Edge vs. Cloud Deployment

Computer vision workloads can run in the cloud, at the edge, or both. Understanding where to deploy your models is key to balancing cost, latency, and scalability.

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Table of contents

• What is cloud deployment?
• What is edge deployment?
• Key differences between edge and cloud
• Why deployment choice matters
• Use cases for edge and cloud
• Hybrid deployment strategies
• Edge and cloud in BaaS platforms
• FAQs

What is cloud deployment?

Cloud deployment runs models and pipelines in centralized data centers (AWS, Azure, GCP).

Benefits

• Virtually unlimited compute and storage.
• Easy scaling up or down.
• Centralized monitoring and updates.

Trade-offs

• Higher latency for real-time use cases.
• Continuous bandwidth costs.
• Dependence on connectivity.

What is edge deployment?

Edge deployment runs models closer to the data source on devices like cameras, gateways, drones, or IoT hardware.

Benefits

• Low latency for real-time analysis.
• Lower bandwidth costs by processing locally.
• Works offline or with intermittent connectivity.

Trade-offs

• Limited compute and memory compared to the cloud.
• Harder to update and maintain at scale.
• Device-specific optimization required.

Key differences between edge and cloud

• Latency: Edge is real-time; cloud introduces round-trip delays.
• Scalability: Cloud can handle petabytes; edge is resource-constrained.
• Connectivity: Cloud requires stable internet; edge can run offline.
• Cost: Cloud charges for compute and bandwidth; edge requires upfront device investment.

Why deployment choice matters

Where you run your pipeline impacts:

• User experience (real-time vs. batch results).
• Cost structure (pay-as-you-go cloud vs. distributed devices).
• Security & privacy (local processing avoids sending sensitive data to the cloud).

Use cases for edge and cloud

• Edge: Autonomous vehicles, factory floor inspections, retail foot-traffic analytics.
• Cloud: Video archive analysis, large-scale training, heavy inference tasks.
• Hybrid: Local edge devices do fast detection; results sync to cloud dashboards.

Hybrid deployment strategies

Most production systems blend both:

• Preprocessing at the edge — compress or filter data locally.
• Inference in the cloud — run large models centrally.
• Feedback loop — cloud insights update edge models over time.

This balances speed, cost, and intelligence.

Edge and cloud in BaaS platforms

Platforms like Lid Vizion abstract deployment choices. Developers can:

• Choose where to run inference (cloud, edge, or both).
• Push updates from the cloud to distributed edge devices.
• Manage data synchronization automatically.
• Monitor performance across mixed deployments.

This makes it easy to scale from prototype to production without re-architecting pipelines.

FAQs

Can I start in the cloud and move to the edge later?
Yes. Many teams prototype in the cloud, then optimize for edge devices once the use case stabilizes.

Do edge deployments require special hardware?
Often yes — GPUs, TPUs, or accelerators designed for vision (e.g., NVIDIA Jetson, Intel Movidius).

What about data privacy?
Edge processing keeps sensitive data local, reducing compliance risks.

Is the cloud always more expensive?
Not necessarily. For bursty workloads, pay-as-you-go cloud may be cheaper than managing edge fleets.

Can one app use both edge and cloud?
Absolutely. Hybrid architectures are now the default in modern AI deployments.

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