How much does predictive maintenance cost?
Predictive maintenance cost has three parts: monitoring hardware (for sensor-based approaches, priced per asset), software or analytics (often per-asset or per-site subscription), and the people-time to act on findings. Analytics on existing data scales cheaper than sensors on every machine.
The three cost components
Rather than a single price, predictive maintenance cost is best understood as three parts:
- Monitoring hardware — sensors and gateways, for sensor-based approaches. This is a per-asset cost, so it scales with how many machines you instrument.
- Software / analytics — the platform that diagnoses and forecasts, usually a subscription priced per asset or per site.
- People-time — the effort to review findings and turn them into work orders. Often underestimated, and essential to getting value.
Exact figures vary widely by vendor, asset count and approach, so treat any single quoted price with caution and get options for your specific scope.
Sensor-based vs analytics-based cost
The biggest cost driver is the approach. Sensor-based platforms add hardware to each machine — fast to deploy and excellent on rotating equipment, but the per-machine cost grows as you scale. Analytics-based platforms model existing historian, SCADA and maintenance data, covering many assets without new sensors — cheaper to scale across a large estate, but dependent on the quality of existing data. Many plants use sensors on the critical few and analytics across the rest.
How to think about payback
The right comparison is not the sticker price but the payback: the cost of monitoring an asset versus the cost of its failures. On a critical asset whose unplanned failure stops a line or causes secondary damage, even a modest monitoring spend pays back quickly. On a cheap, non-critical asset it never will. Start with the assets where the failure cost is highest, prove the payback on a pilot, then scale.
Frequently asked questions
How much does predictive maintenance cost?
It has three parts: monitoring hardware (per asset, for sensor-based approaches), software or analytics (often a per-asset or per-site subscription), and the people-time to act on findings. Costs vary widely by vendor, asset count and approach, so get options scoped to your specific assets rather than relying on a single figure.
Is sensor-based or analytics-based predictive maintenance cheaper?
Sensor-based costs per machine, so it scales up in cost as you instrument more assets; analytics-based models existing data and scales more cheaply across many assets but depends on data quality. Many plants put sensors on the critical few and use analytics across the wider estate.
How do I justify the cost of predictive maintenance?
Compare the cost of monitoring an asset against the cost of its failures. On critical assets where an unplanned failure stops production or causes secondary damage, monitoring pays back quickly; on cheap non-critical assets it does not. Prove the payback on a pilot before scaling.
Related guides
Is predictive maintenance worth it?
Predictive maintenance is worth it where failures are expensive, frequent and detectable — typically critical rotating equipment. It pays back through avoided downtime, less secondary damage and less wasted preventive work. On cheap, non-critical assets it is not worth the effort.
How to choose predictive maintenance software
Choose predictive maintenance software by starting from your critical assets and data, not the feature list: match the approach (sensor vs analytics) to those assets, check it integrates with your CMMS, insist on a clear pilot with a measurable target, and weigh total cost against failure cost.
Predictive maintenance: a practical guide
What predictive maintenance is, how it differs from preventive maintenance, which techniques fit which assets, and how to start without boiling the ocean.
Software that helps
Fiix (Rockwell Automation)
Cloud CMMS with an AI assistant, now part of Rockwell.
Limble CMMS
Easy-to-adopt CMMS focused on fast technician uptake.
Augury
Machine health monitoring for rotating equipment using vibration and AI.