare, retail, and infrastructure are actively deploying.

This guide explains what edge AI is, why it behaves differently from cloud-based AI, what is driving adoption right now, and what the technology actually looks like when it is implemented in a business environment.

Summary

Edge AI is AI inference running locally on hardware at or near the data source, rather than in a centralised cloud. It matters for business because it eliminates the latency, bandwidth cost, and data privacy exposure that come with sending raw data offsite for processing. The result is faster decisions, lower connectivity costs, improved data security, and AI capability that works even when network connectivity is unavailable or intermittent. Adoption is accelerating because the hardware to do this has become compact, power-efficient, and cost-effective enough to deploy at scale outside of controlled data centre environments.

What is edge AI?

To understand edge AI, it helps to separate two concepts that are often used interchangeably: artificial intelligence and inference.

Training is the computationally intensive process of building an AI model - feeding it large datasets and adjusting millions of parameters until it can reliably identify patterns, classify inputs, or make predictions. Training typically happens in the cloud or a data centre, on large clusters of GPUs. It is expensive, slow, and power-hungry.

Inference is the process of using a trained model to make a decision or prediction on new data. Inference is what actually happens in deployment: the model sees a camera frame and identifies a defect; it reads a sensor signal and predicts bearing wear; it analyses a transaction and flags anomalous behaviour. Inference is far less computationally demanding than training, and it is inference that edge AI hardware is designed to run - quickly, efficiently, and locally.

Edge AI, therefore, is inference at the edge: a trained model, compressed and optimised for efficient execution, running on a device deployed in the field. The model might have been trained in the cloud; the decisions it makes happen locally, in real time, without a round trip to any external server.

Edge AI vs Cloud AI: What is the difference?

Understanding the distinction helps clarify why businesses are choosing to deploy AI at the edge rather than, or alongside, the cloud.

Neither model is inherently superior. Many B2B deployments use both: edge AI handles real-time, latency-sensitive decisions locally, while aggregated insights and model updates flow to and from the cloud on a lower-frequency basis. The edge and cloud complement each other rather than compete.

How edge AI works in practice

An edge AI deployment typically involves three components: a sensor or data source (a camera, a machine vibration sensor, a barcode scanner, a microphone), an edge AI device running inference, and some form of output - an alert, a control signal, a log entry, or a dashboard update.

A practical example: A food packaging line uses a camera to inspect each item before it is sealed. Historically, that inspection was done by a human or by a rules-based machine vision system. With edge AI, a trained neural network runs directly on an edge device mounted on the production line. It analyses each frame in real time, identifies defects, and triggers a reject mechanism in under a millisecond - far faster than a cloud round trip, and without sending a continuous HD video feed offsite. The model was trained on thousands of images of acceptable and defective product; the inference that happens on the line costs a fraction of a watt of additional compute.

The edge AI device in this scenario might be a compact embedded computer with a dedicated neural processing unit (NPU) - a specialised processor optimised for the matrix arithmetic that underpins neural network inference. NPU performance is measured in TOPS (Tera Operations Per Second), a metric that has become the standard shorthand for comparing edge AI compute capacity across hardware platforms.

What is driving B2B adoption in 2026?

Several converging factors have accelerated the business case for edge AI beyond where it stood even two or three years ago.

Hardware has become fit for industrial deployment

The NPUs that make edge AI inference practical have migrated from smartphone SoCs into purpose-built industrial platforms. Qualcomm's Dragonwing IQ series, for example, extends the company's AI processing architecture - the same Hexagon NPU found in Snapdragon-powered devices - into industrial-grade SoCs rated for extended temperature ranges and long product lifecycles. The IQ-X Series targets Windows-native industrial PCs and HMIs, with a Hexagon NPU rated up to 45 TOPS alongside Oryon CPU cores, designed for factory automation and edge AI vision applications. The IQ6, IQ8, and IQ9 series target Linux-based embedded and IoT deployments across a range of performance and power envelopes.

At the system level, edge AI stations such as the Thundercomm TurboX EB5G2 (48 TOPS, powered by the Qualcomm QCS8550 on a 4nm process) and the TurboX EB6S (15 TOPS base SoC, expandable to 70 or 200 TOPS via AI accelerator card) package this capability into fanless industrial computers with 5G connectivity, multiple camera inputs, and industrial interfaces - ready to deploy in a factory, a retail unit, or a vehicle, rather than in a rack in a climate-controlled server room.

Model compression has made inference tractable on compact hardware

Techniques such as quantisation (reducing the numerical precision of model weights from 32-bit to 8-bit or lower), pruning, and knowledge distillation have made it possible to run models that would previously have required a GPU server on hardware consuming a few watts. A model that achieves near-identical accuracy to a large cloud model but runs in 10 milliseconds on a compact edge device is a fundamentally different proposition for operational deployment.

Regulatory and data sovereignty pressure

Industries handling sensitive data - healthcare, financial services, critical national infrastructure - face growing regulatory expectations around where data is processed and stored. Edge AI is an architectural response to these constraints: if patient images, biometric data, or operational data never leave the local device, the regulatory exposure associated with transmitting them to a cloud provider is eliminated.

Connectivity economics

A multi-camera smart factory generating continuous HD video from fifty production lines produces data volumes that are prohibitively expensive to transmit to the cloud at full fidelity. Edge AI changes the economics: instead of transmitting raw video, the edge device transmits only structured events - "defect detected, Line 3, Camera 7, 14:23:07" - reducing bandwidth consumption by orders of magnitude while retaining all the operationally relevant information.

B2B applications of edge AI

Manufacturing and quality control

Machine vision for automated defect detection, tool wear monitoring, assembly verification, and worker safety compliance are among the most mature edge AI applications. The low latency of on-device inference is essential for any application where the AI output must trigger an immediate physical response - ejecting a defective product, stopping a machine, or raising an alert before a dangerous condition develops.

Smart retail

Customer behaviour analytics, shelf availability monitoring, queue management, and loss prevention using on-device video analytics. Edge AI allows retailers to extract operational intelligence from camera systems without the cost and compliance complexity of streaming footage offsite.

