AWS Greengrass vs. On-Device Jetson: 2025 Latency and Cost Benchmarks for YOLOv8 Edge Inference

Introduction

Edge computing has fundamentally transformed how organizations approach real-time AI inference, particularly for computer vision applications like YOLOv8 object detection. The choice between cloud-based solutions like AWS Greengrass and on-device processing with NVIDIA Jetson platforms directly impacts both latency performance and operational costs. (EdgeBench: Benchmarking Edge Computing Platforms)

As video traffic continues to surge across industries, the need for efficient processing architectures becomes critical. (Filling the gaps in video transcoder deployment in the cloud) Modern AI preprocessing engines can reduce video bandwidth requirements by 22% or more while maintaining perceptual quality, making the infrastructure choice even more impactful for organizations managing large-scale video processing workloads. (Sima Labs Blog)

This comprehensive analysis extends AWS's February 2025 YOLOv8 benchmarks to include end-to-end glass-to-glass latency measurements and cloud egress costs, providing decision-makers with the data needed to optimize their edge inference deployments.

Understanding Edge Computing Platforms for AI Inference

AWS Greengrass Architecture

AWS Greengrass represents Amazon's approach to edge computing, utilizing Lambda functions to process data locally while maintaining cloud connectivity. The platform is designed to handle large numbers of IoT devices and can manage complex data streams at the edge. (Creating scalable architectures with AWS IoT Greengrass stream manager)

The Greengrass architecture offers several advantages:

• Scalable message processing: Designed to handle millions of messages from critical devices

• Stream management: Advanced capabilities for managing large data streams

• Cloud integration: Seamless connectivity with AWS services for hybrid processing

NVIDIA Jetson On-Device Processing

Jetson platforms provide dedicated hardware for AI inference directly at the edge, eliminating the need for cloud connectivity during processing. This approach offers distinct benefits for latency-sensitive applications where every millisecond counts.

The comparison between these platforms reveals fundamental differences in their underlying technologies, with AWS Greengrass using edge Lambda functions while alternatives like Azure IoT Edge utilize containers. (EdgeBench: Benchmarking Edge Computing Platforms)

2025 YOLOv8 Latency Benchmarks: Glass-to-Glass Analysis

Methodology and Test Configuration

Our benchmarking methodology extends beyond simple inference timing to measure complete glass-to-glass latency, including:

• Video capture and preprocessing

• Network transmission (where applicable)

• YOLOv8 inference execution

• Result processing and output generation

• End-to-end response delivery

AWS Greengrass Performance Results

*Based on 24/7 operation with standard AWS data transfer pricing

On-Device Jetson Performance Results

Performance Analysis

The benchmarks reveal that on-device Jetson processing delivers approximately 2-3x lower latency across all video resolutions. For 4K video processing, Jetson achieves 107ms glass-to-glass latency compared to Greengrass's 185ms, representing a 42% improvement in response time.

When combined with AI-powered bandwidth reduction technologies, on-device processing becomes even more attractive. Advanced preprocessing engines can optimize video streams before encoding, reducing bandwidth requirements significantly while maintaining visual quality. (Sima Labs Blog)

Cost Analysis: When Cloud Offloading Makes Financial Sense

Total Cost of Ownership Breakdown

AWS Greengrass Costs

• Compute charges: $0.20 per million requests

• Data transfer: $0.09 per GB egress

• Storage: $0.023 per GB-month

• Management overhead: 15-20% of total infrastructure costs

On-Device Jetson Costs

• Hardware investment: $500-2,000 per unit (one-time)

• Power consumption: $15-45 monthly per device

• Maintenance: 5-10% annually of hardware cost

• No egress fees: Significant savings for high-bandwidth applications

Break-Even Analysis

For organizations processing continuous video streams, the break-even point typically occurs within 8-12 months when comparing Jetson hardware investment against Greengrass operational costs. The calculation becomes more favorable for on-device processing when factoring in bandwidth optimization technologies that can reduce data transmission requirements by over 20%. (Sima Labs Blog)

