Bring the AWS Cloud to Your Factory Floor
Sphere’s AWS IoT Greengrass practice deploys AWS cloud capabilities – Lambda functions, ML model inference, stream processing, and device management – directly on your edge hardware. Process data locally, make real-time decisions, and sync with the cloud – all from the same AWS toolkit your teams already know.
Trusted by Leading Enterprises
Why This Matters Now
Industrial organizations adopting AWS for IoT analytics face a fundamental tension: the insights they need most require real-time local decisions, but their existing AWS investments are cloud-centric. Running everything in the cloud adds latency, connectivity dependency, and bandwidth costs that make real-time industrial applications impractical.
1. Cloud Processing Can’t Meet Industrial Latency Requirements
Sending plant floor data to AWS regions for processing and waiting for a response adds 50–500ms – too slow for production line control, safety systems, and real-time quality monitoring.
2. Cloud Connectivity Is Unreliable in Industrial Environments
Factories, mines, and remote facilities frequently experience network interruptions. Cloud-dependent systems fail during outages – often at the worst possible moment.
3. Bandwidth Costs Limit IoT Data Volume
Streaming all raw sensor data to the cloud is prohibitively expensive. Edge processing must reduce data volume before cloud transmission.
What Sphere Delivers
AWS IoT Greengrass extends AWS cloud capabilities – Lambda, SageMaker, Kinesis, and Secrets Manager – to run directly on industrial edge devices. Sphere’s Greengrass V2 certified engineers design the edge processing architecture, develop the component library, and manage the deployment pipeline that gives your teams full cloud capability with local performance.
Built On Industry-Leading Technology
Sphere’s AWS IoT Greengrass offering is built for industrial environments that need cloud-grade services at the edge without sacrificing local performance or operational resilience. The stack combines Greengrass V2, Lambda-based edge processing, SageMaker model deployment, secure cloud synchronization, and industrial protocol support so manufacturers and infrastructure operators can run filtering, inference, storage, and control logic directly on site.
Who This Is For
INDUSTRY
VERTICAL APPLICATION
Design Your AWS Edge Architecture With Sphere
Sphere’s Greengrass V2 certified architects will review your current IoT architecture, identify edge processing opportunities, and propose an AWS Greengrass deployment design – in a free 45-minute technical session.
How It Works
Edge Architecture Design
Define edge compute requirements, select hardware (AWS-qualified devices), and design component architecture.
Greengrass Core Setup
Deploy Greengrass V2 nucleus on target edge hardware. Configure cloud connectivity and IAM roles.
Component Development
Develop custom Greengrass components for data processing, protocol translation, and ML inference.
Fleet Deployment
Use Greengrass deployment pipeline for controlled rollout across all edge devices.
ROI & Bussines Impact
Greengrass edge deployments reduce cloud bandwidth costs by 60–90% (by processing data locally before cloud sync), eliminate operational failures from connectivity interruptions, and reduce edge ML inference latency from 200–500ms (cloud) to under 50ms (local).
Average annual cost savings: $200K–$800K from bandwidth reduction and operational efficiency gains.
Let’s Connect
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Client Partner
Hear From Our Clients
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Sphere provided excellent full-stack development manpower to augment our team and work with us.
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With Sphere, we were able to migrate in half the time it would take to train an additional FTE…
These things would not have been achievable if we did not build our own in-house system. We augmented our development team capabilities using Sphere’s developer, who works very well with our Dev Lead in Chicago. Sphere’s developer was an expert in the new system, and continues to be an expert as we evolve it.
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