Predictive Maintenance AI
Senseye
SF 8.6Scalable predictive maintenance platform
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Predictive Maintenance AI
Augury
SF 9.0AI-driven machine health diagnostics
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Quick decision guide
Choose based on your workflow
Senseye may fit better if...
- Attention Index
- Time to Failure
- Auto Diagnostics
Augury may fit better if...
- Vibration Sensors
- AI Diagnostics
- Expert Review
Overview
How each tool is described
Senseye
What is Senseye?
Senseye Predictive Maintenance is Siemens’ scalable predictive-maintenance solution for manufacturers, centered on the cloud-based Senseye Cloud Application and supplemented by implementation services and expert guidance. It uses industrial AI and existing machine data to monitor asset condition, identify degradation, forecast failure risk, and prioritize where maintenance teams should investigate or intervene.
- Predictive maintenance: Senseye Cloud automatically models machine and maintainer behavior to identify abnormal deterioration and prioritize asset risks across a plant. Its analytics can highlight developing problems, failure risk, maintenance priorities, and remaining useful life, reducing the need for engineers to continuously analyze every machine manually.
- Fleet-scale operations: Senseye is designed to work across different machine types and scale from priority assets to thousands of assets across multiple plants. It uses existing condition and operational data from legacy or modern equipment, allowing manufacturers to establish a more consistent predictive-maintenance workflow across sites without requiring a separate analytics approach for every equipment vendor.
- Data and connectivity: Typical machine inputs include vibration, current, torque, temperature, and contextual operating data. Senseye can obtain information from historians, IoT platforms, databases, existing sensors, or its own cloud data store; supported ingestion methods include REST APIs, MQTT, email, AWS S3, Azure Blob Storage, and direct historian connections. New sensors are optional rather than a prerequisite when suitable machine data already exists.
- Implementation and commercial fit: Senseye Predictive Maintenance can combine the cloud application with Siemens advisory, implementation, onboarding, and optimization support. Access is subscription-based, while Siemens documentation states that onboarding into a customer's IT/OT environment can be provided for additional fees under separately agreed terms. Buyers should therefore assess asset scope, existing data infrastructure, connectivity, maintenance maturity, and planned expansion across sites.
Senseye is most relevant to manufacturers that already collect useful condition or operational data and want to scale predictive maintenance beyond isolated machines or pilot projects. Its value depends on data quality, asset criticality, maintenance adoption, and whether earlier identification and prioritization of developing failures can materially reduce unplanned downtime and improve maintenance decisions.
Augury
What is Augury?
Augury is an industrial AI platform for manufacturers that combines machine-condition monitoring, prescriptive diagnostics, process optimization, and role-based AI agents. Its current Industrial AI Workforce connects machine and operational data to help reliability, maintenance, and operations teams identify risks, improve production performance, and move from diagnosis toward approved actions.
- Machine health: Augury’s Machine Health uses continuously connected industrial sensors to capture condition data such as vibration, temperature, and magnetic signals. AI diagnostics identify anomalies, faults, and severity, while Augury reliability experts validate important findings and provide guidance on what is failing, when action is needed, and what maintenance teams should do next.
- Process optimization: Process Health analyzes production data to identify root causes of inefficiencies and provides real-time recommendations to operators and engineers. Teams can adjust process controls and setpoints to pursue objectives such as higher throughput and yield while reducing waste, energy consumption, and process losses.
- Implementation checks: manufacturers should assess which assets justify continuous monitoring, sensor and connectivity requirements, production-data integrations, CMMS and maintenance workflows, process-data availability, and adoption by reliability, maintenance, and operations teams. Augury can also integrate with systems including CMMS, EAM, SCADA, and APM platforms.
- Commercial considerations: Machine Health is offered as a turnkey annual service billed per machine, per year. Augury states that the fee includes sensors, software, connectivity, installation, support, updates, and unlimited diagnostics rather than requiring customers to purchase the monitoring hardware separately.
Augury is particularly relevant to manufacturers that want predictive maintenance and process optimization connected to prescriptive guidance and industrial AI workflows rather than relying only on condition alerts or standalone analytics.
Side-by-side
Key differences
Criteria
Predictive Maintenance AISenseye
Predictive Maintenance AIAugury
Best for
Predictive Maintenance AI
Predictive Maintenance AI
Score
8.6/10
9.0/10
Pricing
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Category / audience
AI Maintenance, CMMS & Asset Reliability Software › Predictive Maintenance AI
- predictive maintenance
- siemens
- machine learning
AI Maintenance, CMMS & Asset Reliability Software › Predictive Maintenance AI
- predictive maintenance
- machine health
- vibration analysis
Feature check
Side-by-side feature check
Feature
Senseye
Augury
Attention IndexScores prioritize assets needing maintenance focus
-
Time to FailureForecasts remaining useful life on machines
-
Auto DiagnosticsModels train automatically on incoming data
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Data ConnectorsReads historians sensors and operational sources
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Fleet ScalingDesigned for thousands of machines together
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Reliability WorkflowRoutes findings into maintenance and engineering teams
-
12 capabilities compared.12 differentiating rows are shown first.
Use cases
Who they're built for
Senseye
- Scale predictive across thousandsRun automated models on huge fleets of machines
- Prioritize reliability engineer focusUse attention scores to direct expert time better
- Forecast time to failurePlan repairs around predicted remaining useful life
Augury
- Protect critical rotating equipmentCatch motor pump and fan issues weeks early
- Reduce unplanned production downtimeUse diagnostics to schedule fixes during planned windows
- Support reliability without analystsGet diagnoses without hiring full-time vibration specialists
The trade-offs
Pros & cons of each tool
Trade-offs
Senseye
Pros
- Designed for very large fleet predictive programs
- Reuses historian and sensor data sources
- Backed by Siemens for long term support
Cons
- Best value at fleet rather than small scale
- Needs solid historian and asset metadata quality
- Roadmap now closely tied to Siemens portfolio
Trade-offs
Augury
Pros
- Trusted diagnostics on critical rotating equipment
- Bundled experts review machine health alerts
- Fast time to value on pilot lines
Cons
- Coverage focused on rotating equipment classes
- Subscription cost adds up across many machines
- Less self-service customization than analytics platforms
Final verdict
Best fit depends on your workflow
Current catalog data shows meaningful overlap between Senseye and Augury. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.
Differentiators available
Senseye has 4 visible decision signals and Augury has 4.
Score signal
Augury has the higher SoftFinders Score in the current catalog data.
Choose Senseye if…
- Scale predictive across thousands
- Prioritize reliability engineer focus
- Forecast time to failure
- Attention Index
Choose Augury if…
- Protect critical rotating equipment
- Reduce unplanned production downtime
- Support reliability without analysts
- Vibration Sensors
TRY THEM YOURSELF
See which one fits your workflow
Both tools have their strengths, the best way to decide is to spend a few minutes inside each.