Your AI model needs to run closer to users. Latency from round-tripping to us-east-1 kills the user experience. You investigate AWS edge options and find two services: IoT Greengrass and Lambda@Edge. Both claim to run code at the edge. Both support AI inference. But they target completely different use cases.
IoT Greengrass runs on devices you control – industrial equipment, retail kiosks, cameras, gateways. Lambda@Edge runs in AWS’s global CDN network at CloudFront locations. Greengrass gives you full device control with offline capability. Lambda@Edge gives you global distribution with zero infrastructure management.
The choice isn’t about which is better. It’s about where your edge is. If you’re deploying to physical devices in factories, stores, or vehicles, you need Greengrass. If you’re serving web traffic and want inference closer to users without managing servers, you need Lambda@Edge. Picking wrong means either paying for infrastructure you don’t control or missing the offline capability your application requires.
AWS IoT Greengrass: AI on Your Devices
IoT Greengrass runs on Linux devices you deploy and manage. These might be industrial PCs, NVIDIA Jetson boards, Raspberry Pis, or custom hardware. You install Greengrass software on your devices, deploy AI models and application code from AWS, and the device runs inference locally.
The architecture centers on autonomous operation. Devices run your code even when disconnected from AWS. They cache results, buffer data, and sync back to the cloud when connectivity returns. This matters for manufacturing lines that can’t tolerate network outages, retail locations with unreliable internet, or vehicles that move through areas without coverage.
Greengrass organizes code as components. A component might be an AI model, inference code, data processing logic, or device management functionality. You package components in AWS, deploy them to device groups, and Greengrass handles installation and lifecycle management. Updates deploy automatically when devices connect.
Greengrass provides AWS ML Inference components that handle common machine learning workloads. AWS offers pre-built components like DLR for object detection that you can deploy directly. You can also create custom ML components using TensorFlow Lite, ONNX, or other frameworks. You deploy models alongside inference components. The device loads models into memory and processes inference requests locally. Results can be cached, sent to local applications, or streamed back to AWS through IoT Core. Greengrass ML components handle the inference runtime complexity while you focus on model selection and application logic.
Hardware requirements vary by workload. A simple classification model runs on a Raspberry Pi 4. A real-time object detection model processing video needs more powerful hardware like NVIDIA Jetson or industrial PCs with GPUs. Greengrass itself is lightweight, but your models and application determine actual requirements.
Device management happens through AWS IoT console. You organize devices into groups, deploy components to groups, and monitor device status. Greengrass reports health metrics, logs errors, and provides remote access for debugging. You can update running devices without physical access.
The cost model is straightforward: you pay for the devices (hardware purchase and maintenance) plus AWS services (IoT Core messaging, S3 storage for components, data transfer). There’s no Greengrass license fee – you mainly pay IoT Core messaging, S3 for artifacts, and data transfer. A device might cost $50-500 for hardware plus $1-5/month in AWS service costs depending on messaging volume and data sync frequency. Check current AWS IoT Core pricing and S3 pricing for your region to build accurate cost models for your deployment scale.
Offline capability is Greengrass’s key advantage. Inference continues when network fails. Local applications access inference results immediately. Latency is deterministic – no network round-trip variability. This enables use cases where cloud inference isn’t viable: autonomous vehicles, industrial automation, medical devices, remote monitoring.
Lambda@Edge: AI in CloudFront’s Global Network
Lambda@Edge runs your code in AWS’s global network of 700+ Points of Presence (PoPs) worldwide, plus 900+ embedded PoPs inside ISP networks. Users request content from CloudFront, Lambda@Edge intercepts the request, runs your code (including AI inference), and returns results. The execution happens at the edge location closest to the user.
The architecture integrates with CloudFront’s content delivery workflow. Lambda@Edge functions trigger on four events: viewer request (before CloudFront looks for content), origin request (before forwarding to origin), origin response (after receiving response from origin), and viewer response (before returning to user).
Each execution is stateless and isolated. Your function receives an event (the HTTP request), processes it, and returns a response. There’s no persistent storage. No state carries between requests. If you need to load data or models, you fetch them on each cold start or use CloudFront caching.
Lambda@Edge supports Node.js and Python runtimes only. If you’re planning to use TypeScript, Go, Java, or .NET at the edge, you’ll need to transpile or rethink your approach. Additionally, Lambda@Edge lacks several standard Lambda features: no VPC access, no Layers, no environment variables (except reserved), no X-Ray tracing, no container images, x86_64 only (no arm64), ephemeral storage limited to 512MB, and no provisioned concurrency. These constraints are especially relevant for ML workloads that typically rely on Layers for ONNX Runtime or other native dependencies. See AWS’s edge functions restrictions documentation for the definitive list of limitations.
