TrendMiner Reviews, Pricing, Features & Alternatives in 2026
TrendMiner is a self-service industrial analytics software providing predictive maintenance and process optimization tools for engineers to analyze, monitor, and predict manufacturing performance using time-series data.
TrendMiner gives you the power to analyze, monitor, and predict industrial process performance without needing a data science degree. You can plug it directly into your existing data historians to uncover patterns, troubleshoot quality issues, and identify the root causes of production deviations in minutes rather than days.
By using a high-performance search engine and pattern recognition, you can compare current sensor data against historical 'golden runs' to ensure your operations stay on track. It is designed specifically for process engineers in the chemical, oil and gas, and pharmaceutical industries who need to turn massive amounts of time-series data into actionable insights to improve overall equipment effectiveness.
Screenshots & Interface
Key Features
Stop digging through spreadsheets and start discovering insights. TrendMiner provides you with intuitive, search-based tools that make complex industrial data easy to navigate and act upon in real-time.
TrendHub Analysis
Search through years of historical process data instantly to find specific events or patterns using a Google-like search interface.
ContextHub Documentation
Add context to your sensor data by capturing digital logs and shift notes to explain why process changes occurred.
DashHub Monitoring
Create personalized operational dashboards to monitor your most critical process parameters and key performance indicators in one view.
Predictive Alerts
Set up automated monitors that notify you the moment your process deviates from established optimal operating windows.
Pattern Recognition
Identify recurring process behaviors automatically to predict future outcomes and prevent potential equipment failures before they happen.
Cross-Asset Comparison
Compare the performance of similar assets across different production lines or global sites to standardize your best practices.
Integrations
OSIsoft PI
AspenTech IP21
Honeywell PHD
Amazon Redshift
Microsoft Azure Data Lake
SAP
Siemens SIMATIC
Yokogawa Exaquantum
GE Digital APM
Software AG Cumulocity
Pricing Plans
TrendMiner uses a custom pricing model tailored to your specific industrial setup and data volume. While they don't publish a standard price list, you can request a personalized demo to see how the platform fits your environment. You'll typically work with their sales team to define a package based on the number of users and connected data sources.
Pros & Cons
Based on feedback from process engineers and operations managers in heavy industry, here is what you can expect when using the platform:
Pros
Fast search capabilities across massive historical datasets
Empowers engineers to perform analytics without data scientists
Seamless integration with major industrial data historians
Intuitive interface mimics familiar web search experiences
Cons
Requires high-quality historical data for accurate predictions
Initial configuration of data connectors takes technical effort
Pricing is not transparent for smaller manufacturing sites
Who Should Use TrendMiner?
Perfect for process engineers and operations managers in asset-intensive industries like chemicals and energy who need to optimize production and reduce downtime.
Best for Company Sizes
mid-market
enterprise
Popular Industries
chemicals
oil-and-gas
pharmaceuticals
Our Verdict
TrendMiner is a top-tier choice if you are looking to democratize data analytics within your engineering team. It removes the bottleneck of waiting for data scientists by giving your subject matter experts the tools to find their own answers directly from process data.
While the lack of public pricing and the need for robust data historians might be hurdles for smaller shops, the ROI for large-scale plants is significant. Highly recommended if you manage complex manufacturing processes and need to move from reactive troubleshooting to proactive, data-driven optimization.
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