Edge AI vs Cloud AI: Which Fits Your Workload?

Most comparisons of edge and cloud AI list advantages in the abstract. This guide works through the numbers that usually settle the question for an Indian factory, store, campus or CCTV control room: how much data you would have to upload, how much delay your process can tolerate, what happens when the link drops, where the data ends up, and how the costs are shaped. If the terms are new, start with What Is Edge AI?

Edge vs cloud, factor by factor

Worked example: the bandwidth of sending CCTV to the cloud

Video analytics is where the comparison is most lopsided, so it's worth doing the arithmetic for your own site. Hikvision's published recommended-bitrate sheet for its IP cameras gives these figures for a 1920×1080 stream at 25 frames per second:

• H.264: 4,096 kbps, about 4.1 Mbit/s per camera
• H.265: 2,048 kbps, about 2.0 Mbit/s per camera

The same sheet notes that complex scenes may need 20–30% more. Your cameras may be set differently, so read the actual bitrate from your NVR before you plan.

Continuous upload converts to monthly volume like this: 1 Mbit/s running for 30 days ≈ 0.324 TB (1,000,000 bits × 86,400 seconds × 30 days ÷ 8). Applying that to whole camera estates:

That is sustained upload, around the clock, on top of the site's normal traffic. Uplinks are usually sold for peak speed, not for being saturated all day, so a 64-camera site would need a dedicated, symmetrical link in the hundreds of megabits just to feed a cloud model.

Now compare the edge pattern, where only events leave the site. Assume the site raises 500 alerts a day and each carries a snapshot of about 0.3 MB (an assumption: JPEG size varies with scene and quality). That is 150 MB a day, or about 4.5 GB a month. The 16-camera H.265 site above uploads 10.6 TB a month, more than two thousand times as much.

There is a middle option. Most IP cameras also produce a low-resolution sub-stream. Hikvision lists 640×360 H.265 at 25 fps at 576 kbps, so 32 cameras would need about 18.4 Mbit/s, or 6.0 TB a month. That is cheaper to send, but small or distant objects such as faces, number plates and PPE are much harder to detect at that resolution, so test accuracy before relying on it.

Latency: set a budget, then measure

Latency matters when the AI result has to act on something: rejecting a part, opening a barrier, or warning a forklift driver. Work backwards from the physical process.

• Moving objects: distance travelled = speed × delay. If a belt moves at 0.5 m/s and the end-to-end delay is 0.3 s, a part moves 15 cm before the reject signal arrives. The camera has to sit far enough upstream, and the worst-case delay matters more than the average.
• Upload time counts too. Sending one 0.3 MB frame over 20 Mbit/s of spare uplink takes 0.12 s before any network round trip, queueing or inference. On a busy or congested link, that grows.
• Measure from the actual site. Time real requests to a test service in the cloud region you would use, at peak hours, over at least a week, and record the slowest results, not just the typical ones. A design that works from the head office may not work from a plant on an industrial estate.

Edge inference keeps the whole loop on the local network, which makes it both faster and more predictable. One caution applies either way: AI alerts can support safety, but they are not a substitute for certified safety systems such as interlocks, light curtains and emergency stops.

When the link goes down

A cloud design stops producing results the moment the uplink fails. Buffering video locally only delays the analysis, and an intrusion alert that arrives an hour late is not much use. An edge design keeps detecting, alerting on the LAN and recording events, then stores and forwards summaries when the link returns. For plants, warehouses and remote sites with unreliable connectivity, this alone often decides the architecture. See offline and private edge AI for how offline-first systems are built.

Data residency and the DPDP Act

CCTV footage in which people can be identified is likely to be personal data. Three points help frame the decision:

• Cloud regions exist in India. AWS lists Mumbai and Hyderabad regions. Microsoft Azure lists Central India (Pune), South India (Chennai), West India (Mumbai) and India South Central (Hyderabad). Google Cloud lists Mumbai and Delhi. Choosing an India region keeps processing in-country, but check that every service you use, including backups and logs, stays in that region.
• The law on transfers. Section 16 of the Digital Personal Data Protection Act, 2023 lets the Central Government restrict, by notification, the transfer of personal data to countries or territories outside India. Sector-specific laws with stricter rules still apply.
• Principles and timing. The DPDP Rules, 2025 were notified on 14 November 2025, with an eighteen-month phased compliance period. The framework's principles include purpose limitation, data minimisation and storage limitation. Processing video on-site and keeping only events supports minimisation, but notices, purpose, retention, security safeguards and breach handling still apply wherever the processing happens.