Model deployment for AI inference requires careful design. Lambda@Edge uses standard ZIP deployments (publish in us-east-1 and replicate). Keep function code and dependencies within the current Lambda@Edge code size quotas. Large ML models typically won’t fit. You have options: use small models that fit within these limits, load models from S3 on cold start (slow), cache small model files in CloudFront to mitigate cold starts, or offload heavy inference to origin while using Lambda@Edge for preprocessing and routing.
For ML workloads at the edge, optimize aggressively. Use ONNX Runtime or TensorFlow Lite CPU builds. Apply quantization (INT8) to reduce model size and improve inference speed. These optimizations often make the difference between fitting Lambda@Edge constraints or requiring origin processing.
Request and response body sizes are also constrained when Include Body is enabled. CloudFront exposes approximately 40KB of the request body at viewer events and approximately 1MB at origin events. Equivalent limits apply to responses generated at the edge. Heavy payloads should be routed to origin rather than processed at the edge.
The package size constraint limits model selection. A compact BERT variant might fit. A GPT-2 small model might fit. Large vision models don’t fit. Most practical Lambda@Edge ML use cases focus on simple classification, embeddings, or routing logic rather than heavy inference. For complex models, Lambda@Edge acts as an intelligent router to backend inference services.
Execution limits vary by trigger type. Viewer request and response events have a 5-second timeout with fixed 128MB memory. Origin request and response events support up to 30-second timeout with standard Lambda memory allocation from 128MB up to 10,240MB on x86_64 architecture. Cold starts can add noticeable seconds of latency, especially at rarely-hit edge locations – budget for cold start variability when testing across different geographic regions. The 128MB memory and 5-second timeout constraints at the viewer level materially affect CPU-bound inference. Simple models work. Heavy processing doesn’t.
You pay for execution time and request count. As an example (as of October 2025), pricing in us-east-1 is roughly $0.60 per million requests plus $0.00005001 per GB-second. A function with 512MB memory running 100ms costs about $0.0000051 per request. At 10 million requests/month, that’s approximately $51 plus the $6 request charge: $57 total. Check the Lambda@Edge pricing section on the AWS Lambda pricing page for current rates in your region.
The value proposition is global presence without infrastructure. Users in Tokyo, London, and São Paulo all get sub-100ms latency because execution happens at their nearest CloudFront location. You write code once. AWS runs it everywhere. No servers to provision or manage.
Use cases center on content personalization, request routing, A/B testing, lightweight ML inference for recommendation, fraud detection, or content filtering. Heavy AI workloads don’t fit Lambda@Edge’s constraints. But routing requests to appropriate origin services based on quick ML classification works well.
Comparing Deployment Models
Greengrass deploys to devices you control. You choose hardware, provision devices, install software, and maintain physical infrastructure. You have full control over the execution environment, device configuration, and local resources.
Lambda@Edge deploys to AWS infrastructure you don’t see. AWS manages hardware, provisioning, scaling, and distribution. You write code and AWS runs it globally. Zero infrastructure burden but zero control over hardware or execution environment.
Greengrass runs continuously on your devices. Functions load once and process requests indefinitely. State persists. Models stay in memory. There’s no cold start after initial deployment. Every request processes with consistent latency.
Lambda@Edge spins up on demand. Functions might cold start. Models reload. Each execution is isolated. Latency varies between warm and cold invocations. Subsequent requests to the same edge location might be warm, but there’s no guarantee.
Greengrass operates offline. Devices function without cloud connectivity. Inference continues during network outages. Results queue and sync when connectivity returns. This enables truly autonomous operation.
Lambda@Edge requires connectivity. Functions execute in response to CloudFront requests. No requests means no execution. If CloudFront can’t reach origins or your function fails, requests fail. There’s no offline mode or autonomous operation.
Greengrass scales by deploying more devices. Need more capacity? Buy and deploy additional hardware. Scaling is physical and predictable but requires capital expenditure and deployment time.
Lambda@Edge scales automatically. AWS provisions capacity based on traffic. Sudden traffic spikes are handled automatically. You pay only for requests processed. Scaling is instant but you’re bounded by Lambda@Edge’s execution limits.
Hardware and Model Considerations
Greengrass hardware selection matters significantly. Your model’s computational requirements determine minimum device specifications. A MobileNet vision model runs on modest hardware. A YOLOv8 model needs GPU acceleration. ResNet-50 processing video needs substantial compute.