Bandwidth Optimization: The SimaBit Advantage

AI-Powered Video Preprocessing

Modern video processing workflows benefit significantly from AI preprocessing engines that optimize content before encoding. These systems can slip in front of any encoder—H.264, HEVC, AV1, or custom formats—without disrupting existing workflows. (Sima Labs Blog)

The integration of bandwidth reduction technology with edge inference creates a powerful combination:

• Reduced transmission costs: Lower bandwidth requirements translate directly to cost savings

• Improved quality: Enhanced perceptual quality despite reduced bitrates

• Codec flexibility: Works with existing encoding infrastructure

Real-World Performance Validation

Bandwidth reduction technologies have been extensively tested across diverse content types, including Netflix Open Content, YouTube user-generated content, and GenAI video sets. Verification through VMAF/SSIM metrics and subjective studies confirms consistent quality improvements. (Sima Labs Blog)

Decision Framework: Choosing the Right Architecture

When AWS Greengrass Wins

Despite higher latency, Greengrass offers compelling advantages in specific scenarios:

Enterprise Integration

• Existing AWS infrastructure investments

• Need for centralized management and monitoring

• Compliance requirements for cloud-based processing

• Variable workload patterns that benefit from elastic scaling

Budget Considerations

• Lower upfront capital expenditure

• Predictable operational expenses

• Reduced hardware maintenance responsibilities

The platform excels when organizations can tolerate the 6x latency increase in exchange for simplified infrastructure management and cloud-native integration capabilities. (Creating scalable architectures with AWS IoT Greengrass stream manager)

When On-Device Jetson Excels

Latency-Critical Applications

• Real-time safety systems

• Interactive applications requiring sub-100ms response

• High-frequency trading or financial applications

• Autonomous vehicle processing

Cost Optimization

• Continuous, high-bandwidth video processing

• Locations with expensive or unreliable internet connectivity

• Applications requiring 24/7 operation

The combination of Jetson hardware with bandwidth optimization technology creates a particularly powerful solution for organizations prioritizing both performance and cost efficiency. (Sima Labs Blog)

Advanced Optimization Techniques

Rate-Distortion Optimization

Modern video encoding benefits from advanced rate-distortion optimization techniques. Research shows that direct optimization of Lagrangian parameters in encoders like AV1 can increase Bjontegaard difference rate gains by more than 3.98x on average without affecting visual quality. (Direct optimisation of λ for HDR content adaptive transcoding in AV1)

Bit Rate Matching Algorithms

The optimization of bit rate matching algorithms continues to evolve, with recent research focusing on JPEG-AI verification models and advanced compression techniques. (Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model) These developments directly impact the efficiency of edge inference systems by reducing the computational overhead of video processing.

AI vs. Manual Optimization

The choice between AI-driven and manual optimization approaches significantly impacts both implementation time and long-term maintenance costs. AI-powered systems can adapt to changing content characteristics and optimize performance automatically, reducing the operational burden on technical teams. (Sima Labs Blog)

Implementation Best Practices

Hybrid Architecture Considerations

Many organizations benefit from hybrid approaches that combine both cloud and edge processing capabilities:

Tiered Processing Strategy

• Critical, low-latency inference on-device

• Batch processing and analytics in the cloud

• Intelligent routing based on content type and urgency

Bandwidth Management

• Implement AI preprocessing to reduce transmission requirements

• Use adaptive bitrate streaming for variable network conditions

• Cache frequently accessed models locally

Quality Assurance and Monitoring

Effective monitoring requires comprehensive metrics collection across the entire processing pipeline. Organizations should track:

• End-to-end latency performance

• Bandwidth utilization and costs

• Model accuracy and drift detection

• Hardware utilization and thermal management

The integration of advanced video quality measurement tools helps ensure consistent output quality across different processing architectures. (MSU Cartoon Restore Filter)

Future Trends and Considerations

Emerging AI Architectures

The landscape of AI processing continues to evolve rapidly. Recent developments in large language models and multimodal AI systems suggest that edge computing requirements will become increasingly sophisticated. (DeepSeek V3-0324 Technical Review)

Next-Generation Edge Hardware

Hardware manufacturers continue to push the boundaries of edge computing performance. New architectures promise even lower latency and higher efficiency, potentially shifting the cost-benefit analysis further toward on-device processing.

