Distributed AI Infrastructure

Local models for real-time decisions. Cloud models for strategic insights.

Run inference at the edge when connectivity fails, latency matters, or data can't leave. Augment-don't replace-your cloud ML infrastructure.

Calculate Your Savings

Representative Savings Model

Model: $1.68M Annual Savings

2,000 production lines | Real-time quality inspection | 92% cost reduction, <5ms inference

Before Expanso

Cloud-Only ML Inference

Monthly Cost $165,000

Edge Inference (Existing HW): $0 Cloud Inference (API/GPU): $145,000 Network Connectivity: $15,000 Data Transfer: $4,000 Total: $165,000

Inference Latency 800-2,000ms

Uptime (connectivity-dependent) 85%

Defect Detection Days to Weeks

With Expanso

Hybrid Edge + Cloud Inference

Monthly Cost $13,000

Edge Inference (Existing HW): $0 Cloud Inference (Retraining/Async): $8,000 Network Connectivity: $2,000 Data Transfer: $1000 Total: $11,000

Inference Latency <5ms

Uptime (works offline) 99.99%

Defect Detection Real-time

$1,848,000/year

95% cost reduction | <5ms inference | 99.99% uptime

The Challenge

When Cloud-Only ML Fails

📡

Connectivity Issues

Unreliable networks make real-time decisions impossible.

8-15% downtime in real-world settings

Factory floors, remote facilities, oil rigs, agricultural sites lack reliable low-latency connections. When network drops, cloud inference stops-production halts, defects slip through, autonomous systems fail.

Gartner: 30% of industrial control systems adopting edge AI by 2025

50-200ms latency breaks real-time use cases

Network round-trips make sub-10ms requirements (industrial robotics, quality inspection, autonomous systems) impossible. By the time prediction returns, the moment is gone.

Industrial robotics and transportation scenarios require <5ms latency for accurate control

💸

Cloud Costs Scale Badly

Centralized GPUs and APIs costs explode with volume.

10-100× more expensive than edge inference

Cloud GPU instances cost $1,000-$100,000+/month depending on scale. Edge inference on existing hardware: $0 incremental compute. Same model, dramatically different economics.

Typical manufacturer: $145K/month cloud inference → $8K/month hybrid edge

Local models run faster and better tuned

Edge models optimized for specific conditions (production line config, facility layout, equipment characteristics) outperform generic cloud models. Plus <10ms inference vs 800ms+ cloud round-trip.

80× faster inference and higher accuracy from site and scenario specific tuning

🔒

Privacy & Compliance Risk

Sensitive data creates regulatory exposure.

GDPR/HIPAA require local processing

Healthcare imaging, retail video, manufacturing floor data contain PII/PHI. EU AI Act and GDPR mandate local processing-cloud upload creates violations. High rate of 2024 data breaches involved cloud-based personal data.

Edge AI have unprecidented challenges in data movement restrictions

Proprietary data exposure

Manufacturing processes, product designs, operational patterns are competitive IP. Sending to cloud increases attack surface and data leakage risk. Smart cities anonymize footage in real-time before transmission.

Edge processing ensures that sensitive data never leaves your infrastructure

Cloud-Only ML Architectures
Create Single Points of Failure

Every prediction requires cloud connectivity

8-15% downtime from network failures

50-200ms latency makes real-time impossible

Sensitive data forced to leave premises

Cloud-Only Challenge

Hybrid Edge + Cloud

Inference Stops When Network Fails

Local models work offline-sync insights when connected

50-200ms Cloud Latency (800ms+ with network)

<10ms edge inference (<5ms for industrial robotics)

$1,000-$100,000+/month Cloud GPU Costs

$0 incremental compute-reuse existing hardware

Generic Models for All Locations

Site-specific models tuned to local conditions

GDPR/HIPAA Violations from Cloud Upload

Data stays local-only insights/anomalies go upstream

Expanso vs Traditional Solutions

Traditional Stack

The Expanso Advantage

Data Noise Reduction

Minimal or Manual Filtering

Built-in, Automated Filtering

Time to Insights

Slow

Real-Time

Stack Flexibility

Rigid, Vendor-Locked

Flexible, works with nearly every vendor

Cost Efficiency

Increases rapidly with the amount of stored data

Up to 80% Cost Reduction

Data Noise Reduction

Traditional Stack:

Minimal or Manual Filtering

Expanso:

Built-in, Automated Filtering

Time to Insights

Traditional Stack:

Slow

Expanso:

Real-Time

Stack Flexibility

Traditional Stack:

Rigid, Vendor-Locked

Expanso:

Flexible, works with nearly every vendor

Cost Efficiency

Traditional Stack:

Increases rapidly with the amount of stored data

Expanso:

Up to 80% Cost Reduction

Use Cases Across Industries

Where Expanso Helps

The Expanso Advantage

Manufacturing & Quality Control

Real-time defect detection at production line speed-22% of edge AI market

Healthcare & Medical Imaging

Process patient data locally, maintain HIPAA compliance-14% market share

Retail & Loss Prevention

In-store analytics without uploading customer video-10% conversion boost

Agriculture & Remote Operations

Crop/livestock monitoring with intermittent connectivity

Energy & Utilities

Predictive maintenance at remote substations and wind farms

Autonomous Systems & Robotics

Sub-5ms decisions when cloud connectivity unavailable

Faster, Cheaper, More Reliable

Benefit

10-100× Cost Reduction
<10ms Edge Inference
100% Uptime (Offline-Ready)

What You Get

Edge models work without connectivity-8-15% downtime eliminated
Site-specific tuning for better accuracy-5G-enabled <1ms latency
Works with TensorFlow, PyTorch, ONNX-augments existing cloud ML

Talk to an Expert

Show us your ML infrastructure

We'll show you where to augment cloud models with edge inference-cutting costs 10-100×, achieving <10ms latency, and keeping sensitive data local.

Book A Demo

Local models for real-time decisions. Cloud models for strategic insights.

Model: $1.68M Annual Savings

Cloud-Only ML Inference

Hybrid Edge + Cloud Inference

When Cloud-Only ML Fails

Connectivity Issues

8-15% downtime in real-world settings

50-200ms latency breaks real-time use cases

Cloud Costs Scale Badly

10-100× more expensive than edge inference

Local models run faster and better tuned

Privacy & Compliance Risk

GDPR/HIPAA require local processing

Proprietary data exposure

Cloud-Only ML ArchitecturesCreate Single Points of Failure

Expanso vs Traditional Solutions

Data Noise Reduction

Time to Insights

Stack Flexibility

Cost Efficiency

Use Cases Across Industries

Faster, Cheaper, More Reliable

Benefit

What You Get

Show us your ML infrastructure

Cloud-Only ML Architectures
Create Single Points of Failure