Healthcare and medical devices

Diagnostic support, patient monitoring, and medical imaging analysis benefit from edge AI where patient data cannot leave the clinical environment, or where real-time response is required independently of network connectivity. Edge inference on medical imaging devices is an area of growing development activity.

Transportation and logistics

Vehicle-mounted AI for driver monitoring, load inspection, route optimisation, and predictive maintenance. Platforms such as the Thundercomm EB5G2, with integrated 5G and multiple camera interfaces, are well suited to in-vehicle edge AI deployments where connectivity is intermittent and latency requirements are strict.

Smart infrastructure and utilities

Substation monitoring, pipeline anomaly detection, traffic management, and smart grid applications use edge AI to process sensor data locally and act on it without depending on a centralised control system. At remote sites with limited connectivity, the ability to operate autonomously is often a baseline requirement rather than a feature.

Security and surveillance

On-device object detection, people counting, and anomaly recognition in video feeds, replacing rules-based motion detection with AI-driven analysis that can distinguish between a genuine security event and an innocuous movement. Processing locally means lower storage requirements and reduced privacy exposure from continuous cloud video streaming.

What to look for when evaluating edge AI hardware

For businesses beginning to evaluate edge AI hardware, the following factors tend to define the practical fit between a platform and an application.

• TOPS rating and the model it needs to run: TOPS is a useful comparative metric but is not the only one that matters. The NPU architecture, supported precision levels (INT8, INT4, FP16), and compatibility with common AI frameworks (ONNX, TensorFlow Lite, PyTorch Mobile) determine whether a given model will run efficiently on a given platform
• Camera and sensor input support: multi-camera deployments require ISP capability and sufficient interface bandwidth; platforms like the Qualcomm QCS8250 support up to 24 simultaneous camera feeds, which matters considerably for larger surveillance or inspection deployments
• Industrial connectivity: 5G, Wi-Fi 6, Ethernet, and industrial fieldbus interfaces (RS232/RS485/CAN) determine how the edge device integrates into the existing site infrastructure
• Software and model deployment tooling: edge AI stations with built-in management platforms (such as OSware.Edge on the Thundercomm EB series) simplify OTA model updates, device monitoring, and multi-site deployment without custom infrastructure development
• Environmental rating: fanless industrial design, wide operating temperature, and DIN-rail or panel mounting options determine whether the platform can be deployed in the intended environment without additional enclosure engineering
• Scalability path: a platform that supports AI accelerator expansion (such as the EB6S with its optional 70 or 200 TOPS accelerator cards) allows deployments to grow compute capacity without replacing the base hardware

Frequently asked questions

Do I need internet connectivity to run edge AI?

No. That is one of edge AI's primary advantages. Once a model is deployed on the device, inference runs entirely locally. Connectivity may be used for model updates, telemetry transmission, or cloud integration, but is not required for the AI workload itself.

What is a TOPS rating and how much do I need?

TOPS (Tera Operations Per Second) measures the number of arithmetic operations a processor can perform per second at a given numerical precision. Higher TOPS allows more complex models or more simultaneous inference tasks. A single-camera quality inspection application might run comfortably on 10–15 TOPS; a multi-camera smart factory deployment running several concurrent models simultaneously may require 50 TOPS or more. Requirements should be derived from the specific models and workloads involved rather than from TOPS figures alone.

How is an edge AI model different from a cloud AI model?

The underlying neural network architecture may be identical, but edge-deployed models are typically compressed and quantised for efficient execution on constrained hardware. This involves some accuracy trade-off that must be validated for the specific application, though for many industrial use cases the practical difference is negligible.

Can edge AI hardware be updated as models improve?

Yes. Most industrial edge AI platforms support over-the-air (OTA) model updates, allowing improved models to be pushed to deployed devices without physical access. This is a key operational requirement for real-world deployments where retraining and model improvement are part of an ongoing operational process.

Is edge AI only relevant for large enterprises?

No. The cost profile of edge AI hardware has fallen significantly, and compact, lower-TOPS platforms are accessible to smaller manufacturers and operators. The operational benefits - reduced cloud costs, improved latency, offline resilience - are relevant at almost any scale.

What is the difference between an edge AI station and an edge AI SoC or SOM?

An edge AI station (such as the Thundercomm TurboX EB series) is a complete, deployable device with enclosure, power management, cooling, and interfaces included. A SoC (System on Chip) or SOM (System on Module) such as the TurboX C6490 or Qualcomm Dragonwing IQ series is a component or module that an engineering team integrates into a custom carrier board or product design. Edge AI stations suit deployment of AI capability in existing operations; SoCs and SOMs suit building AI capability into a new product.

How does edge AI affect data privacy and compliance?

Significantly and positively, in most cases. When inference runs locally and only structured results (rather than raw video, audio, or biometric data) leave the device, the data governance and compliance obligations associated with transmitting sensitive data to third-party cloud infrastructure are reduced or eliminated. This is a meaningful advantage in healthcare, financial services, and public sector deployments.

Key takeaways

• Edge AI is AI inference running locally on hardware at the data source, rather than in a remote cloud - faster decisions, lower bandwidth cost, better privacy, and offline resilience
• Training still happens in the cloud; edge AI is about deploying the resulting models where the data is generated
• Three factors are driving B2B adoption in 2026: industrial-grade hardware has become available at deployable cost and form factor; model compression techniques have made powerful inference tractable on compact devices; and regulatory and connectivity economics favour local processing for many applications
• Manufacturing quality control, smart retail, healthcare, transport, and infrastructure are the leading sectors for edge AI deployment today
• Evaluating edge AI hardware requires looking beyond TOPS to framework compatibility, connectivity, environmental rating, and model deployment tooling
• Edge AI stations suit operational deployment; SoCs and SOMs suit integration into custom product designs - both have their place in a B2B technology strategy

Conclusion

Edge AI is not a future technology. It is being deployed today by businesses that need decisions faster than a cloud round trip allows, data privacy that cloud processing cannot guarantee, and AI capability that does not fail when the internet connection does. The hardware that makes this possible - compact, fanless, industrially rated, with enough on-device compute to run meaningful AI models - has reached the point where it can be deployed outside a controlled lab environment and into real-world industrial and commercial operations.