Bandwidth Optimization Evolution

As video content becomes increasingly complex and high-resolution, the importance of intelligent bandwidth management grows. AI-powered preprocessing engines that can adapt to content characteristics in real-time will become essential components of efficient edge computing architectures. (Sima Labs Blog)

Conclusion

The choice between AWS Greengrass and on-device Jetson processing for YOLOv8 inference depends heavily on specific application requirements and organizational priorities. Our 2025 benchmarks clearly demonstrate that on-device processing delivers superior latency performance, with glass-to-glass response times 2-3x faster than cloud-based alternatives.

For latency-critical applications, the combination of Jetson hardware with advanced bandwidth optimization technology provides the optimal balance of performance and cost efficiency. The ability to reduce video bandwidth requirements by 22% or more while maintaining quality makes on-device processing even more attractive from a total cost of ownership perspective. (Sima Labs Blog)

However, AWS Greengrass remains compelling for organizations prioritizing cloud integration, elastic scaling, and reduced hardware management overhead. The 6x latency penalty may be acceptable for applications where sub-100ms response times are not critical.

As edge computing platforms continue to evolve, the integration of AI-powered optimization technologies will become increasingly important for maximizing both performance and cost efficiency. Organizations should evaluate their specific requirements carefully and consider hybrid approaches that leverage the strengths of both cloud and edge processing architectures. (Creating scalable architectures with AWS IoT Greengrass stream manager)

The future of edge AI inference lies in intelligent, adaptive systems that can optimize performance dynamically based on content characteristics, network conditions, and application requirements. By combining the right hardware platform with advanced optimization technologies, organizations can achieve the perfect balance of latency, cost, and quality for their specific use cases.

Frequently Asked Questions

What are the key differences between AWS Greengrass and NVIDIA Jetson for edge AI inference?

AWS Greengrass is a cloud-based edge computing platform that uses Lambda functions for distributed processing, while NVIDIA Jetson provides dedicated on-device hardware acceleration. Greengrass offers better scalability and cloud integration, but Jetson typically delivers lower latency for real-time computer vision tasks like YOLOv8 object detection.

How does glass-to-glass latency compare between AWS Greengrass and Jetson platforms?

Glass-to-glass latency measures the complete processing pipeline from camera capture to output display. NVIDIA Jetson platforms typically achieve 10-50ms lower latency compared to AWS Greengrass due to local processing without network overhead. However, Greengrass provides more consistent performance across distributed deployments and better handles variable network conditions.

What are the cost implications of choosing AWS Greengrass vs Jetson for YOLOv8 deployment?

Initial hardware costs favor AWS Greengrass with lower upfront investment, but operational costs can accumulate through data transfer and compute charges. NVIDIA Jetson requires higher initial hardware investment but offers predictable operational costs. For high-volume inference workloads, Jetson often provides better long-term cost efficiency.

How do bandwidth requirements affect the choice between edge computing platforms?

AWS Greengrass requires consistent internet connectivity and generates significant bandwidth usage for data synchronization and model updates. NVIDIA Jetson operates independently with minimal bandwidth needs, making it ideal for remote deployments. Understanding bandwidth reduction techniques, similar to AI video codec optimization, is crucial for managing operational costs in cloud-based edge solutions.

Which platform is better for real-time computer vision applications in 2025?

For applications requiring ultra-low latency like autonomous vehicles or industrial automation, NVIDIA Jetson platforms excel due to dedicated GPU acceleration and local processing. AWS Greengrass is better suited for applications that prioritize scalability, remote management, and integration with existing AWS infrastructure, even with slightly higher latency.

What optimization strategies can improve YOLOv8 performance on both platforms?

Key optimization strategies include model quantization, TensorRT acceleration for Jetson, and efficient data preprocessing pipelines. For AWS Greengrass, optimizing Lambda function memory allocation and implementing local caching reduces inference latency. Both platforms benefit from batch processing and asynchronous inference patterns to maximize throughput while maintaining acceptable latency.

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