This section is orientation, not legal advice. Take advice on your specific deployment.

Cost structure: what to put in the comparison

Prices vary too much by provider, region and contract to publish meaningful numbers, so compare the line items over the same period, typically the expected life of the hardware.

• Edge: inference hardware, installation, rack or enclosure, power and cooling, spares, software licences, AMC, and staff or partner time for updates and site visits.
• Cloud: compute or per-inference charges, storage for footage and results, data-transfer charges where they apply, monitoring, and the site uplink upgrade that continuous upload requires. That last item is easy to forget and often the largest.

The rule of thumb is about usage pattern. Constant 24×7 workloads such as cameras favour owned hardware. Occasional or bursty workloads, such as reviewing recorded footage after an incident, favour paying per use.

When each one wins

Hybrid patterns that work

1. Edge inference, cloud management. Models run on-site. A central console deploys updates and monitors health across sites. Cloud providers offer services built for this: AWS IoT Greengrass runs locally deployed, cloud-trained models on edge devices, and Azure IoT Edge deploys and monitors containerised applications on site hardware.
2. Edge filter, cloud second look. The edge flags candidate events and sends only those short clips for a heavier model or a human to review.
3. Edge inference, cloud training loop. Hard or misclassified examples are sampled, under your data policy, to retrain the model centrally. The improved model then returns to the sites.
4. Store-and-forward reporting. Counts and events queue locally and sync to dashboards when the link allows.

Our deployment approach usually starts with the first pattern and adds the others as the system matures. For video workloads, see computer vision and CCTV analytics, and for sizing on-site hardware, see edge AI hardware categories.

Decision checklist

1. What decision does the AI make, and how quickly must it arrive in the worst case?
2. How much raw data does the workload produce per hour, and what is your real sustained uplink?
3. How often does each site lose connectivity, and what happens to the process when it does?
4. Which data may leave the site, where may it be stored, and for how long?
5. Does a model small enough for on-site hardware meet your accuracy target on your own footage?
6. Is the workload constant or occasional?
7. Who will patch, monitor and repair devices at each site?
8. What is the total cost of each option over the same period, including the uplink?

If most of your answers point to real-time, high-volume, always-on or sensitive data, start at the edge. If they point to occasional, cross-site or training-heavy work, start in the cloud. If the answers are mixed, design a hybrid from day one. Tell us about your workload and we'll help map it.

Frequently asked questions

Is edge AI cheaper than cloud AI?

For continuous video at many cameras it often is, mainly because it avoids sustained upload. For occasional workloads the cloud is often cheaper, because you pay only when it runs. Compare full line items over the same period, not hourly rates.

Can we start in the cloud and move to the edge later?

Yes, if the models and pipeline use portable formats and runtimes, such as ONNX models served by a runtime available on both sides. Plan for that from the start, so the pilot does not lock you into one platform.

Does an India cloud region solve data residency?

It keeps processing in-country, which helps. But residency is only one question. Purpose, retention, access control and breach handling still have to be designed, wherever the processing runs.

Sources and further reading

• Hikvision: H.264(5) & H.264(5)+ Recommended Bit Rate at General Resolutions (vendor PDF; 1080p and 640×360 figures used above)
• Ministry of Electronics and Information Technology, Government of India: The Digital Personal Data Protection Act, 2023 (Section 16)
• Press Information Bureau: DPDP Rules, 2025 Notified (backgrounder, 17 November 2025)
• AWS documentation: AWS Regions
• Microsoft Learn: List of Azure regions
• Google Cloud documentation: Regions and zones
• AWS documentation: Perform machine learning inference with AWS IoT Greengrass
• Microsoft Learn: What is Azure IoT Edge