For businesses at the beginning of their edge AI journey, the most valuable first step is usually identifying the specific decisions that need to be made faster or more reliably than current processes allow, and working backwards to the hardware and model requirements from there.

BCD Atlantik supplies edge AI hardware from Thundercomm and Qualcomm Dragonwing, ranging from deployable edge AI stations to industrial SoCs and SOMs for embedded product development. Browse our edge computing range, or speak to our team about the right starting point for your application.

Choosing the right 5G router for business in 2026 is no longer a niche decision reserved for large enterprises or telecoms specialists. UK 5G coverage has expanded significantly - Ofcom data from late 2025 places EE at 89% outdoor premises coverage, O2 at 76%, Three at 71%, and Vodafone at 64% - which means the connectivity is now available across a wide enough footprint to make 5G a credible primary or failover WAN option for most business sites. The challenge has shifted from availability to selection: which router is the right fit for your application, your environment, and your management requirements?

This guide covers what separates a genuine business-grade 5G router from a consumer device, the specifications that matter most at the evaluation stage, and a comparison of the options available from BCD Atlantik to help you narrow down your shortlist.

Summary

For most UK businesses in 2026, the right 5G router comes down to three questions:

• Is this a fixed-site deployment or a mobile/vehicle application?
• Do you need integrated enterprise security and cloud management, or a more open and configurable platform?
• And how important is 4G fallback coverage for locations where 5G signal is inconsistent?

The Teltonika RUTX50 suits industrial and IoT-adjacent fixed or semi-fixed deployments where configurability and remote management matter; the Lantronix G520 suits enterprises and critical infrastructure operators where built-in security, fieldbus protocol support, and ConsoleFlow cloud management are priorities.

What is a business 5G router?

A business 5G router is a purpose-built networking device that combines a 5G cellular modem with routing, security, and management features designed for commercial deployment rather than domestic use. Consumer 5G routers prioritise simplicity and price; business 5G routers prioritise reliability, security, manageability at scale, and durability in demanding environments.

In practice, this means business-grade devices typically include:

• Dual-SIM support with automatic carrier failover, allowing a backup cellular connection if the primary SIM loses coverage or signal
• Multi-WAN failover between 5G, 4G, and fixed-line Ethernet, so no single connectivity path is a single point of failure
• Enterprise VPN support (IPsec, OpenVPN, WireGuard) for secure site-to-site or remote access connectivity
• Cloud-based remote management, enabling configuration, monitoring, firmware updates, and diagnostics across a fleet without requiring physical access to each device
• Industrial-grade hardware ratings for temperature, power input, and physical durability, particularly for vehicles, cabinets, or outdoor enclosures

This is a meaningfully different product category from a consumer hotspot or a CPE router, even where headline 5G speeds might appear similar.

How business 5G routing works

In a typical business 5G deployment, the router connects to the cellular network via one or two SIM cards, negotiates a data session with the operator, and presents a standard IP network to the devices behind it. The router manages WAN selection - preferring 5G where available and falling back to 4G LTE automatically - and handles NAT, firewall, VPN, and DHCP functions for the local network.

Where a fixed-line broadband connection is also present, the router can be configured to use cellular as a primary connection, as a failover backup, or as part of a load-balanced arrangement that spreads traffic across both. For branch sites where fibre is unavailable or prohibitively expensive, 5G as primary WAN is an increasingly practical architecture given current UK coverage levels.

It is important to note that, according to Ofcom's Mobile Matters 2025 research 5G Standalone (SA) downloads are on average 45% faster than 5G Non-Standalone (NSA). Business router selection should take into account whether the device supports SA mode, particularly as operators extend their SA network rollouts through 2026.

Key features and specifications to evaluate

5G standard and band support

• SA (Standalone) and NSA (Non-Standalone) architecture support - SA offers lower latency and better performance where available
• Sub-6 GHz band support for wide-area coverage; mmWave for high-density urban deployments (less relevant for most business use cases in the UK currently)
• Backward compatibility with 4G LTE Cat 20 and 3G for areas where 5G has not yet reached

Dual-SIM and failover

• Dual physical SIM slots with automatic SIM switching and operator black/white listing
• Multi-WAN failover between cellular and Ethernet WAN, with configurable failover triggers (ping check, signal threshold, data limits)
• Band locking capability to pin the modem to specific frequency bands where the operator's 5G coverage is known to be strongest at a given site

Security

• Stateful firewall, IDS/IPS, and application-level filtering for enterprise-grade network perimeter control
• VPN support: IPsec, OpenVPN, WireGuard, and ZeroTier are the most commonly required protocols for business site-to-site and remote access deployments
• Regular firmware updates and a manufacturer with a clear security disclosure process

Remote management

For any deployment involving more than a handful of devices, remote management is essential. The ability to push firmware updates, change configurations, monitor signal quality, and reboot devices remotely makes the difference between a manageable network and one that requires a site visit for every issue. Teltonika's RMS platform and Lantronix's ConsoleFlow each address this need from different perspectives. RMS excels at high-volume IoT and industrial fleet management, while ConsoleFlow offers zero-touch provisioning and SaaS-based device management ideal for enterprise and mission-critical deployments.

Physical and environmental ratings

• Operating temperature range suitable for the deployment environment — a cabinet in an unheated plant room or a vehicle installation can see temperatures well outside the range of a consumer device
• Wide DC voltage input with reverse polarity and overvoltage protection for vehicle or industrial power environments
• Antenna connector type and count (SMA or RP-SMA) and MIMO capability — 4x4 MIMO provides significantly better throughput and reliability than 2x2 in real-world conditions

Benefits of a purpose-built business 5G router

• Reliable primary connectivity at sites where fibre is unavailable, expensive, or subject to long provisioning lead times
• Fast, resilient failover that prevents downtime when a primary fixed-line connection drops
• Secure remote access and site-to-site VPN without depending on a fixed IP address from the operator
• Scalable fleet management without proportional growth in on-site IT overhead
• Future-proofed investment: 5G networks in the UK continue to expand, meaning a device purchased in 2026 will access progressively better coverage and speeds over its operational life