Device choices range from embedded boards (Raspberry Pi, NVIDIA Jetson Nano) for basic inference to industrial PCs with discrete GPUs for heavy workloads. Cost ranges from $50 for basic boards to $5,000+ for industrial equipment with GPUs.
GPU acceleration on Greengrass devices provides major performance improvements for vision and language models. NVIDIA Jetson boards offer good price-performance for edge AI. They include CUDA support, TensorRT optimization, and power-efficient inference. A Jetson Orin Nano costs around $500 and handles real-time object detection.
Lambda@Edge runs on AWS’s infrastructure. You don’t choose hardware. Functions execute on AWS’s managed environment with allocated CPU and memory. There’s no GPU access. All inference uses CPU. This limits model selection to CPU-efficient architectures.
Model optimization becomes critical for Lambda@Edge. Quantization reduces model size and inference time. INT8 quantized models run faster than FP32. ONNX Runtime provides CPU-optimized kernels. TensorFlow Lite offers efficient mobile/edge inference. These optimizations can make the difference between fitting Lambda@Edge constraints or not.
For Greengrass, optimize for your target hardware. Use TensorRT for NVIDIA devices. Use CoreML for edge devices with Apple silicon. Use ONNX with DirectML for Windows devices. Match the optimization to your deployment hardware.
Development and Deployment Workflows
Greengrass development involves several steps. Build and test your model locally. Package model and inference code as Greengrass components. Upload components to AWS. Create device groups. Deploy components to groups. Monitor deployment status and device logs.
Component development uses standard ML frameworks. Train your model in PyTorch or TensorFlow. Export to ONNX or TensorFlow Lite. Write inference code using your framework’s runtime. Package everything with dependency declarations. Greengrass handles deployment to devices.
Testing Greengrass deployments requires physical devices or virtual machines running the Greengrass software. You can’t fully test Greengrass locally without simulating the edge environment. AWS provides Docker containers for testing, but real device testing remains necessary before production.
Lambda@Edge development is simpler. Write your function in Node.js or Python. Test locally using SAM or similar tools. Deploy through CloudFormation or AWS Console. Lambda@Edge handles global distribution automatically. Testing is standard Lambda testing with CloudFront event formats.
The deployment pipeline differs significantly. Greengrass deployments propagate to devices when they next connect. A device offline for days receives updates when it comes back online. Rollback means deploying previous component versions. Gradual rollout means deploying to device groups sequentially.
Lambda@Edge deployments propagate to all edge locations over minutes. You deploy once. AWS distributes to hundreds of locations globally. Rollback means deploying previous function versions. Gradual rollout uses CloudFront traffic splitting or feature flags in code.
Cost Models and Economics
Greengrass costs combine hardware (one-time purchase plus maintenance) with AWS services (ongoing). A retail deployment with 100 devices might cost $10,000 in hardware ($100/device) plus $200/month in AWS services ($2/device). Over three years, that’s $10,000 + $7,200 = $17,200, or $5.73/device/month amortized.
Lambda@Edge costs are purely usage-based. No hardware. No maintenance. You pay for requests and compute time. The same inference workload might cost $500/month in Lambda@Edge if traffic is moderate, or $5,000/month if traffic is high. The break-even depends on scale and usage patterns.
For low traffic (thousands of requests per day), Lambda@Edge wins on cost. No hardware investment. Pay only for usage. For high traffic with stable device count, Greengrass becomes more economical after hardware amortization.
Hidden costs matter. Greengrass requires device provisioning, physical installation, network connectivity at device locations, and maintenance when hardware fails. Lambda@Edge requires origin infrastructure to handle requests Lambda@Edge can’t process alone and data transfer costs for responses.
Development costs differ too. Greengrass requires expertise in embedded systems, device management, and IoT protocols. Lambda@Edge requires expertise in serverless, CDN behavior, and distributed systems. Team skill sets influence which platform is more cost-effective to build and maintain.
Security and Compliance
Greengrass security centers on device security. Devices authenticate to AWS IoT Core using X.509 certificates. Local execution means data doesn’t leave the device unless you explicitly send it. This helps with data sovereignty and privacy requirements – sensitive data can be processed locally without cloud upload.
Device compromise is a risk. If an attacker gains physical or network access to a Greengrass device, they potentially access the models, data, and application code. Device hardening (secure boot, encrypted storage, network isolation) becomes critical for sensitive deployments.
