Sundr is a repair intelligence platform that analyzes hundreds of thousands of real repair outcomes to tell you what lasts, what breaks, and when fixing it is smarter than replacing it. Every recommendation is the result of a weighted scoring model that evaluates five factors using real-world data.
Sundr's Repair Breakpoint model evaluates five factors: cost, remaining lifespan, environmental impact, parts availability, and repair success rate. Using data from 305,000+ community repair records, iFixit teardowns, and the French Indice de Réparabilité to determine whether repairing a device is economically rational.
The commonly cited 50% rule says if repair costs more than half the replacement price, you should replace. It's a useful starting point, but it only looks at cost. Sundr evaluates five weighted factors to determine your Repair Breakpoint , the actual point where repair stops making economic sense.
Each factor produces a score from −1 (strong repair signal) to +1 (strong replace signal). These scores are multiplied by their weights and combined into a single weighted average. A negative final score means we recommend repair; positive means replace. The further the score is from zero, the more confident the recommendation.
Uses the 50% rule as a baseline: if the repair cost exceeds 50% of the replacement cost, the cost signal favours replacement. This is one input to the broader Repair Breakpoint.
Compares the device's current age against its expected total lifespan. Newer devices are worth repairing; devices near end-of-life favour replacement.
Support lifecycle: When security or software update end dates are known, the effective remaining lifespan is capped by those dates. A device without security updates has reduced practical value regardless of physical condition.
Repair almost always has a lower carbon footprint than manufacturing a new device. This factor quantifies the COâ‚‚ difference.
Energy efficiency: For appliances, we account for energy savings from newer, more efficient models. If a new appliance saves significant energy over its lifetime, that COâ‚‚ reduction offsets some of the manufacturing emissions.
A device can't be repaired if parts don't exist. This factor checks whether replacement parts are accessible and from which sources.
The score is scaled by a confidence multiplier reflecting how certain we are about parts availability for your specific device.
Historical data on how often this type of repair succeeds, drawn from community repair records.
Rates are shown for both DIY and professional repair paths. When data confidence is low, the score impact is reduced.
You can adjust how much each factor matters by choosing a preset that matches your priorities. Each preset changes the weight distribution:
| Preset | Cost | Lifespan | Environ. | Parts | Success |
|---|---|---|---|---|---|
| Balanced | 40% | 20% | 15% | 15% | 10% |
| Budget-first | 45% | 15% | 5% | 23% | 13% |
| Eco-friendly | 15% | 15% | 30% | 25% | 15% |
| Longevity | 20% | 35% | 10% | 20% | 15% |
Every recommendation includes a confidence score (0-100%) that reflects both data quality and decision clarity. Higher confidence means we have better data and the decision is more clear-cut.
Our recommendations draw from multiple independent data sources to provide the most accurate picture possible.
Over 305,000 community repair records from openrepair.org. ORA data provides repair success rates by problem type, common failure patterns, and barriers to repair across device categories.
Professional teardown-based repairability scores covering 350+ devices on a 1-10 scale. Scores evaluate fastener types, adhesive use, modularity, and component accessibility. We also use iFixit data to estimate parts availability and DIY difficulty.
Government-mandated repairability scores from France's Indice de Réparabilité program. Manufacturers selling in France must publish repairability scores (0-10) with sub-scores for documentation, disassembly ease, parts availability, parts pricing, and category-specific criteria. Our dataset includes 2,109 products from 33 manufacturers, covering laptops, dishwashers, vacuums, washing machines, and smartphones. This is verified, government-regulated data, not estimates. Note: not all manufacturers participate, so coverage varies. Apple and Samsung are not currently in the dataset. Source: data.gouv.fr.
When device-specific data is unavailable, we fall back to category-level defaults for typical repair costs, expected lifespans, and environmental figures. These defaults are maintained for electronics, appliances, and kitchen device categories.
We strive for accuracy, but all estimates come with limitations. Here's what you should know:
Repair cost estimates use an additive model: parts cost (from iFixit listings and Keepa pricing data, with standard shop markup) plus a labour tier based on repair complexity (calibrated against real independent repair shop rates). Community-reported costs from Sundr users and device-specific data supplement the model. All prices are stored in USD, with CAD conversion at display time.
Every result page shows a data confidence indicator so you know how much data backs your recommendation:
Sundr is built and maintained by Keith, a software engineer. The scoring model, data analysis, and editorial calls are all mine. I use AI to help draft some of the written content, but everything gets reviewed against the actual data before it goes live. If a number shows up on this site, it traces back to a real source.
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