Business 5G router use cases

• Branch and temporary office connectivity: 5G as primary or backup WAN for sites without fibre, pop-up offices, or temporary site offices on construction or events projects
• Vehicle and fleet networking: In-vehicle routers providing crew connectivity, live CCTV uplink, telematics data, and GNSS tracking for transport, logistics, and public safety fleets
• Industrial IoT backhaul: 5G routers aggregating sensor and controller data from a site and transmitting it to a cloud platform or head office SCADA system
• Retail and hospitality: Resilient connectivity for card payment systems, EPOS, digital signage, and guest Wi-Fi where a fixed-line outage would directly impact trading
• Critical infrastructure and utilities: Remote sites such as substations, pumping stations, and renewables installations where cellular backhaul is the only practical connectivity option

5G Business router comparison: Options from BCD Atlantik

The Teltonika RUTX50 is the most versatile option for deployments requiring strong industrial credentials alongside 5G, particularly where a highly configurable open platform and RMS remote management matter. The TRB500 suits scenarios where a compact 5G uplink is needed behind an existing router or connected directly to a single device. The Lantronix G520 is the stronger choice for Industry 4.0 and critical infrastructure applications, where built-in cybersecurity features, fieldbus protocol conversion, and ConsoleFlow's zero-touch provisioning reduce integration effort and ongoing management overhead. The E224 complements this for enterprise branch deployments where serial device connectivity alongside cellular WAN is a requirement.

A note on UK 5G coverage in 2026

When specifying a 5G router for a fixed site, it is worth checking coverage from multiple operators before committing to a SIM contract. Ofcom estimates that only 62% of the UK's land mass currently has 5G service available from at least one provider, which means rural and semi-rural sites may find 4G LTE remains the primary cellular technology available. A dual-SIM router with strong 4G fallback is therefore still the right specification for many UK deployments, even where 5G is the target technology.

Following the completion of the Vodafone and Three merger in mid-2025, the UK mobile market has entered a period of structural change, with network integration still under way. The resulting VodafoneThree network is the largest in the UK by subscribers and is expected to accelerate 5G rollout through combined spectrum and infrastructure. This makes dual-SIM capability with multi-operator support a sensible investment for deployments intended to remain in service for three to five years.

5G Business router buying guide: Key questions to answer

1. Fixed site or mobile? Vehicle and mobile deployments need a device rated for vibration, wider temperature ranges, and vehicle power inputs. Fixed-site deployments prioritise Ethernet port count and mounting options.
2. How many devices do you need to manage? For more than five or six routers, cloud-based remote management (Teltonika RMS or Lantronix ConsoleFlow) becomes essential rather than optional. ConsoleFlow is available as both SaaS and on-premises deployment for organisations with data sovereignty requirements.
3. Do you need Wi-Fi at the router, or will existing Wi-Fi infrastructure handle local wireless? The TRB500 is a pure 5G uplink gateway with no Wi-Fi; the RUTX50 includes dual-band Wi-Fi; the Lantronix G520 includes Wi-Fi depending on variant.
4. Do you need fieldbus protocol support or serial device integration? The Lantronix G520's fieldbus conversion suite and the E224's serial connectivity (RS232/RS485) are relevant for sites integrating legacy industrial equipment alongside cellular WAN.
5. Which operators have 5G at your target sites? Confirm this before selecting SIMs; the router hardware is only part of the equation.
6. What is the total cost of ownership? Factor in hardware, management platform subscriptions, SIM contracts, and installation when comparing options; headline hardware prices rarely tell the full story.

Frequently asked questions

What is the difference between a 5G business router and a 5G consumer router?

Business routers include dual-SIM failover, enterprise VPN, remote fleet management, more Ethernet ports, and hardware rated for industrial or vehicle environments. Consumer devices are designed for home use, typically with a single SIM, basic Wi-Fi, and no remote management capability.

Do I need a 5G router if my site already has fibre broadband?

Not necessarily as a primary connection, but a 5G router as a cellular failover can prevent costly downtime if your fixed-line circuit goes down. For businesses where connectivity outages directly impact trading or operations, the cost of a 5G failover device is typically low relative to the cost of an outage.

What is the difference between 5G SA and 5G NSA?

5G Non-Standalone (NSA) uses the 4G LTE network for control signalling and adds 5G for data throughput. 5G Standalone (SA) is a native 5G network architecture that delivers lower latency and higher efficiency. Ofcom data shows SA downloads are around 45% faster than NSA on average, and SA is the architecture being expanded by UK operators through 2026.

Can a 5G router replace a fibre broadband connection for business?

In many cases, yes — particularly for sites where fibre is unavailable or subject to long lead times. 5G download speeds are now sufficient for most business workloads including VoIP, video conferencing, and cloud applications. Latency and upload speeds are the main considerations; some high-demand workloads may still favour a wired connection where one is available.

Is Teltonika RMS included with the RUTX50?

The RMS platform is a subscription service from Teltonika, separate from the router hardware. It provides cloud-based remote management for device configuration, monitoring, and firmware updates. Contact BCD Atlantik for current RMS licensing and pricing details, as well as RUTX50 hardware quotations.

What is Lantronix ConsoleFlow and does the G520 require a subscription?

ConsoleFlow is Lantronix's cloud device management and connectivity services platform, available as both a SaaS application and an on-premises deployment. The G520 comes pre-enabled for ConsoleFlow, providing zero-touch provisioning, remote configuration, firmware updates, and device telemetry from day one. It suits organisations that need centralised visibility across distributed sites without building custom management infrastructure. Contact BCD Atlantik for current ConsoleFlow licensing details alongside G520 hardware pricing.

Which UK networks provide the best 5G coverage for business in 2026?

Based on Ofcom's late 2025 figures, EE leads for outdoor premises coverage at 89%, followed by O2 at 76%. For any specific site, checking coverage directly with operators and ideally conducting a site survey with a test SIM is the most reliable approach before committing to a 5G deployment.

Can the Teltonika TRB500 be used as a 5G modem behind an existing router?

Yes. The TRB500's single Gigabit Ethernet port can connect to an existing router's WAN input, providing a 5G uplink without replacing the downstream network. This makes it a practical option for adding 5G capability to an established network without a full infrastructure change.