Lambda@Edge security follows AWS Lambda’s model. Functions run in isolated execution environments. You don’t manage underlying infrastructure security. But all data flows through CloudFront and Lambda@Edge, so data in transit must be encrypted (HTTPS). You can’t keep data entirely on-device because Lambda@Edge only exists in AWS infrastructure.
Compliance requirements influence choice. HIPAA, GDPR, or industry-specific regulations might require keeping data on controlled devices. Greengrass supports this. Lambda@Edge processes data in AWS’s edge locations, which might not meet specific geographic or sovereignty requirements.
Audit logging differs. Greengrass publishes logs to CloudWatch when connected. You can also log locally. Lambda@Edge always logs to CloudWatch. All function executions generate logs. This makes Lambda@Edge easier to audit but means all execution data flows to AWS.
Real-World Use Cases
Greengrass excels at industrial IoT. Manufacturing lines run quality inspection using computer vision models on Greengrass-enabled cameras. Models detect defects in real-time. Results feed directly into production control systems. No network latency. No cloud dependency. Production continues during internet outages.
Retail deployments use Greengrass for customer analytics. Cameras with Greengrass run person detection and tracking models. Analytics stay on-premises for privacy. Aggregate statistics (not video) sync to AWS for business intelligence. Stores operate independently. Corporate systems get insights without video data.
Autonomous vehicles rely on Greengrass. Cars can’t depend on cloud connectivity for safety-critical decisions. Models for object detection, lane keeping, and decision making run on Greengrass-enabled edge computers in the vehicle. Connectivity enables updates and diagnostics but isn’t required for operation.
Lambda@Edge serves dynamic content personalization. E-commerce sites use lightweight recommendation models in Lambda@Edge to customize product displays based on user behavior. The model runs at the edge, personalizes content quickly, and returns customized pages without backend round-trips.
Fraud detection benefits from Lambda@Edge. Payment processors run quick classification models to identify suspicious requests at the edge. Obviously fraudulent requests are rejected immediately. Borderline cases route to backend systems for deeper analysis. This reduces backend load and improves latency for legitimate users.
A/B testing and feature flagging use Lambda@Edge for intelligent routing. Rather than random assignment, lightweight ML models predict which experience suits each user. The function runs at the edge, classifies the user, and routes to appropriate origin or returns customized content directly.
Hybrid Architectures
Many production systems combine both services. Greengrass handles local inference on devices. Lambda@Edge handles global content delivery and user-facing APIs. Data flows from Greengrass devices to AWS, where backend systems process and aggregate. Lambda@Edge serves the resulting insights to users globally.
A smart city deployment might use Greengrass on traffic cameras for real-time vehicle detection. Data aggregates in the cloud. Lambda@Edge powers a public traffic map website, showing real-time conditions with sub-100ms latency globally. Greengrass provides data collection. Lambda@Edge provides data delivery.
Retail chains might run inventory tracking on Greengrass devices in stores. Computer vision models detect products on shelves. Data syncs to central inventory systems. A corporate dashboard uses Lambda@Edge to deliver real-time inventory views to regional managers worldwide. Greengrass handles edge data collection. Lambda@Edge handles edge data delivery.
The architecture pattern is clear: Greengrass for data collection and local processing on devices you control. Lambda@Edge for content delivery and API endpoints serving global users. Neither replaces the other. They complement each other in comprehensive edge computing architectures.
Choosing the Right Service
Choose Greengrass when you have physical devices, need offline operation, require persistent state and long-running processes, or have data sovereignty requirements that mandate on-premises processing. The device cost and management overhead are justified by the autonomous operation and control.
Choose Lambda@Edge when you’re serving web traffic, need global low-latency without managing infrastructure, have lightweight inference requirements that fit Lambda’s constraints, or want purely usage-based costs. The execution limits are acceptable tradeoffs for zero infrastructure management.
Don’t choose Greengrass for web serving. Deploying physical devices to run web APIs is expensive compared to serverless options. Don’t choose Lambda@Edge for IoT devices. CloudFront isn’t designed for device-to-cloud communication.
The decision often isn’t either/or. Production systems use both. Greengrass handles the device edge. Lambda@Edge handles the delivery edge. Plan your architecture to use each service for its strengths rather than forcing one service to cover use cases it wasn’t designed for.
Need help architecting your edge AI deployment? At ZirconTech, we help teams design and implement edge computing solutions on AWS. Whether you’re deploying AI to IoT devices with Greengrass, optimizing global content delivery with Lambda@Edge, or building hybrid architectures that combine both, we can help you make the right architectural decisions and avoid costly mistakes. Get in touch to discuss your edge computing requirements.