Key takeaways

• 5G coverage in the UK has expanded materially through 2025 and 2026, making a 5G router for business a practical choice for primary or failover WAN across a much wider range of sites than a few years ago
• Dual-SIM with automatic 4G failback remains essential, as 5G coverage is still unavailable across significant portions of the UK's geographic landmass
• The Teltonika RUTX50 offers the strongest combination of 5G capability, interface flexibility, and open configurability for industrial and IoT-oriented deployments
• The Lantronix G520 suits Industry 4.0, critical infrastructure, and transportation deployments where built-in security, fieldbus protocol conversion, and ConsoleFlow cloud management are priorities
• Total cost of ownership should include router hardware, any management platform subscription, SIM contracts, and installation; hardware price alone is not a complete comparison
• 5G Standalone (SA) mode delivers materially better performance than NSA where available; confirm which architecture your target operator supports at your specific sites

Conclusion

A business 5G router is now a realistic part of most UK network infrastructure decisions, whether as a primary WAN connection, a resilient failover, or a connectivity backbone for remote and mobile deployments. The right choice depends less on headline 5G speeds and more on the deployment environment, management requirements, security posture, and the specific coverage available from your chosen operators at the sites you need to connect.

BCD Atlantik supplies 5G routers and gateways from Teltonika and Lantronix, available for B2B purchase with project support from our team. Browse our full 5G and cellular router range, or contact us to discuss the right specification for your deployment.

Raspberry Pi Compute Module business use has grown significantly over the past few years, moving well beyond education and hobbyist prototyping into serious commercial product development. Manufacturers, system integrators, and engineering teams across sectors from healthcare to industrial automation are now designing the Raspberry Pi Compute Module into products they intend to ship in volume. The reason is straightforward: the Compute Module takes the processing power, software ecosystem, and community support of the Raspberry Pi platform and repackages it into a compact, integration-ready form that suits custom hardware design rather than off-the-shelf development boards.

This guide explains what the Raspberry Pi Compute Module is, why it has become a credible choice for embedded systems in a business context, and how to decide which variant fits your application.

Summary

The Raspberry Pi Compute Module is a system-on-module (SOM) that combines the processor, RAM, and optional eMMC storage of a Raspberry Pi onto a compact board designed to plug into a custom carrier board rather than be used standalone. For businesses, it offers a well-supported, cost-effective platform for building embedded products in volume, with the advantage of a mature Linux software stack, long-term supply commitments, and a large developer community. The CM4 and CM5 are the two current generations, differing primarily in processing power, I/O capability, and target application profile.

What is a Raspberry Pi Compute Module?

A Raspberry Pi Compute Module is a small form-factor system-on-module that packages the core components of a Raspberry Pi (processor, memory, and storage) onto a board designed for integration rather than direct use. Unlike the standard Raspberry Pi single-board computers, a Compute Module has no USB ports, no full-size HDMI output, and no GPIO headers in the traditional sense. Instead, it exposes its interfaces through a high-density connector, allowing an engineer to design a custom carrier board that breaks out only the interfaces the product actually needs.

This distinction matters commercially. A standard Raspberry Pi is designed as a general-purpose development board; a Compute Module is designed to become a component inside something else. That shift in intent changes what becomes possible in terms of product form factor, interface selection, environmental ruggedisation, and BOM optimisation.

How it works in an embedded product

In a typical embedded deployment, the Compute Module sits on a custom-designed carrier board developed by the product manufacturer. The carrier board provides the connectors, power management, peripherals, and industrial interfaces relevant to that specific product; whether that is a touchscreen interface, RS485 for machine communication, a cellular modem, or an array of digital I/O lines for control applications.

During development, teams commonly use an official IO board, such as the Raspberry Pi CM4 IO Board or the Raspberry Pi CM5 IO Board, to prototype and validate their software before committing to a custom PCB design. Once the software stack is stable and the hardware requirements are confirmed, the carrier board can be designed to exactly match the product's needs, removing any unnecessary components and reducing unit cost accordingly.

The Raspberry Pi OS and its ecosystem of libraries, drivers, and community tooling transfer directly from the development environment to production, significantly reducing the software bring-up time compared to platforms with less established support.

Key features and specifications

Raspberry Pi CM4

The Compute Module 4 is built around a quad-core ARM Cortex-A72 processor running at 1.5GHz, with up to 8GB of LPDDR4 RAM and optional eMMC storage in 8GB, 16GB, or 32GB capacities. It supports dual 4K display output, hardware video decode at up to 4Kp60, Gigabit Ethernet, USB 2.0, dual camera interfaces, and a PCIe Gen 2 x1 interface for expansion. Wireless variants include dual-band 2.4/5GHz Wi-Fi and Bluetooth 5.0 with modular compliance certification, reducing the regulatory overhead of adding wireless connectivity to a product.

Available in a number of variants covering different combinations of RAM, storage, and wireless, the CM4 gives engineering teams flexibility to match the module specification tightly to the application, avoiding the need to pay for memory or storage that the product does not require. Extended temperature variants rated from -40°C to +85°C are also available through BCD Atlantik, making the CM4 suitable for demanding environmental conditions.

Raspberry Pi CM5

The Raspberry Pi Compute Module 5 represents a significant step up in performance. Built on the same quad-core Arm Cortex-A76 architecture as the Raspberry Pi 5, it delivers noticeably higher compute throughput and improved I/O capabilities compared to the CM4. RAM options extend to 16GB, and eMMC storage is available from 0GB Lite up to 64GB. Dual 4Kp60 HDMI output, Gigabit Ethernet, and the option of a fully-certified wireless module round out the specification.

The CM5 is the right choice where the application needs more compute headroom, such as handling multiple data streams, running edge inference workloads, or driving high-resolution display content alongside background processing tasks. For teams starting a new design today, the CM5 offers a longer effective product life before compute becomes a constraint.

Benefits of using Raspberry Pi Compute Modules for business applications

• Established ecosystem: The Raspberry Pi software stack, community, and library support are among the most mature of any embedded Linux platform, reducing development risk and recruitment difficulty.
• Flexible variant selection: Specifying the right combination of RAM, storage, and wireless avoids over-engineering the BOM for applications with modest requirements.
• Modular compliance certification: Wireless-equipped variants carry pre-certified RF compliance, reducing the regulatory testing burden when adding Wi-Fi or Bluetooth to a product.
• Long-term supply commitment: Raspberry Pi has committed to long-term availability of its Compute Modules, which matters considerably for embedded products with multi-year production runs.
• Separation of hardware and software development: Software can be developed and validated on an IO board while carrier board hardware is being designed in parallel, compressing the overall development timeline.
• Carrier board design freedom: Exposing all interfaces through a single high-density connector means the carrier can be optimised entirely around the product's requirements, with no compromises inherited from a general-purpose board layout.

Applications and use cases

Raspberry Pi CM business applications span a broad range of industries and product types. Some of the most common include:

• Industrial HMI and control panels: The CM5-based Edatec ED-HMI3120 series integrates the Compute Module into a ruggedised touchscreen display with industrial power input, watchdog, and crypto authentication - a ready-made embedded HMI solution for machine builders
• Edge gateways and connectivity computers: The Edatec ED-IPC1100 and ED-IPC3100 series use the CM0 and CM5 respectively, combining fieldbus interfaces, 4G connectivity, and DIN-rail mounting to create deployable industrial computers based on the Raspberry Pi platform.
• Medical and diagnostic devices: The combination of a controlled software environment, modular compliance certification, and established Linux drivers makes the CM platform attractive for Class I medical device integration.
• Digital signage and kiosk systems: Dual 4K display output and reliable long-term supply make the CM4 and CM5 well-suited to commercial display products requiring multi-year consistency.
• Retail and point-of-sale terminals: Low power draw, a compact footprint, and strong USB peripheral support suit embedded POS applications where the hardware must disappear inside a polished enclosure.
• Building automation and smart infrastructure: Wireless connectivity, Ethernet, and broad GPIO availability through a carrier board make the Compute Module a practical foundation for building management systems and access control devices.

CM4 vs CM5: Which is right for your project?

Feature	Raspberry Pi CM4	Raspberry Pi CM5
Processor	Quad-core ARM Cortex-A72, 1.5GHz	Quad-core Arm Cortex-A76, 2.4GHz
RAM options	1GB, 2GB, 4GB, 8GB	2GB, 4GB, 8GB, 16GB
eMMC storage	0GB Lite, 8GB, 16GB, 32GB	0GB Lite, 8GB, 16GB, 32GB, 64GB
Display output	Dual 4K	Dual 4Kp60 HDMI
PCIe	Gen 2 x1	Gen 3 x1 (via IO board)
Wireless (optional)	Wi-Fi 5 + BT 5.0	Wi-Fi 5 + BT 5.0
Extended temp option	Yes, -40°C to +85°C (contact BCD Atlantik)	Not yet available
Best suited for	Cost-sensitive, stable designs; extended temp applications	Higher compute demand; new designs with longer roadmap

For most new embedded product designs starting today, the CM5 is the stronger foundation — particularly where the product roadmap extends several years and compute requirements may grow. The CM4 remains highly relevant for cost-sensitive applications, designs already in production, and projects requiring the extended temperature range variants.

Getting Started: Development Kit and IO boards

For teams new to the Compute Module platform, or moving from CM4 to CM5, the Raspberry Pi CM5 Development Kit provides a complete starting point: a CM5 module and IO board with all the accessories needed to begin software development and hardware prototyping immediately. It is the most efficient way to validate the software stack before investing in carrier board design.

The CM4 IO Board and CM5 IO Board both expose the full interface set of their respective modules, providing a reference design that carrier board engineers can use to understand interface routing, power requirements, and recommended component selection before committing to a custom layout.

Best practices for Raspberry Pi CM business deployments

1. Define your interface requirements before selecting a variant. The carrier board design is driven by your I/O needs; the CM selection should follow from that, not the other way around.
2. Choose the Lite (0GB eMMC) variant if using external storage. Where the product will boot from an NVMe SSD or SD card via the carrier board, the Lite variant avoids paying for eMMC that will not be used.
3. Account for thermals early. Compute Modules in enclosed carrier board designs may need active or passive thermal management; the development kit phase is the right time to characterise this, not after tooling the enclosure.
4. Validate modular compliance early if shipping wireless products. The certified wireless module simplifies regulatory approval, but you still need to confirm the antenna design and placement on your carrier board do not invalidate the certification.
5. Plan for long-term software maintenance. Choose an OS distribution with a clear long-term support commitment; the Raspberry Pi OS LTS releases or Ubuntu LTS are both well-supported choices for production embedded deployments.
6. Register your project with your distributor. BCD Atlantik's project registration process for B2B CM customers ensures priority access to stock and supports volume pricing discussions before production ramp.

Frequently asked questions

What is the difference between the Raspberry Pi CM4 and a standard Raspberry Pi 4?

The CM4 uses the same processor and core silicon as the Raspberry Pi 4 Model B but removes the standard connectors and packages everything into a compact module designed to plug into a custom carrier board. It is not meant to be used standalone; it is a component for integration into a product.

Is the Raspberry Pi Compute Module suitable for commercial production runs?

Yes. As well as individually boxed modules, you can also purchase compute modules in bulk packs, where they come in trays. This reduces the time and cost of opening individual retail packages, and means they can easily be incorporated into a production workflow.

In addition, Raspberry Pi has committed to long-term supply of its Compute Modules specifically to support commercial and industrial customers with multi-year production needs. BCD Atlantik can support volume & bulk pack procurement and project registration for B2B customers.

Can I upgrade from CM4 to CM5 without redesigning my carrier board?

The CM5 uses a different connector than the CM4 and is not a drop-in replacement. A carrier board redesign is required. However, the software migration path is straightforward given the shared Raspberry Pi OS ecosystem.

What operating system do Raspberry Pi Compute Modules run?

Raspberry Pi OS (based on Debian) is the primary supported OS and is well-suited to embedded Linux deployments. Ubuntu and other Linux distributions are also supported on the CM platform.

Are extended temperature variants of the CM4 available?

Yes. Since March 2025, extended temperature CM4 variants rated from -40°C to +85°C have been available. These are not listed as standard shop variants; contact BCD Atlantik directly for pricing and availability.

What is the difference between the eMMC and Lite (0GB) variants?

The eMMC variants include onboard flash storage for the OS and application data. The Lite variant omits this, requiring the carrier board to provide an alternative boot storage, typically via an SD card slot or NVMe SSD on the carrier. Lite variants cost less and are preferred where external storage is already part of the design.

Do I need a development kit to get started?

Not strictly, but it is strongly recommended. The CM5 Development Kit provides the IO board, module, and accessories needed to begin software development and hardware bring-up without designing a carrier board first. It significantly reduces the time from evaluation to a validated software stack.

Can BCD Atlantik support ODM and custom hardware development around the Compute Module?

Yes. Alongside distribution, BCD Atlantik supports custom hardware development projects, including concept design, prototype development, and production support for products built on the Raspberry Pi Compute Module platform.

Key takeaways

• Raspberry Pi Compute Modules are designed for integration into custom carrier boards, not standalone use - this makes it well-suited to embedded product development.
• The CM4 remains a strong choice for cost-sensitive, extended-temperature, or already-in-production designs; the CM5 is the right foundation for new designs requiring more compute headroom.
• Modular wireless compliance certification, a mature Linux software stack, and long-term supply commitments are key commercial advantages of the Compute Module platform.
• Development kits and IO boards allow software development to proceed before carrier board hardware is finalised, compressing overall project timelines.
• Raspberry Pi CM business applications span HMI, edge computing, medical devices, digital signage, and industrial connectivity - with ready-made carrier solutions such as the Edatec IPC and HMI series also available

Conclusion

The Raspberry Pi Compute Module has earned its place in commercial embedded system design by combining genuine processing capability with an accessible software ecosystem, a flexible variant structure, and supply chain commitments that make it credible for multi-year production. Whether you are building an industrial HMI, an edge gateway, a connected kiosk, or a custom embedded controller, the CM platform offers a faster route from prototype to production than many proprietary alternatives.

BCD Atlantik is an authorised Raspberry Pi reseller supplying the full Compute Module 4 and Compute Module 5 range, including Lite and wireless variants, IO boards, development kits, and extended temperature CM4 options. We also stock the Edatec range of CM5-based industrial computers and HMI displays for teams looking for a ready-made carrier solution. Browse our full Raspberry Pi embedded range or get in touch to discuss your project requirements.

An IoT gateway for industrial use sits at the boundary between your machinery and your data systems, translating signals from sensors, PLCs and legacy equipment into a format your monitoring platform or cloud service can actually use. Choosing the wrong one is rarely obvious at first; it tends to show up months later as dropped connections, security gaps, or a device that simply cannot keep up as the deployment grows.

This guide walks through what an industrial IoT gateway actually does, the specifications that matter most, and how to evaluate options against your own application before you commit budget to hardware that will likely be in the field for five to ten years.

Summary

The right industrial IoT gateway for your application depends on five factors: the connectivity protocols your equipment already speaks (Modbus, CAN, RS485, etc.), the environmental conditions it will face (temperature, vibration, ingress), the network options you need (Ethernet, Wi-Fi, 4G/5G), the level of edge processing required, and the security and certification standards your industry demands. For most industrial sites, that means a DIN-rail mountable gateway rated for at least -20°C to 60°C, with wide-voltage DC input, hardware-level security, and remote management support rather than a consumer-grade router repurposed for the job.

What is an industrial IoT gateway?

An industrial IoT gateway is a ruggedised device that connects field-level equipment, such as sensors, actuators, PLCs and meters, to local networks, cloud platforms, or both. Unlike a standard network router, it is built to operate continuously in environments with extreme temperatures, vibration, electrical noise, and limited or no on-site IT support.

Functionally, an industrial gateway typically performs three jobs at once:

• Protocol translation, converting industrial fieldbus protocols such as Modbus RTU/TCP, CAN, or RS232/RS485 into IP-based formats like MQTT or HTTP

• Connectivity, providing a reliable uplink via Ethernet, Wi-Fi, or cellular (4G/5G) with failover between them
• Edge processing, filtering, aggregating, or acting on data locally before it reaches the cloud, reducing bandwidth use and latency

This is different from a generic IoT router, which usually just provides connectivity, or an industrial PC, which offers full compute but at higher cost and power draw. A gateway sits between the two: enough processing for protocol handling and light analytics, with the durability and interfacing that industrial environments demand.

How industrial IoT gateways work

In a typical deployment, sensors and controllers on the plant floor connect to the gateway over a wired fieldbus or local wireless link. The gateway normalises that data, applies any local logic (threshold alerts, basic filtering, store-and-forward buffering during connectivity drops) and forwards it upstream to a SCADA system, historian, or cloud IoT platform.

For example, a water treatment facility might use a gateway to poll a dozen Modbus RTU flow meters every few seconds, buffer the readings locally if the cellular connection drops, and push aggregated data to a cloud dashboard once connectivity is restored. The gateway is doing the unglamorous but essential work of keeping data flowing reliably, even when the network around it is not.

Key features and specifications to evaluate

When comparing industrial IoT gateways, the following specifications tend to separate genuinely industrial-grade hardware from consumer products in industrial packaging.

Connectivity and protocol support

• Wired interfaces: Ethernet (1x or more, ideally Gigabit), RS232/RS485, CAN
• Wireless: Wi-Fi, Bluetooth, 4G LTE Cat 1/4/6 or 5G depending on bandwidth needs
• Protocol support: Modbus RTU/TCP, MQTT, SNMP, and increasingly OPC-UA for newer automation systems
• Dual-SIM with automatic failover, useful for sites without a fixed-line backup

Environmental and mechanical durability

• Operating temperature range of at least -20°C to 60°C, with -40°C to 70°C for extreme climates or outdoor installations
• Wide DC input voltage (commonly 9–36V) with reverse polarity, overvoltage and overcurrent protection
• All-metal enclosure with DIN-rail mounting for cabinet installation
• IP-rated ingress protection where the gateway will be exposed to dust or moisture

Security

• Hardware-based crypto authentication and secure boot
• VPN support (IPsec, OpenVPN, WireGuard) for secure remote access
• Firewall and access control, with regular firmware updates from the manufacturer
• Remote management platform support for fleet-wide configuration and monitoring

Edge compute and AI capability

Increasingly, industrial gateways are expected to do more than relay data. Where a deployment involves machine vision, predictive maintenance, or anomaly detection, the gateway needs enough onboard processing to run inference locally rather than sending every frame to the cloud. This is where edge AI-capable platforms, built on processors such as the Qualcomm Dragonwing IQ series, are becoming increasingly relevant for gateway-class devices rather than just industrial PCs.

Benefits of choosing the right gateway

• Reduced downtime, through reliable failover connectivity and local data buffering during outages
• Lower long-term cost, by avoiding premature hardware failure or replacement in harsh conditions
• Easier scaling, with remote management reducing the need for site visits as the deployment grows
• Stronger security posture, limiting exposure as more OT devices connect to IT networks
• Faster decision-making, where edge processing reduces the latency between an event and a response

Applications and use cases

Industrial IoT gateways are used across a wide range of sectors, including:

• Manufacturing: Connecting legacy PLCs and sensors to modern MES or SCADA systems without replacing existing equipment
• Energy and utilities: Remote monitoring of substations, pumping stations and renewable assets in locations with limited connectivity
• Transportation and logistics: Fleet tracking, cold chain monitoring, and remote diagnostics on vehicles and containers
• Building and infrastructure management: HVAC, lighting and access control integration across distributed sites
• Agriculture: Soil and environmental sensor networks in remote, low-connectivity locations

Comparison: Gateway options from BCD Atlantik

To make this concrete, here is how three different gateway classes available from BCD Atlantik compare against typical industrial requirements.

The Teltonika TRB and RUT series suit applications that are primarily about reliable connectivity and remote management. The Edatec ED-IPC range adds more onboard compute and isolated I/O for sites running fieldbus protocols alongside connectivity. Where AI inference at the edge is part of the requirement, Dragonwing IQ-based platforms extend that further with substantial on-device AI performance and industrial temperature tolerance, while remaining well short of full industrial PC cost and power draw.

Best practices for selecting an industrial IoT gateway

1. Map your existing protocols first. List every fieldbus or interface your equipment already uses before evaluating hardware; retrofitting protocol support later is expensive.
2. Plan for the environment, not the spec sheet. If summer ambient temperatures inside a cabinet regularly exceed 50°C, build in headroom rather than choosing a gateway rated right at that limit.
3. Prioritise remote manageability. A device fleet without remote configuration and firmware update support becomes a maintenance liability as it scales beyond a handful of sites.
4. Check long-term availability. Industrial deployments often run for a decade; choose manufacturers with clear product longevity commitments rather than consumer-cycle hardware.
5. Don't over-spec edge compute you don't need. Edge AI capability adds cost and complexity, only worth it where local inference genuinely reduces latency or bandwidth requirements.
6. Confirm certifications relevant to your sector, such as CE, FCC, ATEX, or industry-specific standards before procurement, not after.

Frequently asked questions

What is the difference between an IoT gateway and an industrial router?

A router primarily provides network connectivity. A gateway typically adds protocol translation and edge processing on top of connectivity, making it better suited to integrating legacy industrial equipment with modern data platforms.

Can an industrial IoT gateway work without an internet connection?

Yes. Most industrial gateways support local data buffering and store-and-forward functionality, so data collection continues during connectivity outages and resumes uploading once the connection is restored.

What temperature range should I look for in an industrial gateway?

For most indoor industrial environments, -20°C to 60°C is a reasonable baseline. Outdoor, vehicle-mounted, or extreme-climate applications typically need -40°C to 70°C or wider.

Do industrial IoT gateways support 5G?

Increasingly, yes. 5G-capable gateways such as the Teltonika >a href="https://www.bcdatlantik.shop/products/trb500-gateway/28944000004581405">TRB500 and RUTX50 are available where high bandwidth or low latency is required, though many industrial applications are still well served by 4G LTE.

How important is cybersecurity in gateway selection?

Very. As OT and IT networks converge, gateways are a common attack surface. Look for hardware-based authentication, VPN support, regular firmware updates, and a manufacturer with a clear security disclosure process.

Can one gateway support multiple protocols at once?

Most industrial gateways support several protocols simultaneously, such as Modbus RTU over RS485 alongside MQTT over cellular, though the exact combination depends on the hardware and firmware.

What is edge AI doing in a gateway, and do I need it?

Edge AI allows the gateway to run inference, such as defect detection or anomaly recognition, locally rather than sending raw data to the cloud. It is worth the added cost where latency, bandwidth, or data privacy make cloud-only processing impractical; for straightforward monitoring and control, it is usually unnecessary.

How long do industrial IoT gateways typically last in deployment?

Well-specified industrial gateways are commonly deployed for five to ten years, particularly where the manufacturer commits to long-term product availability and firmware support.

Key takeaways

• An industrial IoT gateway combines protocol translation, connectivity and edge processing ‐ it is not simply a ruggedised router
• Match connectivity and protocol support to your existing field equipment before evaluating other specifications
• Environmental rating, power input flexibility and DIN-rail mounting are non-negotiable for most industrial sites
• Security and remote manageability become more important as deployments scale beyond a single site
• Edge AI capability is increasingly available in gateway-class hardware, but should be matched to genuine application need rather than added by default

Conclusion

Choosing the right IoT gateway for an industrial application comes down to matching hardware to the realities of your site: the protocols already in use, the environmental conditions, the connectivity options available, and how much processing needs to happen at the edge rather than in the cloud. Getting this right at the evaluation stage avoids costly retrofits later and sets up a deployment that can scale reliably over its full operating life.

BCD Atlantik supplies industrial IoT gateways and connectivity computers from Teltonika and Edatec, alongside Qualcomm Dragonwing-based edge compute platforms for applications requiring on-device AI. If you're evaluating options for a specific site or application, our team can help match the right hardware to